There are 9 repositories under rdkit topic.
Molecular Processing Made Easy.
Interaction Fingerprints for protein-ligand complexes and more
Plausibility checks for generated molecule poses.
Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.
Python for chemoinformatics
RadonPy is a Python library to automate physical property calculations for polymer informatics.
A powerful cheminformatics and molecule rendering toolbelt for JavaScript, powered by RDKit .
ScaffoldGraph is an open-source cheminformatics library, built using RDKit and NetworkX, for the generation and analysis of scaffold networks and scaffold trees.
⚛️ RDKit Python Wheels on PyPI. 💻 pip install rdkit
Python for chemoinformatics
Collection of data sets of molecules for a validation of properties inference
Showcase of Redis integration with Python FastAPI framework supported by Pydantic as API backend for RDKit: Open-Source Cheminformatics Software
This set of essential and valuable microservices is designed to be accessed via API calls to support cheminformatics.
Draw molecules with plotly!
eMolFrag is a molecular fragmentation tool based on BRICS algorithm written in Python.
A Docker-based, cloudable platform for the development of chemoinformatics-centric web applications and microservices.
Protein target prediction using random forests and reliability-density neighbourhood analysis
moldrug (AKA mouse) is a Python package for drug-oriented optimization on the chemical space
Lightweight RDKit images for production deployment
Containerised components for cheminformatics and computational chemistry
rdKit basics (provided jupyter notebooks are custom curated and will help the users to start working on rdKit)
Variational Autoencoder for Molecules
Reaction Data and Molecular Conformers (RDMC) is a package dealing with reactions, molecules, conformers, majorly in 3D.
pytoda - PaccMann PyTorch Dataset Classes. Read the docs: https://paccmann.github.io/paccmann_datasets/
MCP server that enables language models to interact with RDKit through natural language
The objective of this work is to develop machine learning (ML) methods that can accurately predict adverse drug reactions (ADRs) using the databases SIDER and OFFSIDES.