voxel_gan_mk3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, rdMolTransforms\n",
    "import os\n",
    "import fnmatch\n",
    "import numpy as np\n",
    "import deepchem as dc\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.mixture import GMM\n",
    "import random\n",
    "from tflearn.activations import leaky_relu\n",
    "from deepchem.utils.rdkit_util import get_xyz_from_mol\n",
    "from deepchem.feat.rdkit_grid_featurizer import convert_atom_to_voxel, compute_centroid, rotate_molecules\n",
    "np.set_printoptions(precision=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from deepchem.feat.graph_features import ConvMolFeaturizer\n",
    "from deepchem.feat.adjacency_fingerprints import AdjacencyFingerprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def featurize_mols(mol_files, max_n_atoms):\n",
    "    featurizer = AdjacencyFingerprint(max_n_atoms=max_n_atoms)\n",
    "    features = []\n",
    "    for idx, mol_file in enumerate(mol_files):\n",
    "        if idx % 32 == 0:\n",
    "            print(idx)\n",
    "        try:\n",
    "            bond_dist_mat = np.zeros((max_n_atoms, max_n_atoms))\n",
    "            angle_mat = np.zeros((max_n_atoms, max_n_atoms, max_n_atoms))\n",
    "            angle_tuples = np.zeros((max_n_atoms, max_n_atoms, max_n_atoms)).astype(np.uint8)\n",
    "            \n",
    "            if \".pdb\" in mol_file:\n",
    "                mol = Chem.MolFromPDBFile(mol_file)\n",
    "            else:\n",
    "                mol = Chem.MolFromMol2File(mol_file)\n",
    "            mol_xyz = get_xyz_from_mol(mol)\n",
    "            centroid = compute_centroid(mol_xyz)\n",
    "            mol_xyz -= centroid\n",
    "            temp = np.zeros((max_n_atoms, 3))\n",
    "            temp[:mol_xyz.shape[0]] = mol_xyz\n",
    "            mol_xyz = temp\n",
    "            \n",
    "            #mol_xyz = rotate_molecules([mol_xyz])[0]\n",
    "\n",
    "            c = mol.GetConformer(0)\n",
    "            \n",
    "            n_bonds = 0\n",
    "            n_angles = 0\n",
    "            for bond in mol.GetBonds():\n",
    "                n_bonds += 1\n",
    "                atom_i, atom_j = (bond.GetBeginAtom().GetIdx(), bond.GetEndAtom().GetIdx())\n",
    "                bond_dist_mat[atom_i, atom_j] = np.power(rdMolTransforms.GetBondLength(c, atom_i, atom_j),2)\n",
    "                bond_dist_mat[atom_j, atom_i] = np.power(rdMolTransforms.GetBondLength(c, atom_i, atom_j),2)\n",
    "                \n",
    "                for sub_bond in bond.GetBeginAtom().GetBonds():\n",
    "                    atom_k = sub_bond.GetEndAtom().GetIdx()\n",
    "                    if atom_k == atom_j or atom_k==atom_i: continue\n",
    "                    angle = np.cos(rdMolTransforms.GetAngleRad(c, atom_j, atom_i, atom_k))\n",
    "                    angle_mat[atom_i, atom_j, atom_k] = angle\n",
    "                    angle_mat[atom_i, atom_k, atom_j] = angle\n",
    "                    \n",
    "                    angle_tuples[atom_i, atom_j, atom_k] = 1\n",
    "                    angle_tuples[atom_i, atom_k, atom_j] = 1\n",
    "                    n_angles += 2\n",
    "                    \n",
    "                for sub_bond in bond.GetEndAtom().GetBonds():\n",
    "                    atom_k = sub_bond.GetEndAtom().GetIdx()\n",
    "                    if atom_k == atom_i or atom_k==atom_j: continue\n",
    "                    angle = np.cos(rdMolTransforms.GetAngleRad(c, atom_i, atom_j, atom_k))\n",
    "                    angle_mat[atom_j, atom_i, atom_k] = angle\n",
    "                    angle_mat[atom_j, atom_k, atom_i] = angle\n",
    "\n",
    "                    angle_tuples[atom_j, atom_i, atom_k] = 1\n",
    "                    angle_tuples[atom_j, atom_k, atom_i] = 1\n",
    "                    n_angles += 2\n",
    "                    \n",
    "            if mol is None:\n",
    "                features.append(None)\n",
    "                continue\n",
    "                \n",
    "            graph_feat = featurizer.featurize([mol])[0]\n",
    "            \n",
    "            features.append((mol_file, bond_dist_mat, mol_xyz, angle_mat, angle_tuples, n_bonds, n_angles, graph_feat))\n",
    "        except:\n",
    "            features.append(None)\n",
    "    return(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "max_n_atoms = 12\n",
    "S = 64\n",
    "feature_file = \"./voxel_features_pdbbind.pkl\"\n",
    "if not os.path.exists(feature_file):\n",
    "#if 1== 1:\n",
    "    pdbbind_dir = \"/home/evan/Documents/deep_docking/datasets/v2015/\"\n",
    "    def find_files(directory, pattern):\n",
    "        for root, dirs, files in os.walk(directory):\n",
    "            for basename in files:\n",
    "                if fnmatch.fnmatch(basename, pattern):\n",
    "                    filename = os.path.join(root, basename)\n",
    "                    yield filename\n",
    "    ligand_files = []\n",
    "    for f in find_files(pdbbind_dir, \"*ligand.mol2\"):\n",
    "        ligand_files += [f]\n",
    "    ligand_files = ligand_files[:] + [\"/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\"]*S  \n",
    "    features = featurize_mols(ligand_files, max_n_atoms)\n",
    "    with open(feature_file, \"wb\") as f:\n",
    "        pickle.dump(features, f, protocol=2)\n",
    "else:\n",
    "    with open(feature_file, \"rb\") as f:\n",
    "        features = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = [f for f in features if f is not None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "994"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/evan/Documents/deep_docking/alanine_dipeptide.pdb'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[-1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0][1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "tf.reset_default_graph()\n",
    "alpha = 0.01\n",
    "n_layers = 1\n",
    "\n",
    "S = 32\n",
    "\n",
    "B = max_n_atoms\n",
    "p = 75\n",
    "\n",
    "z_dim = 8\n",
    "\n",
    "L_list = [p, 16, 64, 128, 256]\n",
    "\n",
    "dihed_per_bond = 1\n",
    "valence = 4\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.device('/gpu:0'):\n",
    "    x = tf.placeholder(tf.float32, [S, B, L_list[0]], name=\"atom_features\")\n",
    "    keep_prob = tf.placeholder(tf.float32, name=\"keep_prob\")\n",
    "    \n",
    "    adj_matrix = tf.placeholder(tf.float32, shape=[S, B, B], name=\"adj_matrix\")\n",
    "    \n",
    "    #dist_matrix2d = tf.placeholder(tf.float32, shape=[S, B, B], name=\"dist_matrix2d\")\n",
    "    #bond_dist_mat = tf.placeholder(tf.float32, shape=[S, B, B], name=\"bond_dist_mat\")\n",
    "    \n",
    "    #angle_tensor = tf.placeholder(tf.float32, shape=[S, B, B, B], name=\"angle_tensor\")\n",
    "    angle_tuples = tf.placeholder(tf.float32, shape=[S, B, B, B], name=\"angle_tuples\")\n",
    "    n_bonds = tf.placeholder(tf.float32, shape=[S,1], name=\"nbonds\")\n",
    "    n_angles = tf.placeholder(tf.float32, shape=[S,1], name=\"nangles\")\n",
    "    \n",
    "    \n",
    "    mol_xyz = tf.placeholder(tf.float32, shape=[S, B, 3], name=\"molxyz\")\n",
    "    \n",
    "    z = tf.random_normal([S, z_dim], mean=0, stddev=1)\n",
    "    \n",
    "    mol_noise = tf.random_normal([S,B,3], mean=0,stddev=0.0001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#adapted from: http://stackoverflow.com/questions/37009647/compute-pairwise-distance-in-a-batch-without-replicating-tensor-in-tensorflow\n",
    "def compute_distance_matrix(A):\n",
    "    r = tf.reduce_sum(A*A, 1) # turn r into column vector \n",
    "    r = tf.reshape(r, [-1, 1]) \n",
    "    D = r - 2*tf.matmul(A, tf.transpose(A)) + tf.transpose(r)\n",
    "    return(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building clades and adj\n",
      "0\n",
      "[12, 12]\n",
      "Building hidden layers\n"
     ]
    }
   ],
   "source": [
    "GW_list = [None for i in range(n_layers)]\n",
    "Gb_list = [None for i in range(n_layers)]\n",
    "\n",
    "DW_list = [None for i in range(n_layers)]\n",
    "Db_list = [None for i in range(n_layers)]\n",
    "\n",
    "\n",
    "Gh_list = [x]\n",
    "Dh_list = [x]\n",
    "\n",
    "adj_list = [adj_matrix]\n",
    "B_list = [B]\n",
    "clades_list = []\n",
    "graph_stride = 1.\n",
    "\n",
    "print(\"building clades and adj\")\n",
    "for i in range(n_layers):\n",
    "    print(i)\n",
    "    B_list.append(int(np.ceil(B_list[i]/graph_stride)))\n",
    "    print(B_list)\n",
    "    clades_list.append(tf.stack([tf.one_hot(range(0,B_list[i],int(graph_stride)), depth=B_list[i])]*S, axis=0)) \n",
    "\n",
    "\n",
    "    adj_temp = tf.matmul(clades_list[i], adj_list[i])\n",
    "    sub_adj = tf.matmul(adj_temp, tf.transpose(adj_temp, perm=[0, 2, 1]))\n",
    "    sub_adj = tf.minimum(sub_adj, 1.)\n",
    "    adj_list.append(sub_adj)\n",
    "\n",
    "print(\"Building hidden layers\")\n",
    "for layer_idx in range(n_layers):\n",
    "    GW_list[layer_idx] = tf.Variable(tf.truncated_normal([L_list[layer_idx], L_list[layer_idx+1]], seed=2017), name=\"GW_list%d\" %layer_idx)\n",
    "    Gb_list[layer_idx] = tf.Variable(tf.ones([1, L_list[layer_idx+1]]))\n",
    "    \n",
    "    DW_list[layer_idx] = tf.Variable(tf.truncated_normal([L_list[layer_idx], L_list[layer_idx+1]], seed=2017), name=\"DW_list%d\" %layer_idx)\n",
    "    Db_list[layer_idx] = tf.Variable(tf.ones([1, L_list[layer_idx+1]]))\n",
    "    \n",
    "g_w1 = tf.Variable(tf.truncated_normal([L_list[n_layers]+z_dim, 16*5]))\n",
    "g_b1 = tf.Variable(tf.ones([1, 16*5]))\n",
    "\n",
    "g_w2 = tf.Variable(tf.truncated_normal([16*5, B*3]))\n",
    "g_b2 = tf.Variable(tf.ones([1, B*3]))\n",
    "\n",
    "d_w1_ini = tf.Variable(tf.truncated_normal([1, B*(L_list[n_layers]*2+1), 32]))\n",
    "d_w1 = tf.tile(d_w1_ini, [S, 1, 1])\n",
    "d_b1 = tf.Variable(tf.ones([1, 1, 32]))\n",
    "\n",
    "d_w2_ini = tf.Variable(tf.truncated_normal([1, 32, 1]))\n",
    "d_w2 = tf.tile(d_w2_ini, [S, 1, 1])\n",
    "d_b2 = tf.Variable(tf.ones([1, 1, 1]))\n",
    "\n",
    "lam1 = tf.Variable(tf.constant(0.01))\n",
    "lam2 = tf.Variable(tf.constant(0.01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def adjacency_conv_layer(x, W, b, adj, clades, L_in, L_out, layer_idx, S, B):\n",
    "    with tf.device('/gpu:0'):\n",
    "        print(\"layer_idx: %d\" %(layer_idx))\n",
    "        h = tf.matmul(adj, x, name=\"adj_mult_%d\" %layer_idx)\n",
    "        h = tf.reshape(h, shape=(S*B, L_in), name=\"adj_reshape_1_%d\" %layer_idx)\n",
    "\n",
    "        h = tf.matmul(h, W, name=\"adjconv_%d\" %layer_idx) + b\n",
    "    \n",
    "        h = tf.nn.tanh(h)\n",
    "        h = tf.reshape(h, (S, B, L_out), name=\"adj_reshape_2_%d\" %layer_idx)  \n",
    "\n",
    "        h = tf.matmul(clades, h)\n",
    "\n",
    "        print(\"within func h:\")\n",
    "        print(h)                                                                                                                              \n",
    "\n",
    "    return(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#t = sess.run(tf.matmul(dihed_indices[:,:,:,0], x))\n",
    "#t = np.reshape(t, [t.shape[0]*t.shape[1],t.shape[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator(W_list, b_list, h_list, L_list, n_layers):\n",
    "    for layer_idx in range(n_layers):\n",
    "        h_list.append(tf.nn.dropout(adjacency_conv_layer(h_list[layer_idx], W_list[layer_idx],\n",
    "                                                     b_list[layer_idx], adj_list[layer_idx], \n",
    "                                                     clades_list[layer_idx],\n",
    "                                                     L_list[layer_idx], L_list[layer_idx+1],\n",
    "                                                     layer_idx, S, B_list[layer_idx]), keep_prob))\n",
    "    L_final = L_list[n_layers]  \n",
    "    \n",
    "    print(\"h_list[-1]\")\n",
    "    print(h_list[-1])\n",
    "    \n",
    "    print(\"B_list\")\n",
    "    print(B_list)\n",
    "    \n",
    "    h_final = tf.reduce_sum(h_list[-1], axis=1, keep_dims=False)\n",
    "    h_final = tf.concat([h_final, z], axis=1)\n",
    "\n",
    "    \n",
    "    print(\"h afterz\")\n",
    "    print(h_final)\n",
    "    \n",
    "    g_h1 = tf.nn.dropout(tf.nn.tanh(tf.matmul(h_final, g_w1) + g_b1), keep_prob)\n",
    "    #g_h1 = tf.reshape(g_h1, [S, 5, 16])\n",
    "    print(\"g_h1\")\n",
    "    print(g_h1)\n",
    "    g_h2 = tf.reshape(tf.matmul(g_h1, g_w2) + g_b2, [S, B, 3])\n",
    "    #g_h2 = tf.clip_by_value(g_h2, -10., 10.)\n",
    "    return(g_h2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def compute_angle_tensor(mols_batch, angle_tuples_batch):\n",
    "    i_vec = tf.reshape(mols_batch, [S, B, 1, 3])\n",
    "    i_vec = tf.tile(i_vec, [1, 1, B, 1])\n",
    "\n",
    "    j_vec = tf.reshape(mols_batch, [S, 1, B, 3])\n",
    "    j_vec = tf.tile(j_vec, [1, B, 1, 1])\n",
    "\n",
    "    diff = tf.subtract(i_vec, j_vec)\n",
    "    temp = tf.eye(B, batch_shape=[S])\n",
    "    temp = tf.reshape(temp, [S, B, B, 1])\n",
    "    temp = tf.tile(temp, [1, 1, 1, 3])\n",
    "    diff = diff + temp\n",
    "    diff = diff / tf.sqrt(tf.reduce_sum(tf.square(diff), axis=3, keep_dims=True))\n",
    "    temp = tf.sqrt(tf.reduce_sum(tf.square(temp), axis=3, keep_dims=True))\n",
    "    diff = tf.subtract(diff, temp)\n",
    "\n",
    "    print(\"diff should be [S, B, B, 3]\")\n",
    "    print(diff)\n",
    "\n",
    "    ij = tf.tile(tf.reshape(diff, [S, B, 1, B, 3]), [1, 1, B, 1, 1])\n",
    "    ik = tf.tile(tf.reshape(diff, [S, B, B, 1, 3]), [1, 1, 1, B, 1])\n",
    "    dps = tf.reduce_sum(tf.multiply(ij ,ik), axis=4)\n",
    "    #dps = tf.where(tf.is_nan(dps), tf.ones_like(dps) * 0., dps)\n",
    "    print(\"dps should be [S, B, B, B]\")\n",
    "    print(dps)\n",
    "\n",
    "    #angles_gen = tf.acos(tf.clip_by_value(dps, -1.0, 1.0))\n",
    "    angles_computed = tf.where(tf.is_nan(dps), tf.zeros_like(dps), dps)\n",
    "    angles_computed = tf.multiply(angles_computed, angle_tuples_batch)\n",
    "    return(angles_computed)\n",
    "\n",
    "def compute_dist_tensor(mols_batch):\n",
    "    dist_matrix = tf.concat([tf.reshape(compute_distance_matrix(tf.reshape(mol, [B, 3])), (1, B, B)) for mol in tf.split(mols_batch, S, axis=0)], axis=0)\n",
    "    #dist_matrix = tf.where(tf.is_nan(dist_matrix), tf.ones_like(dist_matrix) * 0., dist_matrix)\n",
    "    print(\"dist_matrix\")\n",
    "    print(dist_matrix)\n",
    "    #dist_matrix = compute_distance_matrix(mol)\n",
    "    return(dist_matrix)\n",
    "\n",
    "def compute_dihedral_tensor(mols_batch, angle_tuples_batch):\n",
    "    i_vec = tf.reshape(mols_batch, [S, B, 1, 3])\n",
    "    i_vec = tf.tile(i_vec, [1, 1, B, 1])\n",
    "\n",
    "    j_vec = tf.reshape(mols_batch, [S, 1, B, 3])\n",
    "    j_vec = tf.tile(j_vec, [1, B, 1, 1])\n",
    "\n",
    "    diff = tf.subtract(i_vec, j_vec)\n",
    "    temp = tf.eye(B, batch_shape=[S])\n",
    "    temp = tf.reshape(temp, [S, B, B, 1])\n",
    "    temp = tf.tile(temp, [1, 1, 1, 3])\n",
    "    diff = diff + temp\n",
    "    diff = diff / tf.sqrt(tf.reduce_sum(tf.square(diff), axis=3, keep_dims=True))\n",
    "    temp = tf.sqrt(tf.reduce_sum(tf.square(temp), axis=3, keep_dims=True))\n",
    "    diff = tf.subtract(diff, temp)\n",
    "\n",
    "    print(\"diff should be [S, B, B, 3]\")\n",
    "    print(diff)\n",
    "\n",
    "    ij = tf.tile(tf.reshape(diff, [S, B, 1, B, 3]), [1, 1, B, 1, 1])\n",
    "    ik = tf.tile(tf.reshape(diff, [S, B, B, 1, 3]), [1, 1, 1, B, 1])\n",
    "    cross = tf.cross(ij, ik)\n",
    "    cross = cross / tf.sqrt(tf.reduce_sum(tf.square(cross), axis=4, keep_dims=True))\n",
    "    \n",
    "    ijk = tf.tile(tf.reshape(cross, [S, B, B, 1, B, 3]), [1m 1])\n",
    "    \n",
    "    dps = tf.reduce_sum(tf.multiply(ij ,ik), axis=4)\n",
    "    #dps = tf.where(tf.is_nan(dps), tf.ones_like(dps) * 0., dps)\n",
    "    print(\"dps should be [S, B, B, B]\")\n",
    "    print(dps)\n",
    "\n",
    "    #angles_gen = tf.acos(tf.clip_by_value(dps, -1.0, 1.0))\n",
    "    angles_computed = tf.where(tf.is_nan(dps), tf.zeros_like(dps), dps)\n",
    "    angles_computed = tf.multiply(angles_computed, angle_tuples_batch)\n",
    "    return(angles_computed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator(W_list, b_list, h_list, L_list, n_layers, mols, mol_xyz):\n",
    "    mols = tf.add(mols, mol_noise)\n",
    "    print(\"mols\")\n",
    "    print(mols)\n",
    "    with tf.device('/gpu:0'):\n",
    "        for layer_idx in range(n_layers):\n",
    "            h_list.append(tf.nn.dropout(adjacency_conv_layer(h_list[layer_idx], W_list[layer_idx],\n",
    "                                             b_list[layer_idx], adj_list[layer_idx], \n",
    "                                             clades_list[layer_idx],\n",
    "                                             L_list[layer_idx], L_list[layer_idx+1],\n",
    "                                             layer_idx, S, B_list[layer_idx]), keep_prob))\n",
    "        h_final = h_list[-1]\n",
    "\n",
    "        #COMPUTE ANGLE LOSS:\n",
    "        angle_tensor = compute_angle_tensor(mol_xyz, angle_tuples)\n",
    "        angles_gen = compute_angle_tensor(mols, angle_tuples)\n",
    "\n",
    "        \n",
    "        wrong_angle_loss = tf.divide(tf.reshape(tf.multiply(tf.reduce_sum(tf.squared_difference(angles_gen, angle_tensor), axis=[1,2,3]), lam2), (S,1)), n_angles)\n",
    "        #wrong_angle_loss = tf.where(tf.is_nan(wrong_angle_loss), tf.zeros_like(wrong_angle_loss), wrong_angle_loss)\n",
    "\n",
    "        #wrong_angle_loss = tf.minimum(wrong_angle_loss, 3.14**2)\n",
    "        print(\"wrong_angle_loss\")\n",
    "        print(wrong_angle_loss)\n",
    "        \n",
    "        #COMPUTE BOND LOSS:\n",
    "        dist_matrix_gen = compute_dist_tensor(mols)\n",
    "        bond_dist_gen = tf.multiply(dist_matrix_gen, adj_matrix)\n",
    "        \n",
    "        dist_matrix_real = compute_dist_tensor(mol_xyz)\n",
    "        bond_dist_real = tf.multiply(dist_matrix_real, adj_matrix)\n",
    "\n",
    " \n",
    "        wrong_bond_loss = tf.divide(tf.reshape(tf.multiply(tf.reduce_sum(tf.square(tf.subtract(bond_dist_real, bond_dist_gen)), axis=[1,2]), lam1), (S,1)), n_bonds)\n",
    "        print(\"wrong_bond_loss\")\n",
    "        print(wrong_bond_loss)\n",
    "        \n",
    "        #COMPUTE DIHEDRAL LOSS?\n",
    "        \n",
    "        \n",
    "        feat_i = tf.reshape(h_final, (S, B, 1, L_list[n_layers]))\n",
    "        feat_i = tf.tile(feat_i, [1, 1, B, 1])\n",
    "        \n",
    "        feat_j = tf.reshape(h_final, (S, B, 1, L_list[n_layers]))\n",
    "        feat_j = tf.transpose(feat_j, perm=[0, 2, 1, 3])\n",
    "        feat_j = tf.tile(feat_j, [1, B, 1, 1])\n",
    "        \n",
    "        d_h1 = tf.concat([tf.reshape(dist_matrix_gen, [S, B, B, 1]), feat_i, feat_j], axis=3)\n",
    "        d_h1 = tf.reshape(d_h1, [S, B, B*(L_list[n_layers]*2+1)])\n",
    "        d_h2 = tf.nn.dropout(leaky_relu(tf.matmul(d_h1, d_w1) + d_b1), keep_prob)\n",
    "        d_h2 = tf.divide(tf.reduce_sum(tf.matmul(d_h2, d_w2) + d_b2, axis=1), n_bonds)#, axis=1\n",
    "        print(\"d_h2\")\n",
    "        print(d_h2)\n",
    "        \n",
    "    \n",
    "    return(tf.add(tf.add(wrong_bond_loss, tf.multiply(wrong_angle_loss, 10.)), d_h2), bond_dist_gen, angles_gen, wrong_bond_loss, wrong_angle_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "theta_D = DW_list[:n_layers] + Db_list[:n_layers] + [d_w1_ini, d_b1, d_w2_ini, d_b2] + [lam1, lam2]\n",
    "\n",
    "theta_G =  GW_list[:n_layers] + Gb_list[:n_layers] + [g_w1, g_b1, g_w2, g_b2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer_idx: 0\n",
      "within func h:\n",
      "Tensor(\"MatMul_2:0\", shape=(32, 12, 16), dtype=float32, device=/device:GPU:0)\n",
      "h_list[-1]\n",
      "Tensor(\"dropout/mul:0\", shape=(32, 12, 16), dtype=float32, device=/device:GPU:0)\n",
      "B_list\n",
      "[12, 12]\n",
      "h afterz\n",
      "Tensor(\"concat:0\", shape=(32, 24), dtype=float32, device=/device:GPU:0)\n",
      "g_h1\n",
      "Tensor(\"dropout_1/mul:0\", shape=(32, 80), dtype=float32, device=/device:GPU:0)\n",
      "mols\n",
      "Tensor(\"Add:0\", shape=(32, 12, 3), dtype=float32, device=/device:GPU:0)\n",
      "layer_idx: 0\n",
      "within func h:\n",
      "Tensor(\"MatMul_5:0\", shape=(32, 12, 16), dtype=float32, device=/device:GPU:0)\n",
      "diff should be [S, B, B, 3]\n",
      "Tensor(\"Sub_1:0\", shape=(32, 12, 12, 3), dtype=float32, device=/device:GPU:0)\n",
      "dps should be [S, B, B, B]\n",
      "Tensor(\"Sum_3:0\", shape=(32, 12, 12, 12), dtype=float32, device=/device:GPU:0)\n",
      "diff should be [S, B, B, 3]\n",
      "Tensor(\"Sub_3:0\", shape=(32, 12, 12, 3), dtype=float32, device=/device:GPU:0)\n",
      "dps should be [S, B, B, B]\n",
      "Tensor(\"Sum_6:0\", shape=(32, 12, 12, 12), dtype=float32, device=/device:GPU:0)\n",
      "wrong_angle_loss\n",
      "Tensor(\"truediv:0\", shape=(32, 1), dtype=float32, device=/device:GPU:0)\n",
      "dist_matrix\n",
      "Tensor(\"concat_1:0\", shape=(32, 12, 12), dtype=float32, device=/device:GPU:0)\n",
      "dist_matrix\n",
      "Tensor(\"concat_2:0\", shape=(32, 12, 12), dtype=float32, device=/device:GPU:0)\n",
      "wrong_bond_loss\n",
      "Tensor(\"truediv_1:0\", shape=(32, 1), dtype=float32, device=/device:GPU:0)\n",
      "d_h2\n",
      "Tensor(\"truediv_2:0\", shape=(32, 1), dtype=float32, device=/device:GPU:0)\n",
      "mols\n",
      "Tensor(\"Add_3:0\", shape=(32, 12, 3), dtype=float32, device=/device:GPU:0)\n",
      "layer_idx: 0\n",
      "within func h:\n",
      "Tensor(\"MatMul_72:0\", shape=(32, 12, 16), dtype=float32, device=/device:GPU:0)\n",
      "diff should be [S, B, B, 3]\n",
      "Tensor(\"Sub_6:0\", shape=(32, 12, 12, 3), dtype=float32, device=/device:GPU:0)\n",
      "dps should be [S, B, B, B]\n",
      "Tensor(\"Sum_76:0\", shape=(32, 12, 12, 12), dtype=float32, device=/device:GPU:0)\n",
      "diff should be [S, B, B, 3]\n",
      "Tensor(\"Sub_8:0\", shape=(32, 12, 12, 3), dtype=float32, device=/device:GPU:0)\n",
      "dps should be [S, B, B, B]\n",
      "Tensor(\"Sum_79:0\", shape=(32, 12, 12, 12), dtype=float32, device=/device:GPU:0)\n",
      "wrong_angle_loss\n",
      "Tensor(\"truediv_3:0\", shape=(32, 1), dtype=float32, device=/device:GPU:0)\n",
      "dist_matrix\n",
      "Tensor(\"concat_4:0\", shape=(32, 12, 12), dtype=float32, device=/device:GPU:0)\n",
      "dist_matrix\n",
      "Tensor(\"concat_5:0\", shape=(32, 12, 12), dtype=float32, device=/device:GPU:0)\n",
      "wrong_bond_loss\n",
      "Tensor(\"truediv_4:0\", shape=(32, 1), dtype=float32, device=/device:GPU:0)\n",
      "d_h2\n",
      "Tensor(\"truediv_5:0\", shape=(32, 1), dtype=float32, device=/device:GPU:0)\n"
     ]
    }
   ],
   "source": [
    "import time \n",
    "\n",
    "with tf.device('/gpu:0'):    \n",
    "\n",
    "    G_sample = generator(GW_list, Gb_list, Gh_list, L_list, n_layers)\n",
    "\n",
    "    D_real, bond_dist_real, angles_gen_real, wrong_bond_real, wrong_angle_real = discriminator(DW_list, Db_list, Dh_list, L_list, n_layers, mol_xyz, mol_xyz)\n",
    "    D_fake, bond_dist_fake, angles_gen_fake, wrong_bond_fake, wrong_angle_fake = discriminator(DW_list, Db_list, Dh_list, L_list, n_layers, G_sample, mol_xyz)\n",
    "\n",
    "\n",
    "    D_loss = tf.reduce_mean(D_real) - tf.reduce_mean(D_fake)\n",
    "    G_loss = -tf.reduce_mean(D_fake)\n",
    "    clip_D = []\n",
    "    for p in theta_D[:-2]:\n",
    "        #print(p)\n",
    "        clip_D.append(p.assign(tf.clip_by_value(p, -0.01, 0.01)))\n",
    "    clip_D.append(lam1.assign(tf.clip_by_value(lam1, 0.001, 0.002)))\n",
    "    clip_D.append(lam2.assign(tf.clip_by_value(lam2, 0.001, 0.002)))\n",
    "\n",
    "    D_solver = (tf.train.RMSPropOptimizer(learning_rate=5e-3)\n",
    "                .minimize(-D_loss, var_list=theta_D))\n",
    "    G_solver = (tf.train.RMSPropOptimizer(learning_rate=5e-3)\n",
    "                .minimize(G_loss, var_list=theta_G))\n",
    "\n",
    "mb_size = S\n",
    "\n",
    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "\n",
    "preds = []\n",
    "\n",
    "d_losses = []\n",
    "g_losses = []\n",
    "#print(sess.run(label_placeholder))\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def construct_feed_dict(X, start=None,\n",
    "                      stop=None, y=None,\n",
    "                      keep_prob_val=0.5, train=True,\n",
    "                       shuffle_inds=True):\n",
    "    a = time.time()\n",
    "    if start is None:\n",
    "      start = 0\n",
    "      stop = len(X)\n",
    "    \n",
    "    inds = range(start, stop)\n",
    "    if shuffle_inds:\n",
    "        random.shuffle(inds)\n",
    "\n",
    "    atom_adj_batch = [X[idx][7][0] for idx in inds]\n",
    "    A_batch = np.array([X[idx][7][1] for idx in inds])\n",
    "    mol_xyz_batch = [X[idx][2] for idx in inds]\n",
    "    mol_xyz_batch = rotate_molecules(mol_xyz_batch)\n",
    "\n",
    "    angle_tuples_batch = [X[idx][4] for idx in inds]\n",
    "    n_bonds_batch = np.reshape(np.array([X[idx][5] for idx in inds]), [S,1])\n",
    "    n_angles_batch = np.reshape(np.array([X[idx][6] for idx in inds]), [S,1])\n",
    "\n",
    "    feed_dict = {x: A_batch,\n",
    "                 adj_matrix: atom_adj_batch,\n",
    "                 mol_xyz: mol_xyz_batch,\n",
    "                 angle_tuples: angle_tuples_batch,\n",
    "                 n_bonds: n_bonds_batch,\n",
    "                 n_angles: n_angles_batch,\n",
    "                 keep_prob: keep_prob_val\n",
    "                }\n",
    "    t = time.time()-a\n",
    "    #print(\"Construct feed dict: %f\" %(t))\n",
    "    return(feed_dict)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_molecule(mol_file, new_coords, new_file):\n",
    "    print(mol_file)\n",
    "    print(new_coords[:4])\n",
    "    if \".pdb\" in mol_file:\n",
    "        mol = Chem.MolFromPDBFile(mol_file)\n",
    "    else:\n",
    "        mol = Chem.MolFromMol2File(mol_file)\n",
    "    c = mol.GetConformer(0)\n",
    "    for i in range(mol.GetNumAtoms()):\n",
    "        c.SetAtomPosition(i, new_coords[i].tolist())\n",
    "    Chem.MolToMolFile(mol, new_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/evan/Documents/deep_docking/datasets/v2015/3s77/3s77_ligand.mol2'"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[3][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.81729912758\n",
      "Training epoch 0\n",
      "Iter: 0\n",
      "D loss: -783.5\n",
      "G_loss: 3.913e+03\n",
      "real:\n",
      "[[-0.8001  1.5268  0.042 ]\n",
      " [ 0.5976  0.872   0.0027]\n",
      " [ 0.6371 -0.7867  0.2045]\n",
      " [-0.4346 -1.6121 -0.2492]]\n",
      "fake:\n",
      "[[  9.0962  12.8373   3.4669]\n",
      " [ -2.1124   0.8269  -4.057 ]\n",
      " [  0.3644 -10.022   -0.6078]\n",
      " [ -4.4219 -13.0377  -8.0855]]\n",
      "bond real:\n",
      "[[ 0.      2.3841  0.      0.    ]\n",
      " [ 2.3841  0.      2.794   0.    ]\n",
      " [ 0.      2.794   0.      2.0356]\n",
      " [ 0.      0.      2.0356  0.    ]]\n",
      "bond fake:\n",
      "[[   0.      322.4796    0.        0.    ]\n",
      " [ 322.4796    0.      140.534     0.    ]\n",
      " [   0.      140.534     0.       85.2395]\n",
      " [   0.        0.       85.2395    0.    ]]\n",
      "angles real computed\n",
      "[[ 0.     0.     0.    -0.   ]\n",
      " [ 0.     0.     0.    -0.518]\n",
      " [ 0.     0.     0.    -0.   ]\n",
      " [-0.    -0.518 -0.     0.   ]]\n",
      "angles fake:\n",
      "[[ 0.      0.      0.     -0.    ]\n",
      " [ 0.      0.      0.     -0.1945]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [-0.     -0.1945 -0.      0.    ]]\n",
      "bond loss real:\n",
      "[[ -3.9279e-09]\n",
      " [ -8.8528e-10]\n",
      " [ -4.4426e-10]\n",
      " [ -1.1000e-09]]\n",
      "bond loss fake:\n",
      "[[-398.3999]\n",
      " [ -74.9887]\n",
      " [ -95.8072]\n",
      " [ -53.7759]]\n",
      "angle loss real:\n",
      "[[ -7.5938e-11]\n",
      " [ -4.2852e-11]\n",
      " [ -5.1760e-11]\n",
      " [ -4.3934e-11]]\n",
      "angle loss fake:\n",
      "[[-0.0004]\n",
      " [-0.0028]\n",
      " [-0.0028]\n",
      " [-0.0014]]\n",
      "lam1\n",
      "-0.0040962\n",
      "lam2\n",
      "-0.00398503\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2\n",
      "[[  7.6791  10.3215   6.0672]\n",
      " [ -4.8659   1.1368  -1.7043]\n",
      " [  0.8888  -7.5579  -0.6682]\n",
      " [ -5.1033 -12.0083  -5.934 ]]\n",
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[ -0.8621   0.88     1.1891]\n",
      " [  9.494   -0.3926  11.6747]\n",
      " [  6.8663   7.7612   6.7436]\n",
      " [  6.0326   2.4689   4.43  ]]\n",
      "1.41234493256\n",
      "Training epoch 1000\n",
      "Iter: 1000\n",
      "D loss: -0.02026\n",
      "G_loss: 0.1777\n",
      "real:\n",
      "[[-0.1295 -0.7381 -1.5529]\n",
      " [ 0.3273 -1.0001 -0.1015]\n",
      " [ 0.3625  0.3312  0.9086]\n",
      " [-0.5603  1.407   0.7458]]\n",
      "fake:\n",
      "[[-3.7875  2.3197  3.6881]\n",
      " [-4.1427  3.4333  3.5466]\n",
      " [-4.8657  2.4164  4.0304]\n",
      " [-5.7642  2.7017  3.9133]]\n",
      "bond real:\n",
      "[[  0.0000e+00   2.3847e+00   0.0000e+00   0.0000e+00]\n",
      " [  2.3847e+00  -2.3842e-07   2.7927e+00   0.0000e+00]\n",
      " [  0.0000e+00   2.7927e+00   0.0000e+00   2.0356e+00]\n",
      " [  0.0000e+00   0.0000e+00   2.0356e+00   0.0000e+00]]\n",
      "bond fake:\n",
      "[[ 0.      1.4172  0.      0.    ]\n",
      " [ 1.4172  0.      1.7622  0.    ]\n",
      " [ 0.      1.7622  0.      0.8979]\n",
      " [ 0.      0.      0.8979  0.    ]]\n",
      "angles real computed\n",
      "[[ 0.      0.     -0.     -0.    ]\n",
      " [ 0.      0.     -0.     -0.5182]\n",
      " [-0.     -0.      0.     -0.    ]\n",
      " [-0.     -0.5182 -0.      0.    ]]\n",
      "angles fake:\n",
      "[[ 0.      0.      0.     -0.    ]\n",
      " [ 0.      0.      0.     -0.2145]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [-0.     -0.2145 -0.      0.    ]]\n",
      "bond loss real:\n",
      "[[ -9.4523e-10]\n",
      " [ -1.0942e-09]\n",
      " [ -1.1114e-09]\n",
      " [ -8.7521e-10]]\n",
      "bond loss fake:\n",
      "[[-0.0085]\n",
      " [-0.0111]\n",
      " [-0.0355]\n",
      " [-0.0293]]\n",
      "angle loss real:\n",
      "[[ -3.5294e-11]\n",
      " [ -1.5763e-11]\n",
      " [ -3.2704e-11]\n",
      " [ -7.6168e-11]]\n",
      "angle loss fake:\n",
      "[[-0.0021]\n",
      " [-0.0016]\n",
      " [-0.0018]\n",
      " [-0.0011]]\n",
      "lam1\n",
      "-0.00391116\n",
      "lam2\n",
      "-0.00367106\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2\n",
      "[[-3.9822  1.9512  3.4161]\n",
      " [-4.3086  3.1349  3.2789]\n",
      " [-4.8675  2.0721  3.8911]\n",
      " [-5.7191  2.3011  3.5588]]\n",
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[ 20.0402  -0.5759   5.8052]\n",
      " [ 18.655   -1.3948   5.3258]\n",
      " [ 18.1312  -2.7162   5.0588]\n",
      " [ 16.8154  -1.8826   5.51  ]]\n",
      "1.16518187523\n",
      "Training epoch 2000\n",
      "Iter: 2000\n",
      "D loss: 0.05751\n",
      "G_loss: 0.2628\n",
      "real:\n",
      "[[ -7.7994e-01   1.4779e+00  -4.2497e-01]\n",
      " [ -3.3893e-01   1.1243e-01  -9.9500e-01]\n",
      " [  6.2601e-01  -8.2142e-01   2.0170e-04]\n",
      " [  4.9286e-01  -7.6890e-01   1.4198e+00]]\n",
      "fake:\n",
      "[[-21.5828   0.7297  10.0264]\n",
      " [-22.2815   1.3943   9.6353]\n",
      " [-23.006    0.6866  10.3936]\n",
      " [-24.1257   1.6589  10.4507]]\n",
      "bond real:\n",
      "[[ 0.      2.3842  0.      0.    ]\n",
      " [ 2.3842  0.      2.7934  0.    ]\n",
      " [ 0.      2.7934  0.      2.0348]\n",
      " [ 0.      0.      2.0348  0.    ]]\n",
      "bond fake:\n",
      "[[ 0.      0.6779  0.      0.    ]\n",
      " [ 0.6779  0.      1.6052  0.    ]\n",
      " [ 0.      1.6052  0.      2.4287]\n",
      " [ 0.      0.      2.4287  0.    ]]\n",
      "angles real computed\n",
      "[[ 0.      0.      0.     -0.    ]\n",
      " [ 0.      0.     -0.     -0.5183]\n",
      " [ 0.     -0.      0.      0.    ]\n",
      " [-0.     -0.5183  0.      0.    ]]\n",
      "angles fake:\n",
      "[[ 0.      0.      0.     -0.    ]\n",
      " [ 0.      0.      0.     -0.0912]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [-0.     -0.0912 -0.      0.    ]]\n",
      "bond loss real:\n",
      "[[ -9.5090e-10]\n",
      " [ -9.8365e-10]\n",
      " [ -2.5504e-09]\n",
      " [ -1.2925e-09]]\n",
      "bond loss fake:\n",
      "[[-0.009 ]\n",
      " [-0.0198]\n",
      " [-0.0413]\n",
      " [-0.0133]]\n",
      "angle loss real:\n",
      "[[ -7.2109e-11]\n",
      " [ -6.6458e-11]\n",
      " [ -8.6662e-11]\n",
      " [ -3.2683e-11]]\n",
      "angle loss fake:\n",
      "[[-0.0019]\n",
      " [-0.0016]\n",
      " [-0.0018]\n",
      " [-0.0011]]\n",
      "lam1\n",
      "-0.00410421\n",
      "lam2\n",
      "-0.00427025\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2\n",
      "[[-23.3539   0.6111   9.4973]\n",
      " [-24.0364   1.318    9.2336]\n",
      " [-24.6869   0.5858   9.9898]\n",
      " [-25.7602   1.5329   9.9559]]\n",
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[ 1.7166 -4.1809  8.6347]\n",
      " [ 0.2863 -5.1991  7.7724]\n",
      " [-0.7714 -6.2164  7.3164]\n",
      " [-1.4962 -4.4748  7.6012]]\n",
      "1.1708419323\n",
      "Training epoch 3000\n",
      "Iter: 3000\n",
      "D loss: -0.004702\n",
      "G_loss: 0.3331\n",
      "real:\n",
      "[[ 0.5845  1.4    -0.8195]\n",
      " [ 0.8882  0.4481  0.3576]\n",
      " [-0.3071 -0.6577  0.7347]\n",
      " [-1.1656 -1.1903 -0.2728]]\n",
      "fake:\n",
      "[[-47.3058  -0.4053  13.2069]\n",
      " [-46.6813  -0.8762  13.4998]\n",
      " [-47.3297  -2.0262  14.0686]\n",
      " [-48.601   -1.5994  13.9341]]\n",
      "bond real:\n",
      "[[  0.0000e+00   2.3843e+00   0.0000e+00   0.0000e+00]\n",
      " [  2.3843e+00   0.0000e+00   2.7926e+00   0.0000e+00]\n",
      " [  0.0000e+00   2.7926e+00   0.0000e+00   2.0358e+00]\n",
      " [  0.0000e+00   0.0000e+00   2.0358e+00  -4.7684e-07]]\n",
      "bond fake:\n",
      "[[ 0.      0.698   0.      0.    ]\n",
      " [ 0.698   0.      2.0686  0.    ]\n",
      " [ 0.      2.0686  0.      1.8191]\n",
      " [ 0.      0.      1.8191  0.    ]]\n",
      "angles real computed\n",
      "[[ 0.     0.     0.    -0.   ]\n",
      " [ 0.     0.     0.    -0.518]\n",
      " [ 0.     0.     0.    -0.   ]\n",
      " [-0.    -0.518 -0.     0.   ]]\n",
      "angles fake:\n",
      "[[ 0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.     -0.1254]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [ 0.     -0.1254 -0.      0.    ]]\n",
      "bond loss real:\n",
      "[[ -1.7342e-10]\n",
      " [ -1.3634e-09]\n",
      " [ -2.5384e-09]\n",
      " [ -1.1459e-09]]\n",
      "bond loss fake:\n",
      "[[-0.0112]\n",
      " [-0.0059]\n",
      " [-0.0217]\n",
      " [-0.016 ]]\n",
      "angle loss real:\n",
      "[[ -5.2912e-12]\n",
      " [ -6.8091e-11]\n",
      " [ -5.3535e-11]\n",
      " [ -3.3056e-11]]\n",
      "angle loss fake:\n",
      "[[-0.0004]\n",
      " [-0.0015]\n",
      " [-0.0022]\n",
      " [-0.0012]]\n",
      "lam1\n",
      "-0.00441211\n",
      "lam2\n",
      "-0.00420445\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2\n",
      "[[-47.3468  -0.4043  13.1679]\n",
      " [-46.7201  -0.8777  13.4634]\n",
      " [-47.37    -2.0269  14.0344]\n",
      " [-48.6418  -1.599   13.8975]]\n",
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[ 23.0655   6.5336  22.6052]\n",
      " [ 22.4385   6.0718  21.1698]\n",
      " [ 22.046    4.1363  21.3193]\n",
      " [ 20.7176   5.1488  21.6748]]\n",
      "1.17878103256\n",
      "Training epoch 4000\n",
      "Iter: 4000\n",
      "D loss: 0.04446\n",
      "G_loss: 0.2114\n",
      "real:\n",
      "[[-1.5855 -0.527   0.4261]\n",
      " [-0.3415  0.2007  0.9801]\n",
      " [ 1.0179  0.1742  0.0081]\n",
      " [ 0.9091  0.152  -1.4143]]\n",
      "fake:\n",
      "[[-64.5392   2.183   19.8435]\n",
      " [-63.7693   1.696   20.1063]\n",
      " [-64.2172   0.3597  20.3914]\n",
      " [-65.6663   1.1961  20.3149]]\n",
      "bond real:\n",
      "[[ 0.      2.3832  0.      0.    ]\n",
      " [ 2.3832  0.      2.794   0.    ]\n",
      " [ 0.      2.794   0.      2.0356]\n",
      " [ 0.      0.      2.0356  0.    ]]\n",
      "bond fake:\n",
      "[[ 0.      1.0317  0.      0.    ]\n",
      " [ 1.0317  0.      1.9399  0.    ]\n",
      " [ 0.      1.9399  0.      2.8647]\n",
      " [ 0.      0.      2.8647  0.    ]]\n",
      "angles real computed\n",
      "[[ 0.      0.     -0.     -0.    ]\n",
      " [ 0.      0.     -0.     -0.5184]\n",
      " [-0.     -0.      0.     -0.    ]\n",
      " [-0.     -0.5184 -0.      0.    ]]\n",
      "angles fake:\n",
      "[[ 0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.      0.1135]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [ 0.      0.1135 -0.      0.    ]]\n",
      "bond loss real:\n",
      "[[ -6.6220e-09]\n",
      " [ -6.2852e-10]\n",
      " [ -1.4418e-09]\n",
      " [ -6.9117e-10]]\n",
      "bond loss fake:\n",
      "[[-0.0089]\n",
      " [-0.0096]\n",
      " [-0.0205]\n",
      " [-0.0429]]\n",
      "angle loss real:\n",
      "[[ -1.2017e-10]\n",
      " [ -4.1719e-11]\n",
      " [ -8.3872e-11]\n",
      " [ -4.9765e-11]]\n",
      "angle loss fake:\n",
      "[[-0.0011]\n",
      " [-0.0013]\n",
      " [-0.0017]\n",
      " [-0.0015]]\n",
      "lam1\n",
      "-0.00401023\n",
      "lam2\n",
      "-0.00404358\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2\n",
      "[[-64.5507   2.1718  19.8423]\n",
      " [-63.7816   1.6845  20.1052]\n",
      " [-64.2295   0.3479  20.3903]\n",
      " [-65.6781   1.1852  20.3127]]\n",
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[  9.8006   8.55    33.5138]\n",
      " [  9.6392   8.3183  32.6781]\n",
      " [  9.3136   6.5038  32.0354]\n",
      " [  8.1219   7.5853  32.4329]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.17196393013\n",
      "Training epoch 5000\n",
      "Iter: 5000\n",
      "D loss: 0.01188\n",
      "G_loss: 0.2868\n",
      "real:\n",
      "[[-1.3582  0.5161  0.9284]\n",
      " [-0.2339 -0.5409  0.8776]\n",
      " [ 0.6738 -0.5803 -0.5253]\n",
      " [ 0.9183  0.6051 -1.2808]]\n",
      "fake:\n",
      "[[-84.3637  -0.3468  21.6557]\n",
      " [-83.5362  -0.6566  21.0708]\n",
      " [-84.1199  -1.6966  21.8712]\n",
      " [-85.3415  -1.1759  21.981 ]]\n",
      "bond real:\n",
      "[[  0.0000e+00   2.3846e+00   0.0000e+00   0.0000e+00]\n",
      " [  2.3846e+00   0.0000e+00   2.7940e+00   0.0000e+00]\n",
      " [  0.0000e+00   2.7940e+00   0.0000e+00   2.0353e+00]\n",
      " [  0.0000e+00   0.0000e+00   2.0353e+00   4.7684e-07]]\n",
      "bond fake:\n",
      "[[ 0.      1.1177  0.      0.    ]\n",
      " [ 1.1177  0.      2.0679  0.    ]\n",
      " [ 0.      2.0679  0.      1.7817]\n",
      " [ 0.      0.      1.7817  0.    ]]\n",
      "angles real computed\n",
      "[[ 0.      0.      0.     -0.    ]\n",
      " [ 0.      0.      0.     -0.5178]\n",
      " [ 0.      0.      0.      0.    ]\n",
      " [-0.     -0.5178  0.      0.    ]]\n",
      "angles fake:\n",
      "[[ 0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.     -0.1357]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [ 0.     -0.1357 -0.      0.    ]]\n",
      "bond loss real:\n",
      "[[ -1.1104e-09]\n",
      " [ -5.5373e-10]\n",
      " [ -1.4479e-09]\n",
      " [ -1.6482e-09]]\n",
      "bond loss fake:\n",
      "[[-0.0049]\n",
      " [-0.0067]\n",
      " [-0.0169]\n",
      " [-0.0212]]\n",
      "angle loss real:\n",
      "[[ -2.6706e-11]\n",
      " [ -4.3521e-11]\n",
      " [ -3.2359e-11]\n",
      " [ -8.2891e-11]]\n",
      "angle loss fake:\n",
      "[[-0.0018]\n",
      " [-0.0014]\n",
      " [-0.0022]\n",
      " [-0.0012]]\n",
      "lam1\n",
      "-0.00336069\n",
      "lam2\n",
      "-0.00410694\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2\n",
      "[[-84.3613  -0.3424  21.6655]\n",
      " [-83.5338  -0.6524  21.0803]\n",
      " [-84.1176  -1.6921  21.8805]\n",
      " [-85.339   -1.1716  21.9903]]\n",
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[ 15.6656  14.4915  44.7411]\n",
      " [ 15.7421  13.5486  43.8961]\n",
      " [ 15.2965  11.8611  44.0445]\n",
      " [ 13.922   12.6111  44.2789]]\n",
      "1.16413807869\n",
      "Training epoch 6000\n",
      "Iter: 6000\n",
      "D loss: 0.06599\n",
      "G_loss: 0.2763\n",
      "real:\n",
      "[[ 0.6606  0.7293 -1.4159]\n",
      " [ 0.0746  1.0542 -0.0249]\n",
      " [-0.6136 -0.2268  0.7992]\n",
      " [-0.1216 -1.5567  0.6416]]\n",
      "fake:\n",
      "[[-104.6242    0.7196   21.7621]\n",
      " [-103.7727    0.4428   21.6688]\n",
      " [-104.569    -0.5608   22.5907]\n",
      " [-105.8415   -0.1203   22.5745]]\n",
      "bond real:\n",
      "[[  0.0000e+00   2.3842e+00   0.0000e+00   0.0000e+00]\n",
      " [  2.3842e+00   0.0000e+00   2.7932e+00   0.0000e+00]\n",
      " [  0.0000e+00   2.7932e+00   0.0000e+00   2.0358e+00]\n",
      " [  0.0000e+00   0.0000e+00   2.0358e+00  -9.5367e-07]]\n",
      "bond fake:\n",
      "[[ 0.      0.8691  0.      0.    ]\n",
      " [ 0.8691  0.      2.5303  0.    ]\n",
      " [ 0.      2.5303  0.      1.8818]\n",
      " [ 0.      0.      1.8818  0.    ]]\n",
      "angles real computed\n",
      "[[ 0.     0.     0.    -0.   ]\n",
      " [ 0.     0.     0.    -0.518]\n",
      " [ 0.     0.     0.    -0.   ]\n",
      " [-0.    -0.518 -0.     0.   ]]\n",
      "angles fake:\n",
      "[[ 0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.     -0.2555]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [ 0.     -0.2555 -0.      0.    ]]\n",
      "bond loss real:\n",
      "[[ -1.3653e-09]\n",
      " [ -8.6528e-10]\n",
      " [ -5.0279e-10]\n",
      " [ -1.3469e-09]]\n",
      "bond loss fake:\n",
      "[[-0.0054]\n",
      " [-0.0121]\n",
      " [-0.0208]\n",
      " [-0.0127]]\n",
      "angle loss real:\n",
      "[[ -4.7850e-11]\n",
      " [ -6.7791e-11]\n",
      " [ -8.4626e-11]\n",
      " [ -5.3674e-11]]\n",
      "angle loss fake:\n",
      "[[-0.0014]\n",
      " [-0.0017]\n",
      " [-0.0024]\n",
      " [-0.0011]]\n",
      "lam1\n",
      "-0.00340119\n",
      "lam2\n",
      "-0.00438763\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2\n",
      "[[ -1.0489e+02   7.6034e-01   2.1027e+01]\n",
      " [ -1.0405e+02   4.6842e-01   2.0909e+01]\n",
      " [ -1.0490e+02  -5.5334e-01   2.1750e+01]\n",
      " [ -1.0615e+02  -7.9021e-02   2.1743e+01]]\n",
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[ 10.6262  15.0898  50.4816]\n",
      " [ 10.3927  14.1565  49.1741]\n",
      " [ 10.0103  12.8161  49.0971]\n",
      " [  8.6837  13.5676  49.2499]]\n",
      "1.16662287712\n",
      "Training epoch 7000\n",
      "Iter: 7000\n",
      "D loss: 0.03751\n",
      "G_loss: 0.1323\n",
      "real:\n",
      "[[ 0.6328 -1.3058  0.9314]\n",
      " [ 1.05   -0.1163  0.0398]\n",
      " [-0.1874  0.6657 -0.767 ]\n",
      " [-1.4954  0.7563 -0.2042]]\n",
      "fake:\n",
      "[[-119.8866   -0.2484   31.583 ]\n",
      " [-119.1432   -0.6929   31.4754]\n",
      " [-119.6507   -1.8964   32.6035]\n",
      " [-121.2906   -1.4524   32.5247]]\n",
      "bond real:\n",
      "[[ 0.      2.3837  0.      0.    ]\n",
      " [ 2.3837  0.      2.7952  0.    ]\n",
      " [ 0.      2.7952  0.      2.0355]\n",
      " [ 0.      0.      2.0355  0.    ]]\n",
      "bond fake:\n",
      "[[ 0.      0.7559  0.      0.    ]\n",
      " [ 0.7559  0.      2.9375  0.    ]\n",
      " [ 0.      2.9375  0.      2.875 ]\n",
      " [ 0.      0.      2.875   0.    ]]\n",
      "angles real computed\n",
      "[[ 0.      0.      0.     -0.    ]\n",
      " [ 0.      0.      0.     -0.5179]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [-0.     -0.5179 -0.      0.    ]]\n",
      "angles fake:\n",
      "[[ 0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.     -0.0866]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [ 0.     -0.0866 -0.      0.    ]]\n",
      "bond loss real:\n",
      "[[ -1.7539e-10]\n",
      " [ -9.1323e-10]\n",
      " [ -7.4102e-10]\n",
      " [ -1.1993e-09]]\n",
      "bond loss fake:\n",
      "[[-0.0075]\n",
      " [-0.0086]\n",
      " [-0.016 ]\n",
      " [-0.0072]]\n",
      "angle loss real:\n",
      "[[ -7.3274e-13]\n",
      " [ -1.0358e-10]\n",
      " [ -6.1712e-11]\n",
      " [ -6.3876e-11]]\n",
      "angle loss fake:\n",
      "[[-0.0007]\n",
      " [-0.0018]\n",
      " [-0.0018]\n",
      " [-0.0016]]\n",
      "lam1\n",
      "-0.00320519\n",
      "lam2\n",
      "-0.00399756\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2\n",
      "[[-121.8506    1.9926   34.1083]\n",
      " [-121.1149    1.5889   33.954 ]\n",
      " [-121.5981    0.2753   35.0768]\n",
      " [-123.2766    0.6005   35.0392]]\n",
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[ 15.3412  17.9618  60.5552]\n",
      " [ 14.9549  17.525   59.1844]\n",
      " [ 14.9047  16.1556  58.7284]\n",
      " [ 14.0656  17.0919  59.1169]]\n",
      "1.17913007736\n",
      "Training epoch 8000\n",
      "Iter: 8000\n",
      "D loss: 0.04053\n",
      "G_loss: 0.3533\n",
      "real:\n",
      "[[-0.6159  1.1723  1.1043]\n",
      " [-0.1501  0.9841 -0.3557]\n",
      " [ 0.5735 -0.4797 -0.7125]\n",
      " [ 0.1924 -1.6768 -0.0361]]\n",
      "fake:\n",
      "[[-143.6012    2.585    45.324 ]\n",
      " [-142.9178    2.3016   45.4772]\n",
      " [-143.6405    1.4892   46.3139]\n",
      " [-144.9646    1.8914   46.1395]]\n",
      "bond real:\n",
      "[[ 0.      2.3842  0.      0.    ]\n",
      " [ 2.3842  0.      2.7934  0.    ]\n",
      " [ 0.      2.7934  0.      2.0356]\n",
      " [ 0.      0.      2.0356  0.    ]]\n",
      "bond fake:\n",
      "[[ 0.      0.5703  0.      0.    ]\n",
      " [ 0.5703  0.      1.8828  0.    ]\n",
      " [ 0.      1.8828  0.      1.9434]\n",
      " [ 0.      0.      1.9434  0.    ]]\n",
      "angles real computed\n",
      "[[ 0.      0.      0.     -0.    ]\n",
      " [ 0.      0.      0.     -0.5182]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [-0.     -0.5182 -0.      0.    ]]\n",
      "angles fake:\n",
      "[[ 0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.     -0.2501]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [ 0.     -0.2501 -0.      0.    ]]\n",
      "bond loss real:\n",
      "[[ -1.4810e-09]\n",
      " [ -1.9822e-09]\n",
      " [ -4.8962e-10]\n",
      " [ -1.6777e-09]]\n",
      "bond loss fake:\n",
      "[[-0.0099]\n",
      " [-0.0133]\n",
      " [-0.0134]\n",
      " [-0.0451]]\n",
      "angle loss real:\n",
      "[[ -2.5026e-10]\n",
      " [ -2.6015e-11]\n",
      " [ -3.6064e-11]\n",
      " [ -2.0586e-11]]\n",
      "angle loss fake:\n",
      "[[-0.0009]\n",
      " [-0.0012]\n",
      " [-0.0016]\n",
      " [-0.0012]]\n",
      "lam1\n",
      "-0.00369191\n",
      "lam2\n",
      "-0.00380893\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2\n",
      "[[-143.2561    2.6048   45.4916]\n",
      " [-142.5775    2.3262   45.6542]\n",
      " [-143.2979    1.5164   46.509 ]\n",
      " [-144.6178    1.9097   46.3302]]\n",
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[ 19.4332  25.8557  74.094 ]\n",
      " [ 19.1706  25.3772  72.2602]\n",
      " [ 18.8797  23.7918  72.1613]\n",
      " [ 17.3895  24.42    72.5382]]\n",
      "1.18215799332\n",
      "Training epoch 9000\n",
      "Iter: 9000\n",
      "D loss: 0.05783\n",
      "G_loss: 0.03163\n",
      "real:\n",
      "[[-0.764   0.6631 -1.3963]\n",
      " [ 0.2178  1.0027 -0.2541]\n",
      " [ 0.3532 -0.1371  0.9608]\n",
      " [ 0.1929 -1.5287  0.6897]]\n",
      "fake:\n",
      "[[-161.5093    3.0534   50.075 ]\n",
      " [-160.5753    2.722    50.0581]\n",
      " [-161.2752    1.2644   50.607 ]\n",
      " [-162.7393    1.6638   50.545 ]]\n",
      "bond real:\n",
      "[[ 0.      2.3844  0.      0.    ]\n",
      " [ 2.3844  0.      2.7934  0.    ]\n",
      " [ 0.      2.7934  0.      2.036 ]\n",
      " [ 0.      0.      2.036   0.    ]]\n",
      "bond fake:\n",
      "[[ 0.      0.9824  0.      0.    ]\n",
      " [ 0.9824  0.      2.916   0.    ]\n",
      " [ 0.      2.916   0.      2.3086]\n",
      " [ 0.      0.      2.3086  0.    ]]\n",
      "angles real computed\n",
      "[[ 0.      0.     -0.     -0.    ]\n",
      " [ 0.      0.     -0.     -0.5182]\n",
      " [-0.     -0.      0.     -0.    ]\n",
      " [-0.     -0.5182 -0.      0.    ]]\n",
      "angles fake:\n",
      "[[ 0.      0.      0.      0.    ]\n",
      " [ 0.      0.      0.     -0.1576]\n",
      " [ 0.      0.      0.     -0.    ]\n",
      " [ 0.     -0.1576 -0.      0.    ]]\n",
      "bond loss real:\n",
      "[[ -1.6933e-09]\n",
      " [ -1.4896e-09]\n",
      " [ -1.3056e-09]\n",
      " [ -1.1490e-09]]\n",
      "bond loss fake:\n",
      "[[-0.0047]\n",
      " [-0.007 ]\n",
      " [-0.0045]\n",
      " [-0.0106]]\n",
      "angle loss real:\n",
      "[[ -3.0974e-11]\n",
      " [ -3.4933e-11]\n",
      " [ -9.2105e-11]\n",
      " [ -4.7925e-11]]\n",
      "angle loss fake:\n",
      "[[-0.0008]\n",
      " [-0.0016]\n",
      " [-0.0017]\n",
      " [-0.0011]]\n",
      "lam1\n",
      "-0.00347217\n",
      "lam2\n",
      "-0.00386381\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2\n",
      "[[-161.5263    3.1093   50.2105]\n",
      " [-160.5909    2.7755   50.1857]\n",
      " [-161.2918    1.3169   50.7307]\n",
      " [-162.7534    1.7145   50.674 ]]\n",
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[ 24.5556  32.1146  92.5088]\n",
      " [ 24.2284  31.2057  90.5484]\n",
      " [ 23.9612  28.9694  89.7831]\n",
      " [ 22.6562  29.9702  90.8255]]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-294-f5ff8591a82f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             _, D_loss_curr, _ = sess.run(\n\u001b[0;32m---> 18\u001b[0;31m                 [D_solver, D_loss, clip_D], feed_dict=feed_dict)\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mfeed_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstruct_feed_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/home/evan/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/evan/.local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "every = 1000\n",
    "train_dataset = features[:64*12]\n",
    "n_train = len(train_dataset)\n",
    "\n",
    "for it in range(1000000):\n",
    "    a = time.time()\n",
    "    random.shuffle(train_dataset)\n",
    "    \n",
    "    batch_sched = list(range(0, n_train+1,S))\n",
    "    for j in range(0, len(batch_sched)-1):\n",
    "        start = batch_sched[j]\n",
    "        stop = batch_sched[j+1]\n",
    "        \n",
    "        for _ in range(5):\n",
    "            feed_dict = construct_feed_dict(train_dataset, start, stop)\n",
    "\n",
    "            _, D_loss_curr, _ = sess.run(\n",
    "                [D_solver, D_loss, clip_D], feed_dict=feed_dict)\n",
    "        \n",
    "        feed_dict = construct_feed_dict(train_dataset, start, stop)\n",
    "\n",
    "        _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict=feed_dict)\n",
    "        \n",
    "        d_losses.append(D_loss_curr)\n",
    "        g_losses.append(G_loss_curr)\n",
    "    \n",
    "    if it % every == 0:\n",
    "\n",
    "        print(time.time()-a)\n",
    "        print(\"Training epoch %d\" %it)\n",
    "                \n",
    "        print('Iter: {}'.format(it))\n",
    "        print('D loss: {:.4}'. format(D_loss_curr))\n",
    "        print('G_loss: {:.4}'.format(G_loss_curr))\n",
    "        feed_dict = construct_feed_dict(features, 0, S, shuffle_inds=False, keep_prob_val=1.)\n",
    "\n",
    "        print(\"real:\")\n",
    "        print(feed_dict[mol_xyz][0][:4])\n",
    "        print(\"fake:\")\n",
    "        print(sess.run(G_sample, feed_dict=feed_dict)[0][:4])\n",
    "        \n",
    "        \n",
    "        print(\"bond real:\")\n",
    "        print(sess.run(bond_dist_real, feed_dict=feed_dict)[0][:4,:4])\n",
    "        #print(sess.run(computed_dist_real, feed_dict=feed_dict)[0][:4])\n",
    "\n",
    "        #print(feed_dict[bond_dist_mat][0][:4])\n",
    "\n",
    "\n",
    "        print(\"bond fake:\")\n",
    "        print(sess.run(bond_dist_fake, feed_dict=feed_dict)[0][:4,:4])\n",
    "    \n",
    "        #print(\"angles real:\")\n",
    "        #print(feed_dict[angle_tensor][0][2,:4,:4])\n",
    "        print(\"angles real computed\")\n",
    "        \n",
    "        print(sess.run(angles_gen_real, feed_dict=feed_dict)[0][2,:4,:4])\n",
    "        print(\"angles fake:\")\n",
    "        print(sess.run(angles_gen_fake, feed_dict=feed_dict)[0][2,:4,:4])\n",
    "        \n",
    "        print(\"bond loss real:\")\n",
    "        print(sess.run(wrong_bond_real, feed_dict=feed_dict)[:4])\n",
    "        print(\"bond loss fake:\")\n",
    "        print(sess.run(wrong_bond_fake, feed_dict=feed_dict))[:4] \n",
    "\n",
    "        print(\"angle loss real:\")\n",
    "        print(sess.run(wrong_angle_real, feed_dict=feed_dict)[:4])\n",
    "        print(\"angle loss fake:\")\n",
    "        print(sess.run(wrong_angle_fake, feed_dict=feed_dict)[:4])\n",
    "\n",
    "        print(\"lam1\")\n",
    "        print(sess.run(lam1, feed_dict=feed_dict))\n",
    "        print(\"lam2\")\n",
    "        print(sess.run(lam2, feed_dict=feed_dict))\n",
    "        \n",
    "        print(\"\\n\\n\\n\")\n",
    "        \n",
    "        generate_molecule(features[0][0], sess.run(G_sample, feed_dict=feed_dict)[0], \"/home/evan/Documents/deep_docking/dcgan_pg/DCGAN-tensorflow/generated_mols/%s_test%d.mol\" %(features[0][0].split(\".\")[0].split(\"/\")[-1], it))\n",
    "        \n",
    "        feed_dict = construct_feed_dict(features, len(features)-S, len(features), shuffle_inds=False, keep_prob_val=1.)\n",
    "        generate_molecule(features[-1][0], sess.run(G_sample, feed_dict=feed_dict)[0], \"/home/evan/Documents/deep_docking/dcgan_pg/DCGAN-tensorflow/generated_mols/%s_test%d.mol\" %(features[-1][0].split(\".\")[0].split(\"/\")[-1], it))\n",
    "\n",
    "        \n",
    "        \n",
    "\n",
    "        #print(\"output_i\")\n",
    "        #print(sess.run(G_sample_output_i, feed_dict=feed_dict))\n",
    "        #print(sess.run(G_sample, feed_dict=feed_dict))\n",
    "\n",
    "    #random.shuffle(train_dataset)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 981,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.6222,  3.1759, -1.7272],\n",
       "       [-2.3206,  3.5   , -1.361 ],\n",
       "       [-3.3241,  4.8481, -1.2788],\n",
       "       [-3.1506,  5.661 , -0.1399],\n",
       "       [ 3.7804,  1.2327, -2.3614],\n",
       "       [ 4.117 ,  1.536 , -2.2268],\n",
       "       [ 3.8312,  1.4693, -2.2949],\n",
       "       [ 3.9703,  1.5608, -2.1931],\n",
       "       [ 3.6365,  1.1914, -2.3312],\n",
       "       [ 4.0506,  1.8041, -1.9465],\n",
       "       [ 3.7155,  1.3008, -2.2993],\n",
       "       [ 4.0951,  1.5755, -2.1624]], dtype=float32)"
      ]
     },
     "execution_count": 981,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feed_dict = construct_feed_dict(features, 0, S, shuffle_inds=False)\n",
    "\n",
    "sess.run(G_sample, feed_dict=feed_dict)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 982,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2',\n",
       " array([[ 0.    ,  2.3839,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 2.3839,  0.    ,  2.7936,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  2.7936,  0.    ,  2.0357,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  2.0357,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,  0.    ,\n",
       "          0.    ,  0.    ,  0.    ,  0.    ,  0.    ]]),\n",
       " array([[-0.0725, -0.0715, -1.7212],\n",
       "        [-0.8925,  0.3145, -0.4713],\n",
       "        [-0.2245, -0.1035,  1.0027],\n",
       "        [ 1.1895, -0.1395,  1.1898],\n",
       "        [ 0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ],\n",
       "        [ 0.    ,  0.    ,  0.    ]]),\n",
       " array([[[ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         ..., \n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ]],\n",
       " \n",
       "        [[ 0.    ,  0.    , -0.4392, ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [-0.4392,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         ..., \n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ]],\n",
       " \n",
       "        [[ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         ..., \n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ]],\n",
       " \n",
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       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ]],\n",
       " \n",
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       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
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       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ],\n",
       "         [ 0.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  0.    ]]]),\n",
       " array([[[0, 0, 0, ..., 0, 0, 0],\n",
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       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       " \n",
       "        [[0, 0, 1, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [1, 0, 0, ..., 0, 0, 0],\n",
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       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       " \n",
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       "         [0, 0, 0, ..., 0, 0, 0]],\n",
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       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]],\n",
       " \n",
       "        [[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ..., \n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]]], dtype=uint8),\n",
       " 3,\n",
       " 4,\n",
       " (array([[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8),\n",
       "  array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "         [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "          0, 1, 0, 1, 0, 0, 0, 0, 0],\n",
       "         [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "          0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "          0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "          0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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       "          0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "          0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)))"
      ]
     },
     "execution_count": 982,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 955,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/evan/Documents/deep_docking/alanine_dipeptide.pdb\n",
      "[[ 1.2995 -3.2777 -0.0282]\n",
      " [ 2.0037 -2.1657 -0.705 ]\n",
      " [ 1.5421 -1.8185 -1.3216]\n",
      " [-0.0418 -1.6029  0.2904]]\n"
     ]
    }
   ],
   "source": [
    "feed_dict = construct_feed_dict(features, len(features)-S, len(features), shuffle_inds=False)\n",
    "generate_molecule(features[-1][0], sess.run(G_sample, feed_dict=feed_dict)[0], \"/home/evan/Documents/deep_docking/dcgan_pg/DCGAN-tensorflow/generated_mols/%s_test%d.mol\" %(features[-1][0].split(\".\")[0].split(\"/\")[-1], it))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.min(results[0])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.where(features[0][1][:,:,:,:-1] == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 886,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ndef discriminator(W_list, b_list, h_list, L_list, n_layers, mols):\\n    mols = tf.add(mols, mol_noise)\\n    with tf.device(\\'/gpu:0\\'):\\n        for layer_idx in range(n_layers):\\n            h_list.append(tf.nn.dropout(adjacency_conv_layer(h_list[layer_idx], W_list[layer_idx],\\n                                             b_list[layer_idx], adj_list[layer_idx], \\n                                             clades_list[layer_idx],\\n                                             L_list[layer_idx], L_list[layer_idx+1],\\n                                             layer_idx, S, B_list[layer_idx]), keep_prob))\\n        h_final = h_list[-1]\\n        dist_matrix = tf.concat([tf.reshape(compute_distance_matrix(tf.reshape(mol, [B, 3])), (1, B, B)) for mol in tf.split(mols, S, axis=0)], axis=0)\\n        print(\"dist_matrix\")\\n        print(dist_matrix)\\n        #dist_matrix = compute_distance_matrix(mol)\\n        bond_dist_gen = tf.multiply(dist_matrix, adj_matrix)\\n        wrong_bond_loss = tf.divide(tf.reshape(tf.multiply(tf.reduce_sum(tf.square(tf.subtract(bond_dist_mat, bond_dist_gen)), axis=[1,2]), lam1), (S,1)), n_bonds)\\n        print(\"wrong_bond_loss\")\\n        print(wrong_bond_loss)\\n\\n        angles = []\\n        for i in range(0, B):\\n            for j in range(0, B):\\n                ij = mols[:, i,:] - mols[:,j,:]\\n                ij = ij / tf.sqrt(tf.reduce_sum(tf.square(ij), 1, keep_dims=True))\\n                #ij shape should be (S, 3)\\n               # print(\"ij\")\\n                #print(ij)\\n                for k in range(0,B):\\n                    ik = mols[:,i,:] - mols[:,k,:]\\n                    ik = ik / tf.sqrt(tf.reduce_sum(tf.square(ik), 1, keep_dims=True))\\n\\n                    #ik shape should be (S, 3)\\n                    #if i == 0 and j == 0 and k == 0: \\n                    #   print(\"ik\")\\n                     #   print(ik)\\n                        \\n                    dp = tf.reduce_sum(tf.multiply(ij, ik), axis=1, keep_dims=True)\\n                    #dp shape should be (S,1)\\n                    #if i ==0 and j ==0 and k==0:\\n                    #    print(\"dp\")\\n                    #    print(dp)\\n                    if i == j or i == k or j == k:\\n                        angle = tf.Variable(tf.constant(np.zeros([4,1]).astype(np.float32)))\\n                    else:\\n                        angle = tf.acos(tf.clip_by_value(tf.reduce_sum(tf.multiply(ij, ik), axis=1, keep_dims=True), -1.0, 1.0))\\n                    #if i ==0 and j==0 and k==0:\\n                        #print(\"angle\")\\n                        #print(angle)\\n                    angles.append(angle)\\n        angles_gen = tf.reshape(tf.concat(angles, axis=1), [S, B, B, B])\\n        angles_gen = tf.multiply(angles_gen, angle_tuples)\\n        wrong_angles_loss = tf.divide(tf.reshape(tf.multiply(tf.reduce_sum(tf.square(tf.subtract(angle_tensor, angles_gen)), axis=[1,2,3]), lam2), (S,1)), n_angles)\\n                \\n        feat_i = tf.reshape(h_final, (S, B, 1, L_list[n_layers]))\\n        feat_i = tf.tile(feat_i, [1, 1, B, 1])\\n        \\n        feat_j = tf.reshape(h_final, (S, B, 1, L_list[n_layers]))\\n        feat_j = tf.transpose(feat_j, perm=[0, 2, 1, 3])\\n        feat_j = tf.tile(feat_j, [1, B, 1, 1])\\n        \\n        d_h1 = tf.concat([tf.reshape(dist_matrix, [S, B, B, 1]), feat_i, feat_j], axis=3)\\n        d_h1 = tf.reshape(d_h1, [S, B, B*(L_list[n_layers]*2+1)])\\n        d_h2 = leaky_relu(tf.matmul(d_h1, d_w1) + d_b1)\\n        d_h2 = tf.divide(tf.reduce_sum(tf.matmul(d_h2, d_w2) + d_b2, axis=1), n_bonds)#, axis=1\\n        print(\"d_h2\")\\n        print(d_h2)\\n        \\n    \\n    return(wrong_bond_loss + wrong_angles_loss + d_h2, bond_dist_gen, angles_gen, wrong_bond_loss, wrong_angles_loss)\\n'"
      ]
     },
     "execution_count": 886,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "def discriminator(W_list, b_list, h_list, L_list, n_layers, mols):\n",
    "    mols = tf.add(mols, mol_noise)\n",
    "    with tf.device('/gpu:0'):\n",
    "        for layer_idx in range(n_layers):\n",
    "            h_list.append(tf.nn.dropout(adjacency_conv_layer(h_list[layer_idx], W_list[layer_idx],\n",
    "                                             b_list[layer_idx], adj_list[layer_idx], \n",
    "                                             clades_list[layer_idx],\n",
    "                                             L_list[layer_idx], L_list[layer_idx+1],\n",
    "                                             layer_idx, S, B_list[layer_idx]), keep_prob))\n",
    "        h_final = h_list[-1]\n",
    "        dist_matrix = tf.concat([tf.reshape(compute_distance_matrix(tf.reshape(mol, [B, 3])), (1, B, B)) for mol in tf.split(mols, S, axis=0)], axis=0)\n",
    "        print(\"dist_matrix\")\n",
    "        print(dist_matrix)\n",
    "        #dist_matrix = compute_distance_matrix(mol)\n",
    "        bond_dist_gen = tf.multiply(dist_matrix, adj_matrix)\n",
    "        wrong_bond_loss = tf.divide(tf.reshape(tf.multiply(tf.reduce_sum(tf.square(tf.subtract(bond_dist_mat, bond_dist_gen)), axis=[1,2]), lam1), (S,1)), n_bonds)\n",
    "        print(\"wrong_bond_loss\")\n",
    "        print(wrong_bond_loss)\n",
    "\n",
    "        angles = []\n",
    "        for i in range(0, B):\n",
    "            for j in range(0, B):\n",
    "                ij = mols[:, i,:] - mols[:,j,:]\n",
    "                ij = ij / tf.sqrt(tf.reduce_sum(tf.square(ij), 1, keep_dims=True))\n",
    "                #ij shape should be (S, 3)\n",
    "               # print(\"ij\")\n",
    "                #print(ij)\n",
    "                for k in range(0,B):\n",
    "                    ik = mols[:,i,:] - mols[:,k,:]\n",
    "                    ik = ik / tf.sqrt(tf.reduce_sum(tf.square(ik), 1, keep_dims=True))\n",
    "\n",
    "                    #ik shape should be (S, 3)\n",
    "                    #if i == 0 and j == 0 and k == 0: \n",
    "                    #   print(\"ik\")\n",
    "                     #   print(ik)\n",
    "                        \n",
    "                    dp = tf.reduce_sum(tf.multiply(ij, ik), axis=1, keep_dims=True)\n",
    "                    #dp shape should be (S,1)\n",
    "                    #if i ==0 and j ==0 and k==0:\n",
    "                    #    print(\"dp\")\n",
    "                    #    print(dp)\n",
    "                    if i == j or i == k or j == k:\n",
    "                        angle = tf.Variable(tf.constant(np.zeros([4,1]).astype(np.float32)))\n",
    "                    else:\n",
    "                        angle = tf.acos(tf.clip_by_value(tf.reduce_sum(tf.multiply(ij, ik), axis=1, keep_dims=True), -1.0, 1.0))\n",
    "                    #if i ==0 and j==0 and k==0:\n",
    "                        #print(\"angle\")\n",
    "                        #print(angle)\n",
    "                    angles.append(angle)\n",
    "        angles_gen = tf.reshape(tf.concat(angles, axis=1), [S, B, B, B])\n",
    "        angles_gen = tf.multiply(angles_gen, angle_tuples)\n",
    "        wrong_angles_loss = tf.divide(tf.reshape(tf.multiply(tf.reduce_sum(tf.square(tf.subtract(angle_tensor, angles_gen)), axis=[1,2,3]), lam2), (S,1)), n_angles)\n",
    "                \n",
    "        feat_i = tf.reshape(h_final, (S, B, 1, L_list[n_layers]))\n",
    "        feat_i = tf.tile(feat_i, [1, 1, B, 1])\n",
    "        \n",
    "        feat_j = tf.reshape(h_final, (S, B, 1, L_list[n_layers]))\n",
    "        feat_j = tf.transpose(feat_j, perm=[0, 2, 1, 3])\n",
    "        feat_j = tf.tile(feat_j, [1, B, 1, 1])\n",
    "        \n",
    "        d_h1 = tf.concat([tf.reshape(dist_matrix, [S, B, B, 1]), feat_i, feat_j], axis=3)\n",
    "        d_h1 = tf.reshape(d_h1, [S, B, B*(L_list[n_layers]*2+1)])\n",
    "        d_h2 = leaky_relu(tf.matmul(d_h1, d_w1) + d_b1)\n",
    "        d_h2 = tf.divide(tf.reduce_sum(tf.matmul(d_h2, d_w2) + d_b2, axis=1), n_bonds)#, axis=1\n",
    "        print(\"d_h2\")\n",
    "        print(d_h2)\n",
    "        \n",
    "    \n",
    "    return(wrong_bond_loss + wrong_angles_loss + d_h2, bond_dist_gen, angles_gen, wrong_bond_loss, wrong_angles_loss)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mol = Chem.MolFromMol2File(ligand_files[0])\n",
    "c = mol.GetConformer(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c.SetAtomPosition(0, [0., 0., 0.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2,  7,  9, 12]),\n",
       " array([ 4,  4, 13,  9]),\n",
       " array([ 5,  6, 11,  8]),\n",
       " array([5, 2, 3, 3]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(features[2][1][:,:,:,:-1] == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = sess.run(G_sample, feed_dict=feed_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.run(pred_real, feed_dict=feed_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.max(results[0][:,:,:,:], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "         2.00000000e+00,   0.00000000e+00,   1.00000000e+00,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
       "         4.09600000e+03])"
      ]
     },
     "execution_count": 615,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol = Chem.MolFromMol2File('/home/evan/Documents/deep_docking/datasets/v2015/4wks/4wks_ligand.mol2')\n",
    "mol.GetNumAtoms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit.Chem import AllChem\n",
    "AllChem.Compute2DCoords(mol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAAEsCAYAAAB5fY51AAALDElEQVR4nO3cf6iW9f3H8ddMdDo9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\ngDIECyhDsIAyBAso438B7w9cPBQEks4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=300x300 at 0x7F35E87B9A50>"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit.Chem import Draw\n",
    "\n",
    "Draw.MolToImage(mol)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
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}