Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
- drug-discovery
- quantum-chemistry
- deep-learning
- biology
Repository Index
47,133 repositories indexed
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Official repository for the Boltz biomolecular interaction models
- binding-prediction
- drug-discovery
- protein-structure
POT : Python Optimal Transport
- optimal-transport
- numerical-optimization
- machine-learning
- emd
Message Passing Neural Networks for Molecule Property Prediction
- machine-learning
- chemistry
- neural-networks
- drug-discovery
Chai-1, SOTA model for biomolecular structure prediction
A research toolkit for particle swarm optimization in Python
- particle-swarm-optimization
- optimization-tools
- pso
- global-optimization
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
- machine-learning
- generative-models
- drug-discovery
- benchmark
Python package for graph neural networks in chemistry and biology
- deep-learning
- graph-neural-networks
- dgl
- cheminformatics
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
- deep-learning
- pytorch
- deep-chemistry
- graph-neural-network
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
Generation of protein sequences and evolutionary alignments via discrete diffusion models
- discrete-diffusion
- generative-model
- multiple-sequence-alignment
- protein-sequences
🎯 A comprehensive gradient-free optimization framework written in Python
- metaheuristics
- optimization
- optimization-algorithms
- python
Molecular Processing Made Easy.
- python
- molecule
- rdkit
- drug-discovery
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.
- quantum-mechanics
- materials-informatics
- data-science
- machine-learning
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
- self-attention
- compound-protein-interaction
- transformercpi
- structure-free-virtual-sreening
TypeDB Bio: Biomedical Knowledge Graph
- bioinformatics
- knowledge-graph
- knowledge-base
- drug-discovery
NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions
- computational-biology
- bioinformatics
- graph-convolution
- deep-learning
Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.
- alphafold3
- docking
- drug-design
- drug-discovery
Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).
- deep-reinforcement-learning
- generative-model
- vae
- molecule-generation