Drug Discovery
Showing 36 of 45 projects
A collection of 134 ready-to-use Agent Skills for scientific research, covering genomics, drug discovery, clinical analysis, and more.
AlphaFold 3 is an AI model that predicts the 3D structures of proteins and their interactions with other biomolecules like DNA, RNA, and ligands.
An open-source Python library for applying deep learning to drug discovery, materials science, quantum chemistry, and biology.
A family of open-source deep learning models for accurate biomolecular interaction and binding affinity prediction, rivaling AlphaFold3 and physics-based methods.
A machine learning package implementing message passing neural networks for predicting molecular and reaction properties.
A multi-modal foundation model for state-of-the-art molecular structure prediction of proteins, small molecules, DNA, RNA, and glycosylations.
A state-of-the-art diffusion model for predicting how small molecules (ligands) bind to proteins.
A deep learning library for drug-target interaction, drug property, protein-protein interaction, drug-drug interaction, and protein function prediction in bioinformatics.
A benchmarking platform for molecular generation models, providing datasets, implementations, and evaluation metrics for drug discovery research.
A curated collection of research papers on molecular and material design using generative AI and deep learning techniques.
A Python package for applying graph neural networks to molecular graphs and biological networks in life science research.
A PyTorch and TorchDrug based deep learning library for drug pair scoring, predicting interactions, side effects, and synergy.
A deep learning toolkit for computational chemistry and drug design research with PyTorch backend.
A deep learning library built on Chainer for molecular property prediction using graph convolutional neural networks.
A junction tree variational autoencoder for generating valid molecular graphs with desired chemical properties.
A Python library for molecular processing built on RDKit with a simple API and good defaults.
A Python library for molecular processing built on RDKit with a simple API and good defaults.
A Python package for benchmarking generative models in de novo molecular design.
Official implementation of a 3D equivariant diffusion model for generating drug-like molecules that bind to specific protein targets and predicting their binding affinity.
A curated collection of research papers tracking the frontier of AI-based protein design methods and applications.
GraphDTA predicts drug-target binding affinity using graph neural networks for drug discovery.
Deep learning model using convolutional neural networks to predict drug-target binding affinity from protein sequences and compound SMILES.
An unsupervised machine learning approach to learn vector representations of molecular substructures for cheminformatics.
A curated collection of papers, datasets, tools, and resources for applying machine learning to small-molecule drug discovery.
A transformer-based model for predicting drug-target interactions using substructural pattern mining and augmented transformer encoders.
A Julia toolkit for graph-based molecule modeling, cheminformatics analysis, and chemical structure manipulation.
A computational pipeline that predicts drug-target interactions by learning low-dimensional vector representations from heterogeneous biological networks.
A Python package for easy molecular docking with a curated dataset and benchmark tasks for drug discovery.
A Python machine learning and informatics suite for analyzing, mining, and modeling chemical and materials data.
A transformer-based model for unconditional and conditional molecular generation using GPT architecture trained on chemical datasets.
A Python script to filter chemical compounds using structural alerts from ChEMBL and property filters from RDKit.
A conversational AI framework for editing small molecules, peptides, and proteins using retrieval-augmented generation and domain feedback.
A deep learning model using transformer architecture to predict compound-protein interactions from molecular and protein sequences.
A deep bilinear attention network framework with adversarial domain adaptation for interpretable drug-target interaction prediction.
A Python toolkit for optimizing chemical reactions using machine learning strategies and benchmarks.
A curated collection of databases, software, and papers for computational biology research.
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