Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2

Implementation of Alphafold 3 from Google Deepmind in Pytorch

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

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Code for the series AlphaFold3 Decoded, to guide students along implementing AlphaFold 3 from scratch in PyTorch.

A PyTorch implementation of AlphaFold3 compatible with the official weights from DeepMind.

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to make more researchers easy to use and understand the alphafold2 by pytorch

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This repo is a re-implementation of AlphaFold by DeepMind, in PyTorch and was implemented in Nov 2020, when AlphaFold 2 was not published yet.

This code demonstrates how to use PyTorch to process the E. coli dataset and train a simple neural network for classification. If you want to use this data with AlphaFold 3 or a similar protein structure prediction model, you would need: The actual amino acid sequences of the proteins, not just the features provided in this dataset.

Docker image of https://github.com/lucidrains/alphafold3-pytorch

AlphaFold 3 inference pipeline using office parameter in pytorch.

Another implementation of AlphaFold2 using PyTorch

Forked from Urinx Casper13 in pytorch

Trainable AlphaFold 2 ported to Pytorch

A trainable PyTorch reproduction of AlphaFold 3.

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2 [Fixed by FixMyRepo SandboxLoop]

Built end-to-end deep learning pipeline using PyTorch, combining CNNs, Graph Neural Networks, and self-attention mechanisms inspired by AlphaFold. Designed to predict accurate atomic coordinates directly from raw protein sequences