/tools

Claude Scientific Skills is a collection of 140 ready-to-use scientific skills that enable users to perform complex workflows in drug discovery and cheminformatics. It includes functionalities for molecular property prediction, virtual screening, and molecular docking, making it a valuable resource for researchers in computational chemistry and molecular biology.

RDKit is a comprehensive cheminformatics library that provides tools for molecular property prediction, descriptor generation, and various molecular operations. It supports both small molecules and proteins, making it a versatile tool for researchers in computational chemistry and molecular biology.

Open Babel is a versatile chemical toolbox designed for handling chemical data across multiple formats. It allows users to search, convert, analyze, and store molecular data, supporting a wide range of applications in chemistry and molecular modeling.

This repository provides a collection of Jupyter notebooks designed to teach practical cheminformatics using open-source software. It covers various topics including molecular property prediction, generative molecular design, and machine learning models applicable to cheminformatics workflows.

SciPipe is a library for creating robust and flexible scientific workflows using the Go programming language. It is particularly suited for bioinformatics and cheminformatics applications, allowing users to design and execute pipelines that can process molecular data and integrate various command-line tools.

TeachOpenCADD is a teaching platform designed to educate users on computer-aided drug design (CADD) through interactive Jupyter Notebooks. It covers various topics in cheminformatics and structural bioinformatics, providing practical examples and resources for students and researchers in the field.

DGL-LifeSci is a Python package designed for applying graph neural networks in chemistry and biology. It includes functionalities for molecular property prediction, reaction prediction, and various modeling tasks relevant to drug discovery.

AiZynthFinder is a Python tool that utilizes Monte Carlo tree search and neural networks for retrosynthetic planning, allowing users to break down complex molecules into purchasable precursors. It supports customizable search algorithms and is aimed at facilitating the design and generation of molecules.

Thermo is an open-source software library designed for calculating thermodynamic and transport properties of chemicals and their mixtures. It facilitates the retrieval of chemical constants and the calculation of properties such as viscosity, density, and solubility, making it useful for molecular simulations and chemical engineering applications.

Ketcher is an open-source web-based chemical structure editor designed for chemists and laboratory scientists. It provides features for drawing, editing, and visualizing molecular structures in various formats, supporting both small molecules and macromolecules.

Avogadro libraries are designed for advanced molecular editing and visualization, supporting computational chemistry and molecular modeling. They offer a flexible plugin architecture and are suitable for a wide range of applications in bioinformatics and materials science.

The Chemistry Development Kit (CDK) is an open-source Java library designed for cheminformatics and bioinformatics applications. It offers functionalities for molecule representation, file format handling, and various algorithms for molecular processing and property calculations.

Datamol is a Python library that simplifies molecular processing by providing a user-friendly API built on RDKit. It allows users to manipulate molecular structures, generate conformers, and visualize molecules, making it a valuable tool for cheminformatics and drug discovery.

PubChemPy provides a convenient interface for accessing and interacting with chemical data from the PubChem database. It allows users to perform searches, retrieve molecular properties, and convert chemical formats, making it a valuable tool for researchers in chemistry and related fields.

ProLIF (Protein-Ligand Interaction Fingerprints) is a tool that generates interaction fingerprints for complexes involving ligands, proteins, DNA, or RNA. It utilizes data from molecular dynamics trajectories and docking simulations, making it valuable for drug discovery and cheminformatics.

The Open Drug Discovery Toolkit (ODDT) is a modular toolkit for cheminformatics and molecular modeling, enabling users to perform tasks such as scoring, docking, and screening of drug candidates. It is built in Python and leverages libraries like RDKit and OpenBabel for enhanced molecular analysis.

Mordred is a molecular descriptor calculator that computes a wide range of molecular descriptors for both 2D and 3D molecular structures. It is designed to assist in the analysis and prediction of molecular properties, which is crucial in fields like drug discovery and cheminformatics.

The ChEMBL webresource client is an official Python library that allows users to access ChEMBL's extensive database of bioactive molecules and their properties. It simplifies the process of retrieving molecular data through a user-friendly interface, making it a valuable resource for cheminformatics and molecular property analysis.

asciiMol is a terminal-based ASCII molecule viewer that allows users to visualize molecular structures using the ncurses library. It supports various file formats and provides features like navigation, zooming, and bond detection.

Indigo is a universal cheminformatics toolkit that includes a variety of utilities for chemistry search, molecule visualization, and SMILES generation. It supports multiple programming languages and is designed for applications in drug discovery and molecular analysis.

ReLeaSE is a tool that utilizes deep reinforcement learning to facilitate the de-novo design of drug molecules. It includes functionalities for optimizing molecular properties and generating new molecular structures based on learned patterns.

REINVENT is a tool designed for the de novo design of molecules using reinforcement learning techniques. It provides a framework for generating molecular structures and optimizing their properties, making it a valuable resource in the field of drug discovery.

BoFire is a Bayesian optimization framework designed for real experiments, particularly in the chemical and pharmaceutical industries. It supports optimization of molecular properties and experimental designs, utilizing advanced techniques like multi-objective optimization and active learning.

RXNMapper is a tool that enables robust atom mapping on valid reaction SMILES using an unsupervised attention-guided approach. It utilizes a trained ALBERT model to extract atom mapping information from a large dataset of chemical reactions, facilitating the analysis and understanding of organic chemistry grammar.