/tools

DrugAI_Drug-Likeness is a tool that evaluates the drug-likeness of molecules based on various molecular properties and structure features. It implements several rules and filters, such as Lipinski's Rule of Five, to assess the suitability of compounds for drug development.

TransAntivirus is a transformer-based molecular generative model aimed at designing antiviral drugs. It allows for the generation of novel compounds and is built upon existing frameworks for molecular generation, making it a valuable tool in the field of drug discovery.

ADMET-AGI is a tool designed to automate the prediction of ADMET properties using artificial intelligence. It aims to enhance the drug discovery process by providing cognitive reasoning capabilities for molecular property predictions.

DTITR is a tool designed for end-to-end prediction of drug-target binding affinities using a Transformer architecture. It leverages self-attention mechanisms to analyze protein sequences and compound SMILES strings, making it a valuable resource for drug discovery.

AttentionDTA_TCBB is a tool designed for predicting drug-target binding affinities using a sequence-based deep learning model with an attention mechanism. It includes datasets for training and testing the model, making it relevant for molecular property prediction in drug discovery.

DockCoV2 is a molecular docking pipeline that facilitates the prediction of binding affinities between FDA-approved drugs and SARS-CoV-2 proteins. It provides a comprehensive drug database and allows users to download docking data for further analysis.

InSilicoQ is a quantum computation-based package designed for drug design and discovery, utilizing quantum algorithms for property prediction and molecule generation. It integrates machine learning techniques to enhance virtual screening and small molecule design.

AutoGrid is a software tool designed to precalculate grids used by docking programs like AutoDock. It helps predict the binding of small molecules to proteins, facilitating structure-based drug design and virtual screening.

PDB-CAT is a Jupyter Notebook tool designed to automatically categorize PDB structures based on the type of interaction between proteins and ligands, while also checking for mutations in the protein sequence. It facilitates decision-making in drug discovery by providing clear classifications and outputs for protein-ligand interactions.

SGNN-EBM is a tool designed for structured multi-task learning aimed at predicting molecular properties. It includes a novel dataset for drug discovery and proposes a state graph neural network-energy based model for effective task modeling.

QADD is a software tool designed for de novo drug design that utilizes iterative multi-objective deep reinforcement learning. It incorporates a graph-based molecular quality assessment model to generate high-quality drug-like molecules while considering their potential drug properties.

De-Novo-Drug-Design is a tool that utilizes Deep Reinforcement Learning to optimize the permeation of drugs across the blood-brain barrier. It aims to facilitate the design of new drug candidates by improving their molecular properties.

ACID is a web server designed for drug repurposing using a consensus inverse docking method. It evaluates the binding affinities of small molecules to various proteins, providing a valuable resource for molecular docking studies.

This repository implements a reinforcement learning framework combined with active learning to optimize the selection of small-molecule drug candidates for in-silico screening. It enhances the efficiency of training by focusing on informative samples, thereby reducing computational costs.

ADMET_Prediction_Models provides tools for predicting various molecular properties related to drug absorption and toxicity. It includes functionalities for training models on SMILES data and predicting outcomes based on trained models, making it a valuable resource for computational chemistry and drug discovery.

moo-denovo is a tool for automated de novo design of drug-like molecular libraries, leveraging deep learning and multi-objective optimization techniques. It allows users to optimize molecular descriptors and generate new molecular structures based on specified criteria.

This repository benchmarks active learning protocols for predicting ligand binding affinities using various datasets corresponding to different protein targets. It evaluates the performance of machine learning models in identifying top binders, providing valuable insights for computational drug discovery.

AttentionDTA_BIBM is a repository that implements a model for predicting drug-target binding affinity using an attention mechanism. It includes code for data processing, model training, and testing, making it a useful tool for researchers in the field of molecular property prediction.

TAGMol is a framework for target-aware gradient-guided molecule generation, aimed at optimizing molecular properties for drug design. It includes functionalities for training models and evaluating generated molecules based on various criteria such as binding affinity and drug-likeness.

ML4SMILES is a tool designed for the automatic prediction of molecular properties by utilizing substructure vector embeddings within a feature selection workflow. It includes scripts for generating molecular descriptors, performing feature analyses, and optimizing predictive models, making it valuable for drug discovery and molecular property prediction.

This repository offers a Jupyter notebook for batch conversion of molecular file formats, such as SDF to PDB and SMILES to PDBQT. It streamlines the process of preparing molecular data for cheminformatics and drug discovery applications.

MGDTA is a tool designed for predicting drug-target binding affinity using multigranular representations. It includes datasets for training and evaluation, making it a valuable resource for researchers in the field of drug discovery.

HGRL-DTA is a PyTorch implementation designed for predicting drug-target binding affinity using hierarchical graph representation learning. It utilizes datasets like Davis and KIBA to train and evaluate its model, making it a valuable tool in the field of drug discovery.

BBB-score is a script that utilizes RDKit to reproduce the Blood-Brain Barrier score as reported in scientific literature. It serves as a tool for predicting molecular properties relevant to drug design and discovery.