/tools

Molecule-RNN is a recurrent neural network designed to generate drug-like molecules for drug discovery. It learns from a dataset and samples new molecules that resemble the training data, utilizing various tokenization methods for SMILES representation.

OptADMET is a webserver designed for ADMET optimization in drug discovery, providing a platform to analyze and optimize compounds based on 32 important ADMET properties. It utilizes a large database of experimental data and transformation rules to guide medicinal chemists in lead optimization.

This tool predicts the binding affinity of protein-ligand complexes by utilizing Graph Neural Networks. It processes 3D structural data to enhance the accuracy of drug-target interaction predictions, aiding in drug design and discovery.

MolCraftDiffusion is a unified generative-AI framework that facilitates the training and deployment of 3D molecular diffusion models for various molecular generation tasks. It supports property-targeted generation and provides tools for analyzing and optimizing generated molecules.

This repository contains computational models that identify potential drugs for repurposing against SARS-CoV-2 by utilizing QSAR models and virtual screening techniques. It specifically targets Mpro inhibitors and includes curated data and predictions for FDA approved and experimental drugs.

This repository provides an introduction to small molecule drug design using structure-based techniques and interactive Jupyter notebooks. It covers ligand-based analysis, generative design with REINVENT4, and docking to analyze interactions with the Zika virus protease.

This repository implements a deep learning approach to classify protein-ligand interactions as allosteric or orthosteric based on molecular dynamics data. It includes a full pipeline for data processing, model training, and evaluation, making it a useful tool for molecular simulations and drug discovery.

Rxitect is a de-novo drug design library that utilizes deep reinforcement learning and generative flow networks to create molecules with a focus on synthetic accessibility. It aims to improve the practicality of generated molecules by incorporating retrosynthesis-aware models into the design process.

DrugDataResource is a repository that offers a variety of datasets aimed at facilitating drug discovery and development. It includes datasets for drug-target interactions, ADMET properties, and other molecular characteristics, which are essential for computational chemistry and molecular biology research.

Drugforge is a toolkit designed for open antiviral drug discovery, offering a range of functionalities including molecular docking, simulations, and machine learning models for predicting compound activity. It aims to support the medicinal chemistry community with a robust pipeline of computational tools.