/tools

CGRtools is a Python library designed for the manipulation of molecules and reactions using the Condensed Graph of Reaction (CGR) approach. It supports various operations such as format conversion, molecule standardization, and subgraph searching.

LABODOCK is a collection of Jupyter Notebooks designed for molecular docking using Google Colab. It simplifies the docking process by automating pre- and post-docking tasks and integrates various cheminformatics tools for effective in-silico experimentation.

Indigo ELN is an open-source electronic lab notebook tailored for chemistry applications. It supports drug discovery processes and provides a platform for managing chemical data and experiments.

eMolFrag is a Python-based tool designed for molecular fragmentation using the BRICS algorithm. It aids in the decomposition of small molecules for fragment-based drug design, making it useful for predicting molecular properties and generating new molecular structures.

'shape-it' is a shape-based alignment tool that utilizes Open Babel and RDKit for molecular shape alignment. It is designed to assist in the analysis and comparison of molecular structures, which is essential in fields like drug discovery and molecular modeling.

Chembience is a Docker-based platform designed for the rapid development of cheminformatics-centric web applications and microservices. It leverages RDKit for molecular data processing and supports the creation of applications that can predict molecular properties and handle various molecule types.

SynPlanner is an open-source tool for computer-aided synthesis planning that enhances the flexibility of developing customized retrosynthetic solutions. It integrates advanced machine learning techniques to evaluate reaction rules and synthesizability of intermediate products, facilitating effective retrosynthetic planning.

Scopy is an integrated Python library that facilitates the screening of undesirable compounds in drug discovery. It includes modules for data preparation, screening filters, and the calculation of molecular properties, making it a valuable tool for designing high-quality chemical libraries.

The Pharmacophore-Toolkit allows users to generate simple pharmacophore models using RDKit, which can be derived from crystal structures, docking poses, or SMILES strings. It provides functionalities for visualizing these models in both 2D and 3D formats, aiding in the understanding of molecular interactions.

MolecuLarva is a molecular simulation tool that visualizes the behavior of particles in 2D and 3D space, simulating interactions such as collisions and forces between different particle types. It aims to study self-organizing systems and explore conditions for the emergence of artificial life.

PIDGINv3 is a tool for predicting protein targets using random forests trained on bioactivity data. It employs molecular descriptors and provides models for various activity cut-offs, making it useful for drug discovery and cheminformatics applications.

The UGM 2016 repository provides educational materials from the RDKit User Group Meeting, featuring notebooks and presentations on various topics in cheminformatics, including molecular simulations and property prediction techniques. It serves as a resource for learning and applying RDKit tools in computational chemistry.

moldrug is a Python package that focuses on drug-oriented optimization within the chemical space. It employs a Genetic Algorithm as a search engine and integrates with the CReM library for generating chemical structures.

The RDKit Tutorials repository offers a collection of Jupyter notebooks that guide users through various cheminformatics tasks using RDKit. It covers fundamental and advanced topics such as property calculations, QSAR modeling, and molecular structure manipulation, making it a valuable resource for those interested in molecular design and analysis.

PyMOL Remote is a Python package that allows users to send commands and data to and from PyMOL, a molecular visualization tool. It enables remote procedure calls between a client and a PyMOL server, facilitating molecular design and analysis workflows.

This repository contains modular components designed for cheminformatics and computational chemistry, allowing users to create data processing pipelines. It leverages RDKit for various cheminformatics functionalities, making it suitable for tasks related to molecular property prediction and simulations.

This repository contains Dockerfiles for building lightweight RDKit images, which are used for cheminformatics tasks such as molecular property prediction and manipulation. It facilitates the use of RDKit in cloud environments, making it easier to deploy molecular modeling applications.

ChemIQ is a benchmark for evaluating the chemical intelligence of large language models by testing their ability to interpret molecular structures and perform chemical reasoning tasks. It includes a variety of questions related to molecular properties and transformations, making it relevant to the field of computational chemistry.

This repository contains algorithms for automatically fragmenting molecules into specified molecular subunits, such as functional groups. It aims to enhance the understanding and manipulation of molecular structures in computational chemistry.

The GenUI framework offers a backend for molecular generation and QSAR modeling through a REST API, enabling users to manage datasets and visualize chemical spaces. It supports the integration of various molecular generators and facilitates the upload and download of bioactivity datasets.

MolFrames is a Python module designed for handling chemical structures in Pandas DataFrames, allowing for efficient operations on large molecular datasets. It includes a pipelining workflow for processing molecular data and integrates with RDKit for chemical functionality.

The 'rxn-ebm' repository implements energy-based modeling to enhance the performance of retrosynthesis models by re-ranking reactant predictions. It leverages machine learning to analyze reaction databases and improve synthesis planning for organic chemistry.

Maxsmi is a tool designed for data augmentation in molecular property prediction using deep learning techniques. It provides various strategies for augmenting SMILES representations to enhance the accuracy of predictions for molecular properties and activities.

UVVisML is a tool designed to predict the optical properties of molecules using machine learning techniques. It provides functionalities for making predictions based on experimental and theoretical data, specifically targeting absorption peaks and excitation energies.