Cheminformatics
Showing 31 of 31 projects
An open-source Python library for applying deep learning to drug discovery, materials science, quantum chemistry, and biology.
A collection of cheminformatics and machine-learning software for molecular informatics, written in C++ with Python wrappers.
A curated list of awesome Python frameworks, libraries, software, and resources for chemistry and cheminformatics.
An open-source chemical toolbox for converting, analyzing, and manipulating molecular data across 90+ file formats.
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 curated list of awesome cheminformatics software, libraries, resources, and tools, primarily command-line based and open-source.
A Python package for applying graph neural networks to molecular graphs and biological networks in life science research.
An open-source Java library for cheminformatics and bioinformatics, providing algorithms for molecular representation, analysis, and data processing.
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 universal cheminformatics toolkit with database search engines, a core library, and utilities for molecular processing.
A reinforcement learning framework for de novo drug design that generates novel molecular structures with desired properties.
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 curated collection of papers, datasets, tools, and resources for applying machine learning to small-molecule drug discovery.
A Julia toolkit for graph-based molecule modeling, cheminformatics analysis, and chemical structure manipulation.
A Java library for converting IUPAC chemical names to molecular structures (SMILES, CML, InChI) with high accuracy.
A T5-based model for bidirectional translation between molecular structures (SMILES) and natural language descriptions.
A Python package for easy molecular docking with a curated dataset and benchmark tasks for drug discovery.
A Python wrapper for RDKit's RunReactants that improves stereochemistry handling in chemical reaction applications.
An R package to programmatically retrieve chemical information from various web databases and APIs.
A simple, open-source graphical molecule editor built with RDKit and PySide6 for chemical structure drawing and editing.
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 deep learning model using transformer architecture to predict compound-protein interactions from molecular and protein sequences.
An automated workflow for generating and storing DFT calculations for organic molecules.
A curated list of resources for molecular docking, protein-protein docking, and related computational biology tasks.
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