A Collection of Variational Autoencoders (VAE) in PyTorch.

Diffusion model papers, survey, and taxonomy

Code for the ProteinMPNN paper

Code for our paper "Protein sequence design with a learned potential"

Official release of the ProGen models

Computation of features of peptides and proteins

A compilation of deep learning methods for protein design

An annotated implementation of the Transformer paper.

动手学CV-Pytorch版

iFeatureOmega is a comprehensive platform for generating, analyzing and visualizing more than 170 representations for biological sequences, 3D structures and ligands. To the best of our knowledge, …

基于Gated Attention Unit的Transformer模型（尝鲜版）

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

BertViz: Visualize Attention in Transformer Models (BERT, GPT2, BART, etc.)

Feature Extraction Package for Biological Sequences

Model zoo for genomics

Conditional Transformer Language Model for Controllable Generation

Library - Vanilla, ViT, DeiT, BERT, GPT

An Open-Source Package for Network Embedding (NE)

DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

Protein sequence prediction for bert

ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.