/tools

This repository provides scripts and resources for performing docking and free energy calculations related to the COVID Moonshot initiative. It includes tools for preparing receptors and ligands, as well as analyzing results from molecular simulations.

Ringtail is a Python package that facilitates the storage and analysis of virtual screening results from AutoDock-GPU and AutoDock Vina. It allows users to create SQLite databases from docking results, perform filtering, and export data, making it a valuable tool for molecular docking studies.

OMTRA is a multi-task generative model that facilitates structure-based drug design by generating novel molecules and performing protein-ligand docking. It supports various tasks including unconditional and conditioned generation of ligands, making it a valuable tool in computational chemistry and drug discovery.

AutoDock CrankPep (ADCP) is a specialized docking engine for peptides that utilizes Monte-Carlo search methods to optimize peptide-receptor interactions. It allows for the docking of peptides provided as 3D structures or sequence strings, making it a valuable tool in molecular docking and protein design.

ChemFlow is a series of computational chemistry workflows designed to automate and simplify the drug discovery pipeline. It includes functionalities for docking, rescoring, and benchmarking, allowing users to focus on analysis and decision-making.

PackDock is a tool that implements a diffusion-based model for flexible protein-ligand docking, allowing for the effective packing of side chains in protein structures. It integrates with various ligand conformation sampling algorithms to enhance docking results.

EasyDock is a Python module designed to automate the molecular docking process, from molecule preparation to result analysis. It supports various docking programs and offers features like distributed computing and detailed protein-ligand interaction analysis.

FlexDock is a tool designed for flexible docking and relaxation of molecular complexes, aiming to improve the accuracy of docking predictions. It includes functionalities for preparing input data, running models, and training on relevant datasets, making it useful for researchers in computational chemistry and drug discovery.

LDM-3DG implements a pipeline for latent 3D graph diffusion, enabling the generation and evaluation of molecular structures. It includes functionalities for training models on molecular data and generating embeddings for small molecules, making it a valuable tool for molecular design and docking applications.

DFMDock is a diffusion model that integrates sampling and ranking for protein docking tasks. It allows users to perform inference on protein structures using PDB files, facilitating the study of molecular interactions.

LABODOCK is a collection of Jupyter Notebooks designed for molecular docking using Google Colab. It simplifies the docking process by automating pre- and post-docking tasks and integrates various cheminformatics tools for effective in-silico experimentation.

DockM8 is an all-in-one structure-based virtual screening workflow that utilizes consensus docking to prepare libraries and proteins, perform docking, and rank poses. It is designed to facilitate drug discovery by streamlining the virtual screening process.

Uni-Dock2 is a GPU-accelerated molecular docking software that enhances docking accuracy through advanced algorithms. It supports various docking tasks, including virtual screening and covalent docking, making it a valuable tool for researchers in drug discovery and molecular design.

ProtoCaller is a Python library designed for the automation of relative protein-ligand binding free energy calculations using GROMACS. It integrates various tools to streamline the free energy calculation process, making it useful for researchers in molecular dynamics and drug discovery.

AliDiff implements a method for aligning target-aware molecule diffusion models with exact energy optimization. It provides tools for data generation, training, and evaluation, including molecular docking capabilities, making it useful for molecular design and drug discovery applications.

GenScore is a generalized scoring framework for protein-ligand interactions that extends RTMScore. It provides capabilities for scoring, ranking, and screening on multiple datasets, making it useful for molecular property prediction and docking tasks.

The prtm repository provides a deep learning library for protein models, enabling tasks such as protein folding, inverse folding, and ligand docking. It aims to streamline workflows in protein design and structure prediction, making it a valuable tool in computational biology and drug discovery.

AA-Score is a tool that predicts protein-ligand binding affinity using an empirical scoring function based on various interaction components. It is utilized in virtual screening and lead optimization within the field of computer-aided drug discovery.

DiffDock-Pocket is a molecular docking model that utilizes diffusion processes to predict ligand poses in protein binding pockets while accommodating flexible side chains. It provides functionalities for training models, running inference, and visualizing docking results.

PyPLIF-HIPPOS is an advanced molecular interaction fingerprinting tool that enhances the analysis of docking results from AutoDock Vina and PLANTS. It generates customized interaction bitstrings from the 3D coordinates of ligands and proteins, facilitating molecular docking post-analysis.

MetaScreener is a collection of scripts that integrates various molecular modeling and docking programs to facilitate virtual screening and analysis of molecular interactions. It automates the processing of data and results, making it a valuable tool for computational chemistry and drug discovery.

LightDock-Rust is a Rust implementation of the LightDock software designed for macromolecular docking. It utilizes scoring functions like DFIRE to optimize the binding of proteins and peptides, making it a valuable tool for molecular simulations in bioinformatics.

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.

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.