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Boltz is a deep learning molecular modeler and biomolecular structure prediction system. It uses neural network architectures to simulate the physical folding and docking of biomolecules, specifically predicting the three-dimensional shapes of protein and ligand complexes.
The main features of jwohlwend/boltz are: Structure Prediction Models, Binding Affinity Prediction, Computational Workbenches, Molecular Deep Learning Frameworks, Protein Interaction Modeling, Affinity Scoring Functions, Geometric Modeling, Coordinate Refinement.
Open-source alternatives to jwohlwend/boltz include: deepchem/deepchem — DeepChem is an open-source Python framework for applying deep learning to molecular, chemical, and biological data,… google-deepmind/alphafold3 — AlphaFold3 is a biomolecular structure prediction model and bioinformatics structural analysis tool. It uses a deep… google-deepmind/alphafold — AlphaFold is a deep learning biology tool and structural bioinformatic pipeline designed to predict the… psipred/dmpfold2 — Fast and accurate protein structure prediction. rosettacommons/deepab — Official repository for DeepAb: Antibody structure prediction using interpretable deep learning. The code, data, and… rosettacommons/fvhallucinator — The code for FvHallucinator is made available under the Rosetta-DL license as part of the Rosetta-DL bundle.
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This repository presents an approach for ligand discovery for protein bindign pockets, by combining Monte Carlo (MC) simulations with the model Chai-1 (Chai-1 github, Chai-1 technical report). There are two types of simulations presented here: - The basic simulation explores chemical space by…