/tools

LSTM_Chem is a tool that implements generative recurrent networks for de novo drug design, allowing users to generate new molecular structures based on learned patterns from existing data. It utilizes SMILES representations for molecules and is built using TensorFlow and Keras.

SynFlowNet is a GFlowNet model that generates molecules based on chemical reactions and available building blocks, allowing for the design of diverse and novel molecules while considering synthesis constraints. It includes functionalities for training the model and sampling molecules guided by user-defined rewards.

Vina-GPU 2.0 is a software tool that accelerates AutoDock Vina and its derivatives for molecular docking using GPU technology. It supports various docking methods and provides a graphical user interface for ease of use in virtual screening applications.

ResGen is a tool for generating 3D molecular structures that are aware of their binding pockets, utilizing parallel multi-scale modeling. It is designed for molecular generation tasks, particularly in the context of drug discovery and protein-ligand interactions.

This repository provides a graph neural network implementation for predicting molecular properties based on 3D structures. It allows users to preprocess datasets and train models to predict various molecular properties using a subset of the QM9 dataset.

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 '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.

Uni-pKa is a machine learning-based tool designed for accurate pKa prediction of small molecules. It includes a microstate enumerator for building protonation ensembles and a model that integrates thermodynamic principles to enhance prediction accuracy.

DrugGEN is a generative system designed for the de novo creation of drug candidate molecules that are tailored to interact with specific protein targets. It utilizes graph transformer-based generative adversarial networks to generate and evaluate potential drug candidates.

Pyscreener is a Python library designed for conducting high-throughput virtual screening (HTVS) of ligands against molecular targets. It supports various docking software and allows users to efficiently evaluate ligand-receptor interactions through a streamlined interface.

SECSE is a platform for systemic evolutionary chemical space exploration aimed at drug discovery. It utilizes deep learning and fragment-based design to generate novel small molecules, enhancing the hit-finding process in drug development.

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.

This repository provides a benchmark dataset of proteins and ligands for validating binding free energy prediction methods. It includes input structures for calculations, experimental binding data for reproducibility studies, and analysis scripts for generating results.

PyAutoFEP is an automated workflow for Free Energy Perturbation (FEP) calculations using GROMACS, aimed at estimating the Relative Free Energies of Binding (RFEB) of small molecules to macromolecular targets. It integrates enhanced sampling methods and provides automation for various steps in the molecular dynamics setup and analysis.

VortexDock is a Python-based distributed molecular docking system designed to improve the efficiency of virtual screening by utilizing parallel computation across multiple nodes. It supports AutoDock Vina and allows for task management and monitoring of docking processes.

The NVIDIA BioNeMo blueprint provides a framework for generative virtual screening in drug discovery, utilizing advanced AI models to design and optimize small molecules and predict protein-ligand interactions. It integrates various tools for protein structure prediction and molecular generation, facilitating efficient drug discovery workflows.

LigParGen is an automatic parameter generator for small organic molecules using OPLS-AA force fields. It accepts multiple molecular formats and produces output files compatible with various molecular dynamics software, facilitating molecular simulations and parameterization.

DockingPie is a consensus docking plugin for PyMOL that provides a user-friendly interface for conducting molecular docking analyses. It integrates several docking programs, allowing users to perform consensus docking and scoring analyses effectively.

DFT_PIB_Code is a collection of interactive Jupyter Notebooks designed to teach the fundamentals of Density-Functional Theory (DFT) through visualizations and practical examples. It allows users to explore molecular properties and behaviors using DFT, making it a valuable educational resource in computational chemistry.

BioSimSpace is a Python framework that facilitates biomolecular simulations and workflows, allowing users to create robust components that can operate across different software and hardware environments. It supports real-time interaction with molecular simulation processes, making it a valuable resource for computational chemistry and molecular biology applications.

Particle Guidance is a tool that enhances the diversity and efficiency of sampling in generative models, specifically applied to molecular conformer generation. It reduces the median error in conformer generation, making it a valuable resource for molecular design and optimization.

PXMeter is a toolkit for assessing the structural quality of biomolecular predictions, including proteins and small molecules. It provides multi-metric evaluations and supports both command line and Python API interfaces for efficient analysis.

The smina-docking-benchmark repository provides tools for evaluating molecular docking models and optimizing generated molecules. It includes benchmarks for various models and allows users to generate and assess molecules based on their docking scores.