Shapley Interactions and Shapley Values for Machine Learning

Chai-1, SOTA model for biomolecular structure prediction

Implementation of Alphafold 3 from Google Deepmind in Pytorch

RoseTTAFold2 protein/nucleic acid complex prediction

Joint embedding of protein sequence and structure with discrete and continuous compressions of protein folding model latent spaces. http://bit.ly/cheap-proteins

PLM based active learning model for protein engineering

Zero-shot prediction of mutation effects on protein function with multimodal deep representation learning

genes and genomes at your fingertips

sequence-based prediction of multiscale genome structure from kilobase to whole-chromosome scale

RNA-seq prediction with deep convolutional neural networks.

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

This repository contains implementations and illustrative code to accompany DeepMind publications

📦 Lempel-Ziv Complexity, fast implementations with 🐍 Python (naive, Numba or Cython for speedup), Open-Source (MIT) 👍 →

CRISPR/Cas9 off-target prediction using physically inspired features

Ankh: Optimized Protein Language Model

Protein language model trained on coding DNA

Evolutionary Scale Modeling (esm): Pretrained language models for proteins

Biological foundation modeling from molecular to genome scale

Multi-task and masked language model-based protein sequence embedding models.

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

Evolutionary couplings from protein and RNA sequence alignments

A generative latent variable model for biological sequence families.

Experiment code for Stochastic Gradient Hamiltonian Monte Carlo