/tools

MCMG is a software tool designed for generating molecules based on specific constraints using advanced machine learning techniques. It allows users to customize tasks for molecular generation, making it suitable for applications in drug discovery and molecular design.

ElectronVisualized is a web-based interactive tool that visualizes atomic and molecular orbitals in quantum mechanics. It allows users to draw molecules and explore their properties, making it a useful resource for those studying chemistry and molecular structures.

This repository provides notebooks and an environment for a workshop on Python scripting for molecular docking. It includes resources for preparing and executing docking simulations, specifically targeting small molecules and proteins.

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.

SurfGen is a tool designed for generating 3D molecular structures by learning from topological surfaces and geometric features. It utilizes a dataset for training and provides functionalities for generating molecules, particularly targeting pharmaceutical applications.

DeepFMPO is a tool designed for optimizing drug design through deep reinforcement learning. It allows users to generate and modify molecules based on lead compounds, facilitating the exploration of new drug candidates.

Watvina is a docking tool that facilitates drug design by supporting both explicit and implicit water models in protein-ligand interactions. It allows for pharmacophore and position-constrained docking, optimizing ligand interactions with receptors using the Autodock Vina engine.

Delete is a tool for deep lead optimization that generates new molecular structures by utilizing a structure-aware network and deleting specific fragments from lead compounds. It is designed to assist in drug discovery by suggesting new ligands that fit within protein pockets.

PatentChem is a tool that downloads USPTO patents and extracts molecules related to specified keyword queries. It processes patent claims to find and output SMILES strings of relevant molecules, making it useful for cheminformatics and molecular data extraction.

PGMG is a PyTorch implementation that utilizes a pharmacophore-guided deep learning model to generate bioactive molecules with structural diversity. It allows users to input pharmacophore hypotheses and generates a large number of candidate molecules that meet specified conditions.

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.

This repository contains code for a deep generative model aimed at generating molecular fragments, as presented in the AISTATS 2020 paper. It includes functionalities for data preprocessing, model training, sampling, and postprocessing, making it a useful tool for molecular design.

This repository serves as a companion to the book 'Machine Learning for Drug Discovery', providing code and data for various machine learning techniques applied to drug discovery. It covers topics such as ligand-based screening, generative models for de novo design, and structure-based drug design, making it a valuable resource for researchers in the field.

PyCGTOOL is a software tool designed to generate coarse-grained molecular dynamics models from atomistic simulation trajectories. It aids in parametrizing coarse-grained molecular mechanics models, allowing for efficient testing of different mapping and bond topologies.

cG-SchNet is a conditional generative neural network that focuses on the inverse design of 3D molecular structures. It allows users to generate molecules based on specified conditions, leveraging a dataset of small molecules to train the model.

GNPS_Workflows provides a collection of public workflows for analyzing mass spectrometry data, focusing on molecular networking and metabolomics. It facilitates the exploration and interpretation of complex molecular data, making it a valuable resource for researchers in the field.

DeepChem GUI is a web-based interface that allows users to predict the docking of ligands to proteins using pretrained DeepChem models. It supports molecular visualization and editing, making it a useful tool for researchers in computational chemistry and drug discovery.

The schnetpack-gschnet repository provides an extension for generating 3D molecular structures using a conditional generative neural network. It allows for targeted molecule generation by conditioning on chemical and structural properties, making it a valuable tool for molecular design.

LP-PDBBind is a repository that develops scoring functions using the PDBBind dataset, providing tools for dataset creation and model retraining for predicting molecular properties such as binding affinities. It includes compiled datasets and scripts for preparing and analyzing protein-ligand complexes.

LEFTNet is a framework for building efficient and expressive 3D equivariant graph neural networks, specifically designed to predict molecular properties using datasets such as QM9 and MD17. It leverages advanced machine learning techniques to enhance molecular simulations and property predictions.

ABFE_workflow is a SnakeMake-based workflow designed for performing Absolute Binding Free Energy calculations using GROMACS. It allows for high-throughput scaling and is aimed at facilitating drug discovery by predicting binding affinities between ligands and protein receptors.

Grappa is a machine learned molecular mechanics force field that utilizes graph neural networks to predict bonded parameters for molecular simulations. It integrates with GROMACS and OpenMM, allowing users to parametrize systems and train custom models using various molecular datasets.

Dock-MD-FEP is an open-source tool designed for automated binding free energy calculations using free energy perturbation methods. It provides a comprehensive workflow for docking and molecular dynamics simulations, specifically targeting interactions between proteins and small molecules.

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.