/tools

Claude Scientific Skills is a collection of 140 ready-to-use scientific skills that enable users to perform complex workflows in drug discovery and cheminformatics. It includes functionalities for molecular property prediction, virtual screening, and molecular docking, making it a valuable resource for researchers in computational chemistry and molecular biology.

DiffDock is a state-of-the-art molecular docking tool that utilizes diffusion models to predict the 3D structure of protein-ligand complexes. It provides a confidence score for its predictions and supports various input formats for proteins and ligands.

Uni-Mol is a universal 3D molecular representation learning framework that supports various tasks such as molecular property prediction, binding pose prediction, and quantum chemical property prediction. It includes tools for molecular representation and docking, demonstrating state-of-the-art performance in these areas.

OpenBioMed is a Python deep learning toolkit designed for AI-empowered biomedicine, offering flexible APIs and over 20 tools for various applications including molecular property prediction, protein folding, and docking. It supports a wide range of molecular types and provides a unified data processing pipeline for handling multi-modal biomedical data.

AutoDock Vina is a fast and widely used open-source docking program that facilitates the docking of ligands to macromolecules. It supports multiple ligands and batch mode for virtual screening, making it a valuable tool in computational drug discovery.

Gnina is a molecular docking program that utilizes deep learning techniques, particularly convolutional neural networks, to score and optimize ligand interactions with protein receptors. It is built on top of existing docking software and aims to enhance the accuracy and efficiency of molecular docking processes.

PyRosetta.notebooks offers Jupyter Notebooks that serve as a learning resource for the PyRosetta platform, which is used for biomolecular structure prediction and design. The repository includes tutorials on protein folding, docking, and design, making it a valuable tool for researchers in computational biology and chemistry.

PLIP (Protein-Ligand Interaction Profiler) is a tool designed to analyze and visualize non-covalent interactions between proteins and ligands in PDB files. It facilitates the understanding of molecular interactions, which is crucial for applications in drug discovery and molecular biology.

AutoDock-GPU is an accelerated version of the AutoDock software that utilizes GPU and OpenCL technologies to perform molecular docking simulations. It allows for efficient virtual screening of ligands against protein targets, enhancing the speed and performance of molecular docking studies.

EquiBind is a geometric deep learning model that predicts the binding location and orientation of small molecules to proteins. It utilizes SE(3)-equivariant neural networks to achieve fast and accurate predictions, making it a valuable tool in drug discovery.

ProLIF (Protein-Ligand Interaction Fingerprints) is a tool that generates interaction fingerprints for complexes involving ligands, proteins, DNA, or RNA. It utilizes data from molecular dynamics trajectories and docking simulations, making it valuable for drug discovery and cheminformatics.

The Open Drug Discovery Toolkit (ODDT) is a modular toolkit for cheminformatics and molecular modeling, enabling users to perform tasks such as scoring, docking, and screening of drug candidates. It is built in Python and leverages libraries like RDKit and OpenBabel for enhanced molecular analysis.

P2Rank is a command-line tool that predicts ligand-binding sites from protein structures using machine learning techniques. It provides high accuracy in identifying potential binding pockets without relying on external databases, making it useful for drug discovery and virtual screening applications.

LightDock is a docking framework that utilizes the Glowworm Swarm Optimization algorithm to facilitate the docking of proteins, peptides, and DNA. It allows users to define custom scoring functions and apply residue restraints, making it a versatile tool for studying molecular interactions.

The Rosetta Bio-macromolecule modeling package is a comprehensive suite for computational modeling and analysis of protein structures. It includes algorithms for de novo protein design, enzyme design, and ligand docking, facilitating significant advancements in computational biology.

PoseBusters is a tool designed to perform plausibility checks on generated molecule poses, particularly in the context of docking studies. It allows users to validate the accuracy of predicted ligand poses against known protein-ligand complexes, contributing to the assessment of molecular docking methods.

Meeko is an interface for AutoDock that prepares input files for molecular docking and processes the output. It parameterizes both small organic molecules and biological macromolecules, facilitating drug discovery and molecular modeling.

DynamicBind is a computational tool that predicts ligand-specific structures of protein-ligand complexes using a deep equivariant generative model. It facilitates the docking process by generating and ranking multiple poses, providing insights into binding affinities and conformational changes.

Jupyter Dock is a collection of Jupyter Notebooks that facilitate interactive molecular docking protocols, allowing users to visualize, convert file formats, and analyze docking results. It supports various docking methods and provides comprehensive protocols for different docking scenarios.

Uni-Dock is a GPU-accelerated molecular docking program designed to enhance the speed and accuracy of virtual screening processes. It supports various scoring functions and includes tools for handling input and output, as well as benchmarks for evaluating performance against public datasets.

This repository contains a series of Google Colab tutorials focused on structural bioinformatics, covering topics such as protein folding, molecular dynamics simulations, and molecular docking. It serves as an educational resource for learning about molecular modeling and simulation techniques.

PLINDER is a dataset and evaluation resource focused on protein-ligand interactions, containing over 400k systems and numerous annotations for training and benchmarking docking algorithms. It aims to standardize the evaluation of protein-ligand interactions in the field of computational chemistry.

EquiDock is a tool designed for fast rigid protein-protein docking using independent SE(3)-equivariant models. It includes preprocessing steps for datasets and allows for training and inference of docking models, making it relevant for molecular simulations and drug discovery.

PLACER is a graph neural network that predicts protein-ligand conformational ensembles by generating structures of small molecules in the context of proteins. It excels in tasks such as docking and modeling conformational heterogeneity, providing a rapid and stochastic approach to molecular design.