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
Jupyter Notebooks for learning the PyRosetta platform for biomolecular structure prediction and design
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
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.
De novo protein structure prediction using iteratively predicted structural constraints
Fast and accurate protein structure prediction
Fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes
A toolkit developed to predict and analyze PROTAC-mediated ternary complexes using AlphaFold3 and Boltz-1.
A Rust implementation of the LightDock macromolecular docking software
Differentiable Pairing using Alignment-based Language Models
An interactive visual simulator for distance-based protein folding
PyMissense creates the pathogenicity plot and modified pdb as shown in the AlphaMissense paper for custom proteins.
NetSurfP 3.0: Protein secondary structure and relative solvent accessibility prediction
Step-by-step guide to install and configure AlphaFold 3 using a Conda Python 3.11 environment. No system-wide installations required. ✅ Miniconda setup & dependencies ✅ Repository cloning & model setup ✅ Database configuration & execution script 🔹 Requirements: Linux, NVIDIA GPU (Ampere+), CUDA, ~700GB disk space.
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