/tools

The ppqm package facilitates the integration of RDKit with various quantum chemistry software, allowing users to perform molecular property calculations and optimizations. It serves as a bridge for cheminformatics applications, enabling efficient quantum chemistry computations in Python.

The ChEMBL MCP Server is a comprehensive tool that facilitates drug discovery and cheminformatics analysis by providing access to the ChEMBL chemical database. It includes features for searching compounds, analyzing bioactivity data, and predicting molecular properties such as ADMET and solubility.

BALL is a Biochemical Algorithms Library that provides tools for molecular simulations, cheminformatics, and drug design. It supports various biochemical computations and is useful for researchers in computational biology and chemistry.

PCMol is a multi-target de novo molecular generator that utilizes AlphaFold's protein embeddings to create relevant molecules for various protein targets. It is designed to aid in drug discovery by generating SMILES representations of potential drug candidates.

MOFid is a system designed for the rapid identification and analysis of metal-organic frameworks. It provides tools for researchers to analyze these materials, which are significant in various applications including catalysis and gas storage.

This repository provides a PyTorch implementation of a variational autoencoder designed for automatic chemical design. It focuses on generating continuous representations of molecules, which can be utilized for drug discovery and molecular optimization.

PatentChem is a tool that downloads USPTO patents and extracts molecules related to specified keyword queries. It processes patent claims to find and output SMILES strings of relevant molecules, making it useful for cheminformatics and molecular data extraction.

This repository serves as a companion to the book 'Machine Learning for Drug Discovery', providing code and data for various machine learning techniques applied to drug discovery. It covers topics such as ligand-based screening, generative models for de novo design, and structure-based drug design, making it a valuable resource for researchers in the field.

The Cheminformatics Microservice offers essential microservices for cheminformatics, including molecular property calculations, structure validation, and visualization through an API. It supports various chemical formats and provides tools for chemical analysis and natural product scoring.

The geo-gcn repository provides an implementation of Spatial Graph Convolutional Networks, which are used to learn from graph-structured data, particularly in the context of chemical compounds. It supports training on chemical datasets for tasks such as predicting molecular properties.

The CADD Vault is an open-source repository that offers a comprehensive collection of resources and tools for computer-aided drug design. It includes materials on virtual screening, molecular dynamics simulations, and machine learning applications, making it a valuable resource for researchers in the field.

MolML is a Python library designed to interface molecules with machine learning by converting molecular structures into vector representations. It supports various molecular descriptors and is aimed at facilitating the application of machine learning techniques in predicting molecular properties.

ChemicaLite is an SQLite database extension designed for cheminformatics applications. It allows users to store molecules and fingerprints, compute chemical descriptors, and perform chemical queries on a database.

Molgraph is a Python package that implements graph neural networks using TensorFlow and Keras, aimed at molecular machine learning. It provides tools for building models that can predict molecular properties and analyze molecular graphs.

iSIM is a module designed to perform simultaneous comparisons of multiple molecules, providing an efficient method to calculate average pairwise similarities. It utilizes binary fingerprints and real number descriptors, making it applicable in chemical sampling, clustering, and visualization tasks.

The PDN repository provides a PyTorch implementation of Pathfinder Discovery Networks, a method for learning message passing graphs. It is designed for tasks such as node classification, which can be applied to molecular data and analysis.

SIRIUS is a framework designed for metabolomics mass spectrometry, enabling the identification of molecular formulas for small molecules. It includes various modules for isotope pattern analysis, fragmentation tree computation, and compound class prediction.

Chemkit is an open source C++ library that facilitates molecular modeling and cheminformatics, providing tools for molecular visualization. It aims to support various applications in the field of chemistry through its comprehensive library functionalities.

This tool utilizes neural machine translation techniques to predict the outcomes of organic chemistry reactions based on known reactants. It employs an attention mechanism to enhance the prediction accuracy and provides a graphical user interface for user interaction.

Chemistry Drawer is a Python package that allows users to draw and visualize molecular structures using Plotly. It provides customizable aesthetics for atoms, bonds, and rings, making it a useful tool for representing chemical structures.

GLaDOS is a web interface for ChEMBL, a comprehensive database that provides information on drug discovery, including molecular structures and drug-target interactions. It facilitates access to cheminformatics data, supporting research in molecular biology and chemistry.

Lemon is a framework that allows users to rapidly mine structural information from the Protein Data Bank. It enables the creation of standardized workflows for querying 3D features of macromolecules, enhancing the efficiency of structural biology research.

Novana is a cheminformatics tool that allows for the decomposition of molecules into their scaffolds and shapes, enhancing the analysis of molecular datasets. It can be used for clustering and creating training/validation sets for machine learning, making it a valuable resource in molecular property prediction and design.

ROBERT is an automated machine learning tool that processes CSV databases of molecular descriptors or SMILES to generate publication-quality results in chemistry. It streamlines the workflow for predicting molecular properties and enhances reproducibility in computational chemistry studies.