/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.

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.

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.

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.

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.

DrugCLIP is a tool designed for contrastive protein-molecule representation learning aimed at enhancing virtual screening processes in drug discovery. It includes datasets and methodologies for training models that predict interactions between proteins and small molecules.

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.

FPSim2 is a Python/C++ package that enables fast compound similarity searches using optimized algorithms. It is particularly useful in cheminformatics applications, allowing researchers to efficiently find similar molecules based on their chemical properties.

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.

Chemprop is a tool designed for guided molecular property prediction and discovery using evidential deep learning techniques. It enables uncertainty quantification in predictions, facilitating better optimization and virtual screening in drug discovery.

Uni-GBSA is an automatic workflow designed to perform MM/GB(PB)SA calculations for evaluating ligand binding free energies in virtual screening. It includes functionalities for topology preparation, structure optimization, and batch processing of multiple ligands against a protein target.

PSICHIC is a physicochemical graph neural network designed to learn protein-ligand interaction fingerprints from sequence data. It enables quick virtual screening and deep analysis of molecular interactions, making it a valuable tool for drug discovery.

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.

DEEPScreen is a virtual screening tool that utilizes deep convolutional neural networks to predict drug-target interactions based on 2-D structural representations of compounds. It is aimed at enhancing early-stage drug discovery by providing accurate predictions from compound images.

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.

CDPKit is an open-source cheminformatics software toolkit designed for processing chemical data. It offers features for molecular representation, property prediction, pharmacophore generation, and integration with machine learning libraries, making it a valuable resource for computational drug discovery.

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.

VSFlow is an open-source tool for ligand-based virtual screening of large compound libraries. It supports various screening methods, including substructure, fingerprint, and shape-based approaches, and allows for database preparation and result visualization.

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.

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.

PharmacoNet is an open-source tool for protein-based pharmacophore modeling that utilizes deep learning for ultra-large-scale virtual screening. It automates the evaluation of ligands and supports feature extraction for deep learning applications in drug discovery.

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.