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
Official repository for the Boltz biomolecular interaction models
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
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
Molecular Processing Made Easy.
A Euclidean diffusion model for structure-based drug design.
bert-loves-chemistry: a repository of HuggingFace models applied on chemical SMILES data for drug design, chemical modelling, etc.
Universal cheminformatics toolkit, utilities and database search tools
Deep Reinforcement Learning for de-novo Drug Design
The official implementation of 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction (ICLR 2023)
GraphDTA: Predicting drug-target binding affinity with graph neural networks
Source code for "DeepDTA: deep drug-target binding affinity prediction"
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
A Network Integration Approach for Drug-Target Interaction Prediction
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.
LLM for Drug Editing, ICLR 2024
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524
Optimising chemical reactions using machine learning