C++ package that provides tools for correcting structural predictions of proteins (eg. from X-Ray Crystallography or AlphaFold) using X-ray small-angle scattering (SAXS) in solution
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Updated
May 16, 2024 - C++
C++ package that provides tools for correcting structural predictions of proteins (eg. from X-Ray Crystallography or AlphaFold) using X-ray small-angle scattering (SAXS) in solution
Use generative ML to design new proteins using this simple, hackable implementation of protein transformer models
The first large protein language model trained follows structure instructions.
Benchmark for Biophysical Sequence Optimization Algorithms
Reveal protein energy centers.
The Rosetta Bio-macromolecule modeling package.
Complex-based Ligand-Binding Proteins Redesign by Equivariant Diffusion-based Generative Models
A library of tools for protein design
Protein Sequence Design with Deep Learning and Tooling like Monte Carlo Sampling and Analysis
List of papers about Proteins Design using Deep Learning
Calculation of interatomic interactions in molecular structures
Protein Graph Library
DE-STRESS is a model evaluation pipeline that aims to make protein design more reliable and accessible.
Geometric deep learning of protein–DNA binding specificity
A thin wrapper around ProteinMPNN for convenient sampling without having to write out or consume files
De Novo Protein Design by Equivariantly Diffusing Oriented Residue Clouds
Code to reproduce experiments in "Accelerating Bayesian Optimization for Protein Design with Denoising Autoencoders" (Stanton et al 2022)
Learning to design protein-protein interactions with enhanced generalization (ICLR24)
Protein Design by Machine Learning guided Directed Evolution
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