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
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)
Benchmarks for generative chemistry
A Euclidean diffusion model for structure-based drug design.
Universal cheminformatics toolkit, utilities and database search tools
The official implementation of 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023)
Open Parser for Systematic IUPAC Nomenclature. Chemical name to structure conversion
A Python package for molecular docking with an extensive, highly-curated dataset and a set of realistic benchmark tasks for drug discovery.
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.
The Massively Parallel Quantum Chemistry program, MPQC, computes properties of atoms and molecules from first principles using the time independent Schrödinger equation.
Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.
UV-first benchmark for protein-ligand docking with reproducible annotation, pocket similarity, and HPC workflows.