Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
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Updated
May 15, 2024 - Python
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Message Passing Neural Networks for Molecule Property Prediction
A powerful and flexible machine learning platform for drug discovery
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
Python package for graph neural networks in chemistry and biology
Working with molecular structures in pandas DataFrames
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
NequIP is a code for building E(3)-equivariant interatomic potentials
EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein
Molecular Processing Made Easy.
Interaction Fingerprints for protein-ligand complexes and more
Allegro is an open-source code for building highly scalable and accurate equivariant deep learning interatomic potentials
AI molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design and molecule optimization.
Deep generative models of 3D grids for structure-based drug discovery
MoleculeKit: Your favorite molecule manipulation kit
The Ersilia Model Hub, a repository of AI/ML models for infectious and neglected disease research.
A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.
Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval, Nat Mach Intell 2023 (https://www.nature.com/articles/s42256-023-00759-6)
[ICLR'23 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
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