/tools

PoseBench is a comprehensive benchmarking tool designed for evaluating protein-ligand structure prediction methods. It facilitates the comparison of various inference methods and provides datasets for benchmarking, making it a valuable resource in computational chemistry and molecular biology.

MolPAL is a software tool that utilizes active learning to enhance the efficiency of virtual chemical library exploration for compound discovery. It supports various objectives, including docking and lookup, and is designed to optimize the selection of molecules in high-throughput screening environments.

Dockstring is a Python package designed for easy molecular docking, allowing users to dock molecules from SMILES strings. It includes a highly-curated dataset and realistic benchmark tasks to aid in drug discovery efforts.

The ReinventCommunity repository provides a collection of Jupyter notebook tutorials for using REINVENT 3.2, focusing on molecular design through reinforcement learning and QSAR modeling. It includes examples of data preparation, model building, and various reinforcement learning scenarios to generate and optimize novel compounds.

TankBind is an open-source tool designed for predicting the binding structures and affinities of drugs to proteins using a trigonometry-aware neural network. It supports high-throughput virtual screening and provides scripts for dataset construction and evaluation.

This repository offers educational materials for a course on drug discovery computing techniques, covering various topics such as molecular dynamics, docking, and solubility. It aims to provide both course-specific resources and broader educational content for the community.

NoLabs is an open-source biolab that facilitates experiments in bioinformatics and drug discovery. It includes features for protein design, molecular simulations, and a workflow engine to manage various bioinformatics tasks.

PINDER is a comprehensive dataset and evaluation resource for protein-protein interactions, aimed at enhancing the training and evaluation of docking algorithms. It includes a large collection of protein structures and associated metadata, making it a valuable resource for researchers in molecular biology and computational chemistry.

PMDM is a software tool that enables the generation of 3D bioactive molecules and lead optimization by utilizing a dual diffusion model. It supports molecular docking and provides benchmarks for evaluating generated molecules, making it a valuable resource for drug discovery and molecular design.

FABind is a software tool designed for fast and accurate prediction of protein-ligand binding interactions. It includes enhancements for molecular docking through improved pocket prediction and pose generation, making it useful for drug discovery and molecular simulations.

KarmaDock is a deep learning tool for ligand docking that enhances the speed and accuracy of predicting binding poses and affinities. It integrates advanced graph neural networks to model protein-ligand interactions and has been validated on benchmark datasets for drug discovery applications.

Molchanica is a comprehensive tool for editing, visualizing, and simulating molecules and proteins. It includes features for molecular dynamics, docking, and predicting pharmacokinetic properties, making it suitable for drug discovery and molecular design.

DockStream is a docking wrapper that automates docking execution and post hoc analysis, providing access to various ligand embedders and docking backends. It is integrated with the REINVENT platform, allowing for the incorporation of docking into the molecular design process.

FlowDock is a geometric flow matching model designed for generative protein-ligand docking and affinity prediction. It provides tools for predicting molecular interactions and includes datasets for training and evaluation, making it a valuable resource in computational chemistry and molecular biology.

The Deep Docking protocol is a deep learning-based tool that accelerates docking-based virtual screening, allowing researchers to screen extensive chemical libraries significantly faster than traditional methods. It integrates various stages of molecular preparation, docking, and model training to identify potential drug candidates effectively.

Protenix-Dock is an end-to-end protein-ligand docking framework that utilizes empirical scoring functions for accurate docking tasks. It provides advanced features for conformation sampling and scoring, making it suitable for applications in drug discovery and molecular simulations.

DiffBindFR is a diffusion model-based framework designed for flexible protein-ligand docking. It provides tools for both forward and reverse docking, allowing users to model interactions between proteins and ligands effectively.

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.

Interformer is a neural network designed for predicting protein-ligand interactions, specifically generating energy functions and scoring docking poses. It utilizes contrastive learning to assess the quality of docking poses and predict binding affinities, making it a valuable tool in drug discovery.

RTMScore is a scoring function designed to predict protein-ligand interactions by utilizing residue-atom distance likelihood potentials and graph transformers. It processes proteins and ligands as 3D and 2D graphs, respectively, to calculate interaction probabilities and contributions.

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.

CarsiDock is a deep learning framework that enhances the accuracy of protein-ligand docking and screening by leveraging a large-scale dataset of protein-ligand complexes. It employs innovative architectural designs and pre-training techniques to predict binding poses effectively.

Awesome-Molecular-Docking is a curated list of resources aimed at solving molecular docking and related tasks. It includes software for docking, datasets, and references to molecular dynamics simulations, making it a valuable tool for researchers in drug discovery and molecular biology.

FlowSite and HarmonicFlow are tools for generating binding structures for single and multi-ligands, as well as designing binding site residues. They utilize advanced generative models to optimize molecular interactions, making them valuable for applications in drug discovery and protein design.