/tools

PCMol is a multi-target de novo molecular generator that utilizes AlphaFold's protein embeddings to create relevant molecules for various protein targets. It is designed to aid in drug discovery by generating SMILES representations of potential drug candidates.

Geometry-Deep-Learning-for-Drug-Discovery is a repository that explores the application of geometric deep learning methods in drug discovery and life sciences. It includes functionalities for predicting molecular properties, designing molecules, and provides datasets and benchmarks relevant to molecular machine learning.

PAMNet is a universal framework designed for accurate and efficient geometric deep learning of molecular systems. It excels in predicting molecular properties, such as binding affinities and RNA 3D structures, and utilizes graph neural networks to enhance performance in these tasks.

FLAG is a framework for generating 3D molecules that bind to target proteins using a fragment-based approach. It employs deep generative models to create realistic molecular structures, enhancing the process of structure-based drug design.

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.

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.

Meta Flow Matching is a tool designed for integrating vector fields on the Wasserstein manifold, particularly useful in predicting individual treatment responses in personalized medicine. It includes datasets and models for training on biological experiments, specifically targeting drug-screen datasets.

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.

PocketVina is a GPU-accelerated software for protein-ligand docking that automates the detection of binding pockets and evaluates interactions between proteins and ligands. It aims to enhance the accuracy and efficiency of docking processes in drug discovery.

This repository provides a PyTorch implementation of a variational autoencoder designed for automatic chemical design. It focuses on generating continuous representations of molecules, which can be utilized for drug discovery and molecular optimization.

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 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.

Matcher is a web application that facilitates the exploration of structure/activity relationships derived from large datasets, enabling users to optimize chemical structures for drug design. It is built on the mmpdb platform and provides a user-friendly interface for querying and analyzing molecular data.

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.

SkipGNN is a tool designed to predict molecular interactions by leveraging skip-graph networks, which consider both direct and second-order interactions in molecular networks. It provides datasets for drug-target and drug-drug interactions, making it useful for applications in drug discovery and molecular property prediction.

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.

The Otter Knowledge repository enhances protein sequence and SMILES drug databases with a multimodal knowledge graph, improving predictions on drug-target binding affinity benchmarks. It provides pre-trained models and datasets for representation learning in drug discovery.

HAC-Net is a deep learning architecture designed for highly accurate prediction of protein-ligand binding affinities. It utilizes a hybrid approach combining 3D convolutional neural networks and graph convolutional networks to achieve state-of-the-art results in this domain.

iSIM is a module designed to perform simultaneous comparisons of multiple molecules, providing an efficient method to calculate average pairwise similarities. It utilizes binary fingerprints and real number descriptors, making it applicable in chemical sampling, clustering, and visualization tasks.

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.

PepINVENT is a generative reinforcement learning framework for designing peptides, including both natural and non-natural amino acids. It allows users to specify objectives for peptide generation and optimization, making it applicable for peptide-based drug design and development.

OMTRA is a multi-task generative model that facilitates structure-based drug design by generating novel molecules and performing protein-ligand docking. It supports various tasks including unconditional and conditioned generation of ligands, making it a valuable tool in computational chemistry and drug discovery.