/tools

CaFE is a VMD plugin that facilitates the prediction of binding affinities through the calculation of free energy. It employs end-point free energy methods, making it a valuable tool for researchers in molecular modeling and drug discovery.

This tool implements the Confidence Bootstrapping procedure for enhancing protein-ligand docking predictions. It includes pretrained models and datasets for benchmarking, making it useful for researchers in molecular docking and drug discovery.

DiffDock is a Colab implementation of a state-of-the-art molecular docking method that allows users to perform docking simulations for single and multiple protein-ligand complexes. It provides example data files and notebooks to facilitate the docking process, making it a useful tool for researchers in molecular biology and computational chemistry.

ML-ensemble-docking is a tool designed to enhance structure-based virtual screening by utilizing ensemble docking methods combined with machine learning techniques. It evaluates the performance of various protein targets and improves ligand ranking through advanced predictive models.

The Drug Design Data Resource (D3R) is a suite of software designed to facilitate the filtering and scoring of new molecular sequences. It supports participants in the CELPP challenge by providing necessary workflows and tools for molecular docking and analysis.

LightDock is a docking framework that utilizes the Glowworm Swarm Optimization algorithm to facilitate protein-protein, protein-peptide, and protein-DNA docking. It allows users to define custom scoring functions and supports various simulation options, making it a versatile tool for molecular docking studies.

QuickBind is a light-weight and interpretable molecular docking model that predicts binding affinities for molecular complexes. It utilizes a dataset from PDBBind for training and evaluation, making it a valuable tool for researchers in drug discovery and molecular design.

The 'vina_docking' repository contains Python scripts for performing molecular docking using AutoDock Vina. It allows users to preprocess receptor and ligand files, run docking simulations, and output results, making it a valuable tool for drug discovery and molecular interactions.

This repository implements a method that combines pre-trained protein language models with geometric deep learning networks to enhance the representation of macromolecules. It addresses tasks such as binding affinity prediction and protein-protein interface prediction, making it a valuable tool in molecular biology and computational chemistry.

TCM-Network-Pharmacology is a tool for traditional Chinese medicine network pharmacology that includes processes like prognostic gene screening, molecular docking verification, and various diagram mappings. It provides functionalities for analyzing and validating molecular interactions and properties.

PIGNet2 is a deep learning-based model that predicts protein-ligand interactions and binding affinities, facilitating virtual screening in drug discovery. It includes training and benchmarking scripts, making it a comprehensive tool for evaluating molecular interactions.

CompassDock is a framework for deep learning-based molecular docking that evaluates binding affinities and protein-ligand interactions. It provides tools for assessing the physical and chemical properties of ligands and their bioactivity favorability.

Deepdock is an experimental package that utilizes deep learning techniques specifically for molecular docking applications. It aims to enhance the accuracy and efficiency of predicting how molecules interact with each other at the molecular level.

DyScore is an open-source tool that implements a scoring method for identifying true binders and non-binders in drug discovery. It utilizes molecular docking and dynamic feature generation to predict the binding likelihood of compounds to target proteins.

The DrugHunting repository provides Python scripts for automating drug discovery processes, including the design and optimization of drug-like molecules. It utilizes stochastic methods and cheminformatics to explore novel chemical spaces, making it suitable for applications like docking and virtual screening.

AnnapuRNA is a scoring function that evaluates RNA-ligand complex structures generated by computational docking methods. It provides a framework for predicting the interactions between RNA and small molecules, making it useful for drug discovery and molecular design.

PRODIGY-LIG is a tool designed for predicting the binding affinities of protein-small molecule complexes. It utilizes a structure-based approach to calculate binding energy, making it useful for drug discovery and molecular interactions.

fpocketR is a command-line tool designed to analyze RNA structures and visualize drug-like RNA-ligand binding pockets. It provides functionalities for characterizing these pockets and supports various analysis workflows for RNA-ligand interactions.

MetalDock is an open-source software tool that facilitates the docking of metal-organic complexes with proteins, DNA, and other biomolecules. It is designed to support research in molecular interactions involving transition metals.

FlexAID is a software tool that facilitates flexible docking of ligands to protein targets, allowing for the optimization of binding conformations. It utilizes genetic algorithms for ligand optimization and is relevant for molecular docking studies in drug discovery.

Sfcnn is a scoring function model that utilizes a 3D convolutional neural network to predict protein-ligand binding affinities. It is trained on various datasets and can be used for rescoring docking results, making it a valuable tool in drug discovery.

Uni-Dock-Benchmarks is a repository that contains a curated collection of datasets and benchmarking tests for assessing the performance and accuracy of the Uni-Dock docking system. It includes prepared structures and input files for both molecular docking and virtual screening, making it a valuable resource for researchers in computational chemistry.

DockBox is a Python wrapper library that simplifies the use of popular docking software for molecular docking tasks. It supports multiple docking programs and provides functionalities for rescoring and analyzing docking poses, making it a valuable tool for molecular modeling and drug discovery.

MetalloDock is an AI-powered molecular docking framework focused on metalloproteins. It excels in predicting binding affinities, reconstructing metal coordination geometries, and performing virtual screenings against metalloprotein targets.