/tools

Molecules_Dataset_Collection is a curated collection of datasets containing molecular structures and their associated physicochemical properties. It aims to facilitate the validation of machine learning models for predicting molecular properties, making it a valuable resource for researchers in computational chemistry and machine learning.

DEEPScreen is a virtual screening tool that utilizes deep convolutional neural networks to predict drug-target interactions based on 2-D structural representations of compounds. It is aimed at enhancing early-stage drug discovery by providing accurate predictions from compound images.

CDPKit is an open-source cheminformatics software toolkit designed for processing chemical data. It offers features for molecular representation, property prediction, pharmacophore generation, and integration with machine learning libraries, making it a valuable resource for computational drug discovery.

GPUSimilarity is a CUDA/Thrust implementation designed for efficient chemical fingerprint similarity searching using GPU acceleration. It integrates with RDKit for fingerprint generation and is intended for high-performance applications in cheminformatics.

sPyRMSD is a Python tool designed for symmetry-corrected RMSD calculations, which helps in comparing molecular structures. It can be used as a standalone tool or as a module, making it versatile for applications in drug discovery and computational biology.

SELFormer is a molecular representation learning tool that utilizes SELFIES language models to generate high-quality molecular embeddings. It is pre-trained on drug-like compounds and fine-tuned for various molecular property prediction tasks, making it a valuable resource for drug discovery and cheminformatics.

Psikit is a wrapper library for Psi4 and RDKit that facilitates quantum chemistry calculations, including the prediction of molecular properties such as HOMO and LUMO energies, structure optimization, and charge calculations. It is designed to assist in molecular design and cheminformatics tasks.

BitBIRCH-Lean is a memory-efficient implementation of the BitBIRCH clustering algorithm designed for high-throughput clustering of large molecular libraries. It allows users to generate molecular fingerprints from SMILES files and cluster them efficiently, making it a valuable tool for drug discovery and cheminformatics applications.

DECIMER-Image-to-SMILES is a tool that utilizes an encoder-decoder neural network to recognize chemical structures from images and convert them into SMILES notation. This process aids in molecular representation and can facilitate further analysis in cheminformatics.

VSFlow is an open-source tool for ligand-based virtual screening of large compound libraries. It supports various screening methods, including substructure, fingerprint, and shape-based approaches, and allows for database preparation and result visualization.

The ANI-1 dataset repository contains scripts for accessing a large dataset of 20 million calculated off-equilibrium conformations for organic molecules. This dataset is useful for training machine learning models in molecular property prediction and molecular simulations.

COCR is a tool designed to convert images of handwritten chemical structures into graphical representations of molecules. It utilizes Optical Character Recognition techniques to facilitate the recognition of chemical formulas, making it useful for cheminformatics applications.

The 'molecules.js' library is designed for chemical graph theory applications in JavaScript. It enables users to import molecules, compute graph matrices, and visualize molecular structures, making it a useful tool for cheminformatics and molecular property analysis.

USearchMolecules is a comprehensive dataset containing over 7 billion small molecules, designed for efficient searching and clustering of molecular structures. It utilizes various molecular fingerprints and is aimed at facilitating drug discovery and cheminformatics research.

This repository provides Jupyter notebooks for a workshop on Python for Cheminformatics-Driven Molecular Docking. It includes practical exercises on digital representations of molecules, exploring chemical data, and preparing structures for docking, making it a useful resource for those interested in molecular docking techniques.

LLaMP is a multimodal retrieval-augmented generation framework designed to enhance large language models with high-fidelity materials knowledge. It allows for dynamic interaction with materials databases, making it useful for applications in materials informatics and molecular property prediction.

The 'reaction_utils' repository offers utilities for handling datasets of chemical reactions, including template extraction and data manipulation. It is designed to facilitate the analysis and processing of chemical reaction data, making it a useful tool in the field of cheminformatics.

Chemcoord is a Python module designed for transforming molecular structures between Cartesian and internal coordinates, specifically Z-matrices. It includes features for handling complex chemical structures and is optimized for performance, making it useful in theoretical chemistry workflows.

ChEMBL Downloader is a tool designed to reproducibly download, open, parse, and query the ChEMBL database. It simplifies the process of accessing a wealth of molecular data, which can be utilized for property prediction and other cheminformatics tasks.

xyz2graph is a Python package that converts XYZ molecular files into interactive 3D visualizations and NetworkX graphs. It allows users to analyze molecular structures and visualize them effectively.

QSPRpred is an open-source software library designed for creating QSPR/QSAR models, enabling researchers to predict molecular properties and activities. It provides a modular interface for building models using various descriptors and machine learning algorithms, facilitating drug discovery and cheminformatics research.

Arpeggio is a tool designed to calculate and visualize interatomic interactions in molecular structures, particularly proteins. It utilizes the CREDO rules to analyze contacts and interactions, providing valuable insights for molecular biology and cheminformatics.

The fastapi-redis project showcases the integration of Redis with the FastAPI framework to create an API for analyzing chemical compounds using RDKit. It allows users to manage and compare molecular data, particularly focusing on compounds related to COVID-19.

PySMILESutils is a package designed for handling SMILES encodings of molecules, facilitating their use in deep learning applications with PyTorch. It includes features for data augmentation, dataset handling, and mini-batch creation, making it suitable for molecular machine learning tasks.