RDKit
FrameworkOpen-source projects built with RDKit
There are currently 30 open-source projects built with RDKit, with a combined total of 28.0k GitHub stars. The most common language among these projects is Python.
Showing 30 open-source projects
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 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 teaching platform providing interactive Jupyter Notebooks for learning computer-aided drug design (CADD) using open-source tools.
A benchmarking platform for molecular generation models, providing datasets, implementations, and evaluation metrics for drug discovery research.
A Python package for applying graph neural networks to molecular graphs and biological networks in life science research.
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.
A sequence-to-sequence transformer model for predicting chemical reaction pathways (retrosynthesis) with uncertainty calibration.
Official implementation of a 3D equivariant diffusion model for generating drug-like molecules that bind to specific protein targets and predicting their binding affinity.
GraphDTA predicts drug-target binding affinity using graph neural networks for drug discovery.
An unsupervised machine learning approach to learn vector representations of molecular substructures for cheminformatics.
A Python library for fast random access to chemical descriptors and molecule indices, optimized for machine learning workflows.
Standardizes and processes chemical molecule structures for the ChEMBL database using RDKit.
A Python wrapper for RDKit's RunReactants that improves stereochemistry handling in chemical reaction applications.
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 simple, open-source graphical molecule editor built with RDKit and PySide6 for chemical structure drawing and editing.
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.
PyTorch implementation of twin graph neural networks with similarity augmentation for drug response prediction using protein-protein associations.
A multi-channel neural network for predicting compound-protein interactions using molecular and protein sequence embeddings.
A GNN-based deep learning model that performs drug-specific gene selection for improved drug response prediction.
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