/tools

The smi2sdf3d repository provides a method for generating diverse 3D conformers of molecules using the RDKit library. This tool is useful for researchers in computational chemistry and drug discovery, facilitating the exploration of molecular conformations.

The mofstructure package is designed for the manipulation of metal-organic frameworks (MOFs) and other porous materials. It allows users to compute geometric properties, remove unbound guest molecules, and deconstruct MOFs into building units while providing cheminformatics data such as SMILES and InChI identifiers.

The 'molgrad' repository contains code for using explainable AI to assess molecular properties relevant to drug discovery. It includes pre-trained models for predicting plasma protein binding and other properties, as well as functionality for training custom models and generating explanations for molecular features.

GenUI is a frontend application that offers a graphical user interface for interacting with the GenUI REST API web services. It is designed to facilitate molecular generation and cheminformatics tasks through a user-friendly dashboard.

ChemicalIdentifiers.jl is a Julia package designed for searching chemical identifiers such as CAS, PubChemID, SMILES, and InChI from a text query. It facilitates access to a database of over 70,000 compounds, making it useful for researchers in the field of chemistry.

This repository contains code for automatically extracting a dataset of interacting compound-target pairs from the ChEMBL database. It enables researchers to analyze drug-target interactions and supports future analyses in drug discovery.

The rdkit-sys repository offers a Rust interface to the RDKit C++ API, facilitating the development of applications in cheminformatics and molecular modeling. It allows users to leverage RDKit's capabilities for various molecular tasks, including property prediction and molecular simulations.

ChemXploreML is a desktop application that utilizes machine learning to predict various molecular properties of organic compounds. It supports advanced molecular representation techniques and automates data preprocessing, making it accessible for chemists to explore and analyze molecular data.

The homebrew-cheminformatics repository offers a collection of cheminformatics formulae for the Homebrew package manager, allowing users to easily install various molecular modeling tools like RDKit and Open Babel. These tools are essential for tasks such as predicting molecular properties and performing simulations.

PySmash is a Python package designed for the automatic generation and application of structural alerts in molecular screening. It provides algorithms for deriving representative substructures, which can be used to evaluate molecular potency and ADMET properties in drug discovery.

This repository offers a Docker image for RDKit, a collection of cheminformatics and machine-learning software. It facilitates the installation and use of RDKit for various molecular modeling and analysis tasks.

MolEnc is a molecular encoder that uses RDKit and OCaml to create chemical fingerprints, specifically the Signature Molecular Descriptor. It allows for encoding databases of molecules and can reuse feature dictionaries, making it useful for various molecular property prediction and design tasks.

The RDKit Blog repository appears to be a collection of Jupyter Notebooks focused on cheminformatics and computational chemistry topics. It likely includes tutorials and examples related to molecular analysis and property prediction using the RDKit toolkit.

This repository provides a Docker image that integrates the RDKit cartridge with PostgreSQL, enabling users to perform cheminformatics tasks and molecular property calculations within a database environment.

This repository offers a Docker image that integrates PostgreSQL with the RDKit cartridge, enabling users to perform cheminformatics operations within a database environment. It facilitates the storage and querying of molecular data, making it useful for various molecular property predictions and analyses.

NonadditivityAnalysis is a tool designed to analyze SAR datasets by identifying complex patterns and computing matched pairs from molecular structures. It processes input data to derive insights about nonadditivity in molecular interactions, making it useful for researchers in computational chemistry and drug discovery.

The SMARTS-RX project provides tools for generating and managing molecular representations using SMARTS notation. It facilitates the classification and identification of various molecular types, making it useful for cheminformatics applications.

The ChEMBL Multitask Neural Network model is a fast and efficient tool for predicting molecular targets based on a variety of bioactivity data. It supports off-target predictions and is implemented in multiple programming languages, making it versatile for different computational environments.

HyFactor is an open-source architecture designed for generating molecular structures using a novel Hydrogen-count Labelled Graph approach. It includes implementations for both HyFactor and ReFactor architectures, facilitating the translation between molecular graphs and their representations.

The 'mol-ellipsize' tool calculates the size of molecules by fitting an ellipsoid around their van der Waals cloud based on multiple conformers. It utilizes RDKit for generating conformer ensembles and provides functionality for molecular size analysis, making it useful in computational chemistry applications.

Mongo-rdkit is a project that integrates RDKit, a cheminformatics toolkit, with MongoDB to facilitate the creation and manipulation of a chemically-intelligent database. It provides methods for performing high-performance searches, including similarity and substructure searches, making it useful for molecular property analysis.

This repository implements algorithms for link prediction in a Chemical Reaction Knowledge Graph, enabling the prediction of novel reactions and products. It utilizes models like SEAL and Graph Auto-Encoder to facilitate the design and generation of chemical compounds.

GlyLES is a Python tool that converts IUPAC representations of glycans into SMILES strings, facilitating the representation and analysis of glycan structures in computational chemistry. It supports various input formats and is currently in development to enhance its functionality.

The RXN reaction preprocessing repository provides tools for preprocessing datasets of chemical reactions, including standardization, filtering, and data augmentation. It facilitates the creation of flexible data pipelines for chemical reaction data, which is crucial for various molecular applications.