A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)

Deep Reinforcement Learning for de-novo Drug Design

OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research

An R package to calculate indices and theoretical physicochemical properties of peptides and protein sequences.

QSARtuna: QSAR model building with the optuna framework

Chemical representation learning paper in Digital Discovery

The backend services of the GenUI framework. The backend provides the REST API used for molecular generation, QSAR modelling and chemical space visualization.

Automated QSAR based on multiple small molecule descriptors

Modeling framework for eTRANSAFE project

Generate Boltzmann-weighted Sterimol Steric Parameters for Conformationally-Flexible Substituents

PyTorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"

Classification models for hemolytic nature and hemolytic activity predictions in peptide/protein sequences

A high-quality hand-curated logD7.4 dataset of 1,130 compounds

A cheminformatics package to perform Applicability Domain of molecular fingerprints based in similarity calculation.

Automated framework for the curation of chemogenomics data and to develop QSAR models for virtual screening using the open-source KNIME software

Calculate Sterimol Parameters from Sructure Input/Output Files

A tool for creating Quantitative Structure Property/Activity Relationship (QSPR/QSAR) models.

A repository to build and deploy GenUI docker images. GenUI is a collection of web services for interactive molecular generation, QSAR modelling and chemical space visualization.

GenUI frontend application. It provides a GUI to the GenUI REST API web services.

Code for the paper Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language