Official repository for the Boltz biomolecular interaction models
- binding-prediction
- drug-discovery
- protein-structure
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Official repository for the Boltz biomolecular interaction models
An ansible-native testing framework for collections, playbooks, and roles with configurable workflows for testing any system or service
Message Passing Neural Networks for Molecule Property Prediction
Graphormer is a general-purpose deep learning backbone for molecular modeling.
Toward High-Accuracy Open-Source Biomolecular Structure Prediction.
A deep learning package for many-body potential energy representation and molecular dynamics
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
Chai-1, SOTA model for biomolecular structure prediction
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Biological foundation modeling from molecular to genome scale
Official Repository for the Uni-Mol Series Methods
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Python package for graph neural networks in chemistry and biology
An open library for the analysis of molecular dynamics trajectories
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)
Molecular Processing Made Easy.
a molecular descriptor calculator
Python-centric Cookiecutter for Molecular Computational Chemistry Packages
The repository host gives no description.
This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data
The official implementation of 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023)
Open Frontier Biomolecular Intelligence
Associated Repository for "Translation between Molecules and Natural Language"
A Python package for molecular docking with an extensive, highly-curated dataset and a set of realistic benchmark tasks for drug discovery.