Implementation of Alphafold 3 from Google Deepmind in Pytorch

Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

An implementation of the DeepMind's AlphaFold based on PyTorch for research

Recurrent Geometric Networks for end-to-end differentiable learning of protein structure

Predicting direct protein-protein interactions with AlphaFold deep learning neural network models.

[ICLR 2022] OntoProtein: Protein Pretraining With Gene Ontology Embedding

Nature Biotechnology: Ultra-fast, sensitive detection of protein remote homologs using deep dense retrieval

Structural Bioinformatics is awesome. Throw your textbook in the garbage, light the garbage can on fire, and blend the ashes into your cold brew almond milk latte and read this.

Fast and accurate protein structure prediction

PyMissense creates the pathogenicity plot and modified pdb as shown in the AlphaMissense paper for custom proteins.

A toolkit developed to predict and analyze PROTAC-mediated protein degradation complexes using AlphaFold3.

Python rich client for visual protein Sequence to Structure Analysis

Calculating a range of protein descriptors using their physicochemical, biological and structural properties 🔬.

CB513 datasets for Protein Secondary Structure Prediction

Source code and dataset for 2022 Publication "Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction"

Distance-based protein model quality estimation using deep ResNets

AIRFold Protein Structure Prediction System

OPUS-Rota5: A Highly Accurate Protein Side-chain Modeling Method with 3D-Unet and RotaFormer

This project aims to solve a simplified version of the Protein Structure Prediction problem, represented as a combinatorial optimization task, using Reinforcement Learning.

A machine learning model that builds amino acids into a protein model.