A common data access layer for AI-driven drug discovery.
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Updated
May 19, 2024 - Python
A common data access layer for AI-driven drug discovery.
Protein Graph Library
Toolkit for open antiviral drug discovery by the ASAP Discovery Consortium
Design of target-focused libraries by probing continuous fingerprint space with recurrent neural networks. The repository accompanies a research paper which is currently under review (08.04.24)
Reinforcement learning environments for drug discovery
Therapeutics Commons: Artificial Intelligence Foundation for Therapeutic Science
Discovering telomerase inhibitors with machine learning.
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
rDock is a fast and versatile Open Source docking program that can be used to dock small molecules against proteins and nucleic acids. It is designed for High Throughput Virtual Screening (HTVS) campaigns and Binding Mode prediction studies.
An interoperable Python framework for biomolecular simulation.
Open Targets python framework for post-GWAS analysis
A powerful abstraction of gene databases
The Website of the Ramiréz Lab
Message Passing Neural Networks for Molecule Property Prediction
A modular inverse QSAR pipeline
DrugGPT: A GPT-based Strategy for Designing Potential Ligands Targeting Specific Proteins
This repository is the collection point of reference data for the GPCRdb. The GPCRdb contains reference data, interactive visualisation and experiment design tools for G protein-coupled receptors (GPCRs).
A deep learning framework for molecular docking
Merging, linking and placing compounds by stitching bound compounds together like a reanimated corpse
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