State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
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Updated
May 18, 2024 - Jupyter Notebook
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Message Passing Neural Networks for Molecule Property Prediction
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
A powerful and flexible machine learning platform for drug discovery
Therapeutics Commons: Artificial Intelligence Foundation for Therapeutic Science
Python package for graph neural networks in chemistry and biology
A deep learning framework for molecular docking
Deep Reinforcement Learning for de-novo Drug Design
Protein Graph Library
NequIP is a code for building E(3)-equivariant interatomic potentials
The Ersilia Model Hub, a repository of AI/ML models for infectious and neglected disease research.
Working with molecular structures in pandas DataFrames
EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
Protwis is the backbone of the GPCRdb. The GPCRdb contains reference data, interactive visualisation and experiment design tools for G protein-coupled receptors (GPCRs).
An open, extensible Python framework for GPU-accelerated alchemical free energy calculations.
Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.
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