Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
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Updated
Dec 16, 2025 - Python
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
User friendly and accurate binder design pipeline
๐งฌ gget enables efficient querying of genomic reference databases
Saprot: Protein Language Model with Structural Alphabet (AA+3Di)
Optimizing AlphaFold Training and Inference on GPU Clusters
Trainable PyTorch framework for developing protein, RNA and complex models.
Predicting direct protein-protein interactions with AlphaFold deep learning neural network models.
Modified version of Alphafold to divide CPU part (MSA and template searching) and GPU part. This can accelerate Alphafold when predicting multiple structures
PyMOL extension to color AlphaFold structures by confidence (pLDDT).
MMseqs2 app to run on your workstation or servers
Protein 3D structure prediction pipeline
Automated, modular pipeline for de novo protein binder design on SLURM clusters, with the BFmonitor dashboard
Exploring Evolution-aware & free protein language models as protein function predictors
Modelling protein conformational landscape with Alphafold
FrameDiPT: an SE(3) diffusion model for protein structure inpainting
A curated list of awesome self-learning materials in Computational Structural Biology, such as sources, tutorials, etc.
[๐ง๐๐ญ๐ฎ๐ซ๐ ๐ฆ๐๐๐ก๐ข๐ง๐ ๐ข๐ง๐ญ๐๐ฅ๐ฅ๐ข๐ ๐๐ง๐๐] ImmunoStruct enables multimodal deep learning for immunogenicity prediction
Run AlphaFold2 (and multimer) step by step
Cryptic pocket prediction using AlphaFold 2
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