/tools

MolDockLab is a data-driven workflow that identifies the best consensus Structure-Based Virtual Screening (SBVS) approach for a target of interest. It integrates various docking tools and scoring functions to optimize hit identification from ligand libraries.

This repository provides tools for virtual screening on the PriA-SSB target using the AMS and Enamine REAL libraries. It includes Jupyter notebooks for analysis, datasets for training, and source code for model training and scoring, making it a valuable resource for molecular property prediction and drug discovery.

AutoDockPipeline is a comprehensive solution for protein-ligand docking, facilitating the study of protein-ligand interactions. It simplifies the docking process and provides customizable options for virtual screening, aiding researchers in drug discovery.

The 'active-learning-drug-discovery' repository provides a framework for iterative chemical screening in drug discovery using active learning methods. It includes datasets and various strategies for selecting compounds, aimed at improving the efficiency of drug discovery processes.

MUBD-HDACs offers comprehensive benchmarking datasets for histone deacetylases and sirtuin families, facilitating research in drug discovery and virtual screening. It is designed to support the development and evaluation of computational tools in the field of molecular biology.

The MAO QSAR repository provides data and code for training machine learning models to predict the activity of compounds against MAO-A and MAO-B. It includes datasets from ChEMBL and pre-computed docking scores, facilitating virtual screening and QSAR analysis.

RPDUAA is a program designed for the rational incorporation of unnatural amino acids into proteins using machine learning techniques. It facilitates protein design and optimization by leveraging existing experimental data to enhance protein functionality.

The vstools repository contains utilities for analyzing Smina virtual screen results, including tools for scanning log files, preparing data for reporting, and generating HTML reports of docking results. It facilitates the evaluation of ligand binding efficiencies and supports the visualization of docking outcomes.

Smina is a fork of AutoDock Vina designed to enhance scoring and energy minimization for molecular docking. It supports various ligand formats and allows for custom scoring functions, making it a valuable tool for researchers in drug discovery and molecular simulations.

CNS_MPO_calculator is a Python tool that calculates the CNS MPO score for molecules based on their SMILES representations and known pKa values. This score is used to evaluate the bioavailability of candidate drugs targeting the central nervous system, making it a valuable resource in drug discovery.

This repository provides a review of deep learning applications in virtual screening, including scripts for ligand and complex encoding. It serves as a resource for understanding recent developments in the field and includes code for generating figures related to the review.

DrugAppy-Validation-Paper provides code and data for validating an AI-guided computational chemistry platform aimed at efficient drug discovery. It includes molecular dynamics inputs, screening scripts, and validation results for compounds, facilitating the exploration of molecular properties and interactions.

OCDocker is a Python toolkit for automated molecular docking and virtual screening, featuring multiple docking engines and machine learning-based consensus scoring. It streamlines the process from preparation to analysis, making it a valuable tool for drug discovery and molecular design.

MUBD-DecoyMaker2.0 is a computational tool designed to create maximal unbiased benchmarking datasets for assessing virtual screening methods in drug discovery. It provides a user-friendly interface for generating data sets that can enhance the evaluation of virtual screening approaches.

ALiBERO is a software designed for Automatic Ligand-guided Backbone Ensemble Receptor Optimization using ICM. It facilitates the docking process by optimizing receptor conformations to improve ligand binding predictions.

ACP4 is a tool designed for the autocorrelation of pharmacophore features, facilitating the analysis of binding sites and the screening of molecular databases. It provides functionalities for encoding input files, querying molecules, and extracting pharmacophore points from 3D molecular structures.

HybridSP is a hybrid statistical potential model that predicts protein-ligand interactions by combining various distance and orientation-dependent interactions. It is intended for use in protein-ligand docking and virtual screening, demonstrating high accuracy compared to existing deep learning models.

This repository contains scripts and data for evaluating various conformer generation tools used in virtual screening workflows. It assesses the ability of these tools to reproduce bioactive conformers from small molecules, providing insights into their effectiveness and efficiency.

NIRII-ML-Design is a prediction model designed to accelerate the design of near-infrared-II molecular fluorophores. It utilizes machine learning to predict the HOMO-LUMO energy gap based on molecular structures provided in SMILES format.

Autodock-vina-batch is a collection of Python scripts designed to automate the preparation and management of batch molecular docking assays using Autodock Vina. It facilitates the preparation of multiple receptors for virtual screening against ligand libraries.

The TIMP2 Docking Pipeline is a computational framework designed for the virtual screening of TIMP2 modulators, incorporating ADMET and toxicity profiling. It utilizes docking simulations to identify potential allosteric modulators that can influence neuroplasticity.

rDock is an open-source docking program that facilitates the docking of small molecules to proteins and nucleic acids. This tool is part of a larger framework aimed at enhancing drug discovery processes through efficient molecular docking techniques.

Docking Factory is a tool that automates molecular docking runs on HPC clusters using the Dask framework. It supports various docking software backends and allows for scalable and customizable virtual screening of large compound databases.

The awROC_calculation module generates Receiver Operating Characteristic (ROC) curves and calculates various metrics to evaluate the performance of drug design software. It focuses on benchmarking virtual screening tools by analyzing the ability to distinguish between active compounds and inactive decoys.