/tools

GeoDock is a novel tool that utilizes a multi-track iterative transformer network to improve protein-protein docking by allowing flexibility at the protein residue level. It aims to enhance the accuracy and speed of predicting docked structures, facilitating large-scale structure screening in molecular biology.

This repository provides trained caffe models for predicting molecular properties related to affinity and pose, specifically designed for use with GNINA. It includes various models and datasets used for training and evaluating these predictions, making it a valuable resource for molecular simulations and drug discovery efforts.

PandaDock is a next-generation molecular docking suite that combines advanced algorithms and GPU acceleration to achieve high-accuracy predictions of protein-ligand interactions. It is designed for applications in drug discovery and computational biology, offering various docking modes and scoring functions.

DisCo-DiffDock is a tool designed for molecular docking experiments, enhancing continuous diffusion models with discrete latents. It provides code and methods for evaluating molecular interactions, specifically targeting protein complexes.

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.

This repository provides educational materials for a course on Structural Bioinformatics, including practical exercises on molecular dynamics simulations, protein structure prediction, and molecular docking. It serves as a resource for understanding and applying computational methods in molecular biology and bioinformatics.

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.

AlphaRED is a pipeline designed for predicting protein complex structures from sequences, utilizing AlphaFold for structure prediction and ReplicaDock for protein-protein docking. It provides a comprehensive approach to analyze and refine docking results based on predicted binding modes.

OnionNet is a deep learning model designed to predict protein-ligand binding affinities based on inter-molecular contact features. It can also be used for re-scoring docking results, making it a valuable tool in drug discovery and molecular design.

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.

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.

Boltzina is a pipeline that integrates AutoDock Vina docking with Boltz-2 scoring to efficiently predict binding affinities for molecular docking. It supports both full docking and scoring-only modes, making it a valuable tool for virtual screening in drug discovery.

This tool implements a reinforced genetic algorithm for structure-based drug design, utilizing neural models to enhance the efficiency of molecular optimization. It aims to intelligently explore chemical space to identify potential drug candidates with improved binding affinity.

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.

gnina-torch is a PyTorch implementation of the GNINA scoring function, designed for molecular docking applications. It allows users to train and utilize deep learning models to predict protein-ligand interactions, enhancing the drug discovery process.

This repository provides notebooks and an environment for a workshop on Python scripting for molecular docking. It includes resources for preparing and executing docking simulations, specifically targeting small molecules and proteins.

PAMNet is a universal framework designed for accurate and efficient geometric deep learning of molecular systems. It excels in predicting molecular properties, such as binding affinities and RNA 3D structures, and utilizes graph neural networks to enhance performance in these tasks.

RxDock is an open-source docking program that facilitates the docking of small molecules to proteins and nucleic acids. It is optimized for high-throughput virtual screening and binding mode prediction, allowing researchers to efficiently explore ligand interactions.

Watvina is a docking tool that facilitates drug design by supporting both explicit and implicit water models in protein-ligand interactions. It allows for pharmacophore and position-constrained docking, optimizing ligand interactions with receptors using the Autodock Vina engine.

PocketVina is a GPU-accelerated software for protein-ligand docking that automates the detection of binding pockets and evaluates interactions between proteins and ligands. It aims to enhance the accuracy and efficiency of docking processes in drug discovery.

rDock is a fast and versatile open-source docking program that facilitates the docking of small molecules to proteins and nucleic acids. It is particularly useful for high-throughput virtual screening campaigns and binding mode prediction studies.

DeepChem GUI is a web-based interface that allows users to predict the docking of ligands to proteins using pretrained DeepChem models. It supports molecular visualization and editing, making it a useful tool for researchers in computational chemistry and drug discovery.

Dock-MD-FEP is an open-source tool designed for automated binding free energy calculations using free energy perturbation methods. It provides a comprehensive workflow for docking and molecular dynamics simulations, specifically targeting interactions between proteins and small molecules.