/tools

Chemfiles is a high-quality library that facilitates reading and writing of trajectory files from computational chemistry simulations. It provides a unified interface for various file formats, enabling users to conduct post-processing analysis and extract physical information from molecular simulations.

MolVS is a Python-based tool that focuses on the validation and standardization of chemical structures. It utilizes the RDKit framework to enhance data quality by standardizing representations, helping with de-duplication, and identifying relationships between molecules.

This repository contains resources and code for teaching cheminformatics, covering topics such as molecular dynamics, machine learning applications, and various cheminformatics techniques. It serves as a comprehensive educational tool for students and educators in the field of computational chemistry.

The ReinventCommunity repository provides a collection of Jupyter notebook tutorials for using REINVENT 3.2, focusing on molecular design through reinforcement learning and QSAR modeling. It includes examples of data preparation, model building, and various reinforcement learning scenarios to generate and optimize novel compounds.

Global Chem is a public dictionary that organizes common chemical names and their molecular definitions into a knowledge graph. It serves as a resource for understanding chemical compounds used in various applications, including drugs and materials.

rdeditor is a simple RDKit molecule editor GUI that allows users to interactively edit molecular structures. It provides tools for adding and modifying atoms and bonds, making it useful for molecular design and visualization.

Pysmiles is a lightweight Python library designed for reading and writing SMILES strings, which are used to represent molecular structures. It allows users to create and manipulate molecular graphs, making it a useful tool in cheminformatics.

FPSim2 is a Python/C++ package that enables fast compound similarity searches using optimized algorithms. It is particularly useful in cheminformatics applications, allowing researchers to efficiently find similar molecules based on their chemical properties.

GNNs For Chemists provides implementations of various graph neural networks tailored for chemical problems. It serves as an educational resource, guiding users through the process of representing molecules as graphs and building models for predicting molecular properties.

This repository provides a collection of Jupyter Notebooks aimed at teaching life science students the fundamentals of deep learning, with a focus on applications in cheminformatics. It includes notebooks on generative models for molecular design and datasets relevant to molecular machine learning.

ChemPlot is a Python library that enables users to visualize the chemical space of molecular datasets in two dimensions. It employs various dimensionality reduction techniques and similarity algorithms to group similar molecules, facilitating the analysis of molecular properties.

DataWarrior is a software tool for interactive data analysis and visualization, particularly useful for cheminformaticians due to its native support for chemical structures and reactions. It provides rich functionality for analyzing molecular data and visualizing chemical information.

DeepSMILES is a Python module that converts standard SMILES notation to a modified version suitable for machine learning applications. It simplifies the representation of molecular structures, making it easier to work with in generative models and other computational chemistry tasks.

E3FP is a tool for generating extended 3-dimensional molecular fingerprints, which are useful for representing molecular structures in cheminformatics. It integrates with RDKit and can be applied in various molecular property prediction tasks.

SIRIUS is a software framework that facilitates the de-novo identification of metabolites using tandem mass spectrometry. It automates the analysis of mass spectra to deduce molecular formulas and structures, integrating various tools for enhanced metabolite identification.

The RDKit Python Wheels repository provides precompiled binaries for the RDKit cheminformatics toolkit, which is used for various molecular modeling tasks including property prediction, molecular simulations, and manipulation of chemical structures. It facilitates the installation of RDKit, enabling users to perform computational chemistry and molecular biology tasks efficiently.

CIRpy is a Python interface that simplifies interaction with the NCI Chemical Identifier Resolver (CIR). It allows users to resolve chemical identifiers, such as names to SMILES strings, enhancing the accessibility of chemical data.

DockStream is a docking wrapper that automates docking execution and post hoc analysis, providing access to various ligand embedders and docking backends. It is integrated with the REINVENT platform, allowing for the incorporation of docking into the molecular design process.

kGCN is a graph-based deep learning framework that focuses on the classification and prediction of molecular properties using graph convolutional networks. It supports the generation of molecular data and provides tools for dataset preparation and model training in cheminformatics.

The 'galaxytools' repository contains a collection of Galaxy Tool wrappers that facilitate the integration of cheminformatics and RNA bioinformatics tools into the Galaxy platform, enabling researchers to utilize these tools for molecular analysis and data processing.

AQME (Automated Quantum Mechanical Environments) offers workflows for generating molecular conformers and creating quantum mechanical input files. It also facilitates the post-processing of quantum mechanical output and the generation of molecular descriptors, making it a valuable tool for computational chemistry.

Auto-QChem is an automated workflow designed for the generation and storage of density functional theory (DFT) calculations specifically for organic molecules. It facilitates the prediction of molecular properties by streamlining the computational chemistry process.

BitBIRCH is an efficient clustering algorithm tailored for handling large molecular libraries, facilitating drug discovery and cheminformatics tasks. It allows researchers to perform similarity searches and analyze chemical space effectively.

Obsidian Chem is a plugin for Obsidian.md that allows users to render chemical structures from SMILES strings. It enhances note-taking in chemistry by enabling the visualization of molecular structures directly within notes.