Transmembrane proteins predicted through Language Model embeddings

Helpful tools and examples for working with flex-attention

Benchmark for Biophysical Sequence Optimization Algorithms

gReLU is a python library to train, interpret, and apply deep learning models to DNA sequences.

Code repository for Ensemble Bayesian Optimization

A Library for Gaussian Processes in Chemistry

Tensorflow implementation of stochastic spectral-sums estimators

A generative model for programmable protein design

Reference implementation for DPO (Direct Preference Optimization)

Official implementation for HyenaDNA, a long-range genomic foundation model built with Hyena

GENA-LM is a transformer masked language model trained on human DNA sequence.

Python bindings for the TM-align algorithm and code for protein structure comparison developed by Zhang et al.

Benchmarking DNA Language Models on Biologically Meaningful Tasks

Fitness landscape exploration sandbox for biological sequence design.

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

GraphPart, a data partitioning method for ML on biological sequences

MMseqs2: ultra fast and sensitive search and clustering suite

allRank is a framework for training learning-to-rank neural models based on PyTorch.

Generate high-order Markov random protein sequences

Collects software dedicated to predicting specific properties of peptides

A simple and efficient tool to parallelize Pandas operations on all available CPUs

An implementation of Performer, a linear attention-based transformer, in Pytorch

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

A python port of the R package SCTransform:

Go binding for Pytorch C++ API (libtorch)

add statistical annotations (pvalue significance) on an existing boxplot generated by seaborn boxplot

FastFormers - highly efficient transformer models for NLU

Listing of papers about machine learning for proteins.

Evaluation of Pre-trained Amino Acid Embeddings in Protein Prediction Tasks done in PyTorch

A Pytorch implementation of the optimal transport kernel embedding