/tools

CheTo is a tool for Chemical Topic Modeling that utilizes topic modeling techniques from text mining to analyze chemical data. It provides Jupyter notebooks and datasets for exploring molecular datasets, making it useful for researchers in cheminformatics.

Molecular_VAE_Pytorch is a PyTorch implementation of a Variational Autoencoder designed for automatic chemical design. It utilizes a continuous representation of molecules to generate new molecular structures based on the ChEMBL dataset.

QM9nano4USTC is a repository that introduces the QM9 dataset, which contains information on 130,462 organic molecules and their properties. It includes preprocessed features for molecular property prediction, making it useful for data-driven experiments in computational chemistry.

Trident Chemwidgets is a set of Jupyter widgets that enhance data visibility in cheminformatics and molecular machine learning. It allows users to interact with molecular datasets through various visualization tools, including histograms and scatter plots, as well as an interactive molecule viewer.

ChemInformatics Model Explorer (CIME) is a web application that enables users to explore chemical compounds through interactive visualizations. It supports the analysis of molecular properties and allows users to upload and visualize datasets, making it a valuable tool for cheminformatics.

ChemInformant is a Python library that provides an all-in-one solution for retrieving chemical properties from the PubChem database. It supports batch processing and offers analysis-ready outputs, making it suitable for various applications in drug discovery and cheminformatics.

Pytoda is a Python package that simplifies the handling of biochemical data for deep learning applications using PyTorch. It is particularly useful for researchers working on molecular design and related tasks in computational chemistry.

The RDKit-Neo4j project provides an extension for the Neo4j graph database that allows for efficient querying of chemical structures and properties. It supports exact, substructure, and similarity searches, making it a valuable tool for managing and analyzing molecular data.

The RDKit MCP Server allows language models to interact with RDKit through natural language, providing agent-level access to its functions. It facilitates molecular property prediction and manipulation, making it a valuable tool for drug discovery and cheminformatics applications.

DockStreamCommunity is a repository that offers Jupyter Notebook tutorials for molecular docking and generative design using reinforcement learning. It supports various docking backends and ligand embedders, making it a useful resource for researchers in molecular design and drug discovery.

RDMC is a software package designed for handling reaction data and molecular conformers, primarily in 3D. It offers functionalities for generating resonance structures, visualizing conformers, and performing bond analysis, making it a valuable tool for molecular modeling and simulations.

The 'route-distances' repository contains tools for calculating distances between synthesis routes and clustering them, primarily aimed at developers and researchers in cheminformatics. It also incorporates a machine learning model for fast predictions of distances between synthetic routes, enhancing its utility in molecular design and retrosynthesis.

This repository serves as the source of truth for drugdesign.org, providing tools and resources for drug discovery, including molecular dynamics and modeling. It supports various aspects of cheminformatics and drug design, making it a valuable resource for researchers in the field.

The knime-rdkit repository contains nodes that integrate RDKit functionalities into the KNIME Analytics Platform, facilitating various cheminformatics tasks. It supports molecular property predictions and data manipulation, which are essential for drug discovery and molecular design.

EdgeSHAPer is a bond-centric explanation method for graph neural networks that utilizes Shapley values to determine edge importance. It is applicable in medicinal chemistry for tasks such as predicting molecular properties and understanding model predictions related to small molecules and proteins.

The MongoDB Chemistry repository provides an implementation for chemical similarity searches using MongoDB, along with performance analysis. It allows users to load molecular data and perform similarity searches, which is useful in various applications within computational chemistry.

Neuraldecipher is a tool that implements a method for reverse-engineering extended-connectivity fingerprints (ECFPs) back to their corresponding molecular structures. It utilizes deep learning techniques to facilitate the generation and analysis of molecular representations, making it useful for cheminformatics applications.

IDSL_MINT is a deep learning framework designed to interpret raw mass spectrometry data, enabling the prediction of molecular fingerprints and structures from MS/MS spectra. It utilizes transformer models to translate mass spectra into molecular descriptors and canonical SMILES, making it a valuable tool for cheminformatics and molecular property prediction.

cuik-molmaker is a specialized package for molecular featurization that transforms chemical structures into formats compatible with deep learning models, particularly graph neural networks. It combines C++ and Python for efficient processing and is designed to facilitate the training and inference workflows in molecular machine learning.

The Chemical Graph Series is an educational resource that teaches users how to represent molecules as graphs and apply deep learning techniques, specifically graph neural networks, to predict chemical properties. It covers foundational concepts in cheminformatics and progresses to advanced modeling techniques, including property prediction using real datasets.

AI4PFAS is a repository that provides a dataset and code for predicting the toxicity of PFAS compounds using uncertainty-informed deep transfer learning. It includes various models and benchmarks for assessing toxicity, specifically focusing on LD50 values.

AutomatedSeriesClassification is a tool designed for the automated classification of chemical series, leveraging datasets like ChEMBL. It prepares data for analysis and can be used to classify compounds similarly to how medicinal chemists would.

Chem-Faiss is a tool that utilizes vector similarity search from Faiss combined with chemical fingerprinting to create a scalable architecture for searching compounds. It is particularly useful for drug design and finding structural matches within large datasets.

The Computer_aided_drug_discovery_kit is a pipeline designed for virtual screening of pharmaceutical compounds using similarity-based and structure-based techniques. It includes modules for data extraction, descriptor calculation, and machine learning classification to predict the bioactivity of compounds.