Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
- drug-discovery
- quantum-chemistry
- deep-learning
- biology
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Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
The official sources for the RDKit library
Message Passing Neural Networks for Molecule Property Prediction
FAIR Chemistry's library of machine learning methods for chemistry
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Open Babel is a chemical toolbox designed to speak the many languages of chemical data.
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Public/backup repository of the GROMACS molecular simulation toolkit. Please do not mine the metadata blindly; we use https://gitlab.com/gromacs/gromacs for code review and issue tracking.
Python package for graph neural networks in chemistry and biology
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
The Chemistry Development Kit
Molecular Processing Made Easy.
A Euclidean diffusion model for structure-based drug design.
Benchmarks for generative chemistry
Universal cheminformatics toolkit, utilities and database search tools
Deep Reinforcement Learning for de-novo Drug Design
Descriptor computation(chemistry) and (optional) storage for machine learning
Open Parser for Systematic IUPAC Nomenclature. Chemical name to structure conversion
Graph-based molecule modeling toolkit for cheminformatics
A Python package for molecular docking with an extensive, highly-curated dataset and a set of realistic benchmark tasks for drug discovery.
Molecule Validation and Standardization
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.