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

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

Deep Reinforcement Learning for de-novo Drug Design

QSARtuna: QSAR model building with the optuna framework

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

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

Chemical representation learning paper in Digital Discovery

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

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

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"

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

Calculate Sterimol Parameters from Sructure Input/Output Files

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

drugdesign.org source of truth

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

MolEnc: a molecular encoder using rdkit and OCaml.

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

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