/tools

This repository provides an implementation of Thompson Sampling for virtual screening of un-enumerated libraries in molecular design. It allows users to efficiently search and score potential molecules based on various scoring functions, facilitating the exploration of chemical space.

Boltzina is a pipeline that integrates AutoDock Vina docking with Boltz-2 scoring to efficiently predict binding affinities for molecular docking. It supports both full docking and scoring-only modes, making it a valuable tool for virtual screening in drug discovery.

RxDock is an open-source docking program that facilitates the docking of small molecules to proteins and nucleic acids. It is optimized for high-throughput virtual screening and binding mode prediction, allowing researchers to efficiently explore ligand interactions.

rDock is a fast and versatile open-source docking program that facilitates the docking of small molecules to proteins and nucleic acids. It is particularly useful for high-throughput virtual screening campaigns and binding mode prediction studies.

GeminiMol is a molecular representation model that enhances molecular feature extraction by incorporating conformational space profiles. It is designed for applications in drug discovery, including virtual screening, target identification, and quantitative structure-activity relationship (QSAR) modeling.

The CADD Vault is an open-source repository that offers a comprehensive collection of resources and tools for computer-aided drug design. It includes materials on virtual screening, molecular dynamics simulations, and machine learning applications, making it a valuable resource for researchers in the field.

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.

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.

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.

DiffPhore is a tool that implements a knowledge-guided diffusion model for mapping ligands to pharmacophores in 3D space. It enhances virtual screening capabilities and provides datasets for pharmacophore-ligand pairs, making it useful for drug discovery applications.

CLIPZyme is a tool for reaction-conditioned virtual screening of enzymes, allowing users to evaluate enzyme reactions based on their features. It provides functionalities for loading screening sets, processing data, and extracting hidden representations of reactions and proteins.

DENOPTIM is a software package that facilitates the de novo design and virtual screening of functional molecules by assembling building blocks and analyzing their properties. It employs genetic algorithms for optimization and is suitable for various types of chemical entities.

OpenPharmaco is an open-source software designed for fully-automated protein-based pharmacophore modeling and high-throughput virtual screening. It utilizes deep learning techniques to enhance the pharmacophore modeling process, making it suitable for drug discovery applications.

RGMolSA is a tool for ligand-based virtual screening that utilizes a new surface-based molecular shape descriptor derived from Riemannian geometry. It aims to predict potential new hits by comparing molecular shapes to those with known favorable properties, facilitating the drug discovery process.

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.

This repository provides scripts for training neural network models to predict binding affinities of ligands to receptors. It includes functionalities for clustering data for cross-validation and generating unique ligand poses, making it a useful tool for molecular property prediction in drug discovery.

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.

Screenlamp is a Python toolkit designed to facilitate ligand-based virtual screening workflows. It enables researchers to prioritize ligand candidates through hypothesis-driven filtering, contributing to drug discovery efforts.

V2DB is a database and tool for generating and predicting properties of novel two-dimensional materials through virtual screening. It utilizes machine learning models to predict various material properties, facilitating the discovery of stable and functional 2D materials.

The Computer_aided_drug_discovery_kit is a pipeline designed for virtual screening of pharmaceutical compounds using similarity-based and structure-based techniques. It includes modules for data extraction, descriptor calculation, and machine learning classification to predict the bioactivity of compounds.

The Automated QSAR Framework is designed for the curation of chemogenomics data and the development of predictive QSAR models using machine learning. It facilitates data preparation, chemical space analysis, and virtual screening, making it a valuable tool for drug discovery.

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.