3D-Scaffold: A Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

(2021) Rohan Gupta et al.   MOLECULAR DIVERSITY

High-throughput screening of the ReFRAME, Pandemic Box, and COVID Box drug repurposing libraries against SARS-CoV-2 nsp15 endoribonuclease to identify small-molecule inhibitors of viral activity

(2021) Ryan Choi et al.   PLoS One

Benchmarking graph neural networks for materials chemistry

(2021) Victor Fung et al.   npj Computational Materials

ScaffoldGraph: an open-source library for the generation and analysis of molecular scaffold networks and scaffold trees

(2020) Oliver B Scott et al.   BIOINFORMATICS

An overview of drug discovery and development

(2020) Nurken Berdigaliyev et al.   Future Medicinal Chemistry

SMILES-based deep generative scaffold decorator for de-novo drug design

(2020) Josep Arús-Pous et al.   Journal of Cheminformatics

Systematic review of the efficacy and safety of antiretroviral drugs against SARS, MERS or COVID‐19: initial assessment

(2020) Nathan Ford et al.   Journal of the International AIDS Society

The SARS-CoV-2 main protease as drug target

(2020) Sven Ullrich et al.   BIOORGANIC & MEDICINAL CHEMISTRY LETTERS

A comprehensive review about SARS-CoV-2

(2020) SK Manirul Haque et al.   Future Virology

Advancing Drug Discovery via Artificial Intelligence

(2019) H.C. Stephen Chan et al.   TRENDS IN PHARMACOLOGICAL SCIENCES

Randomized SMILES strings improve the quality of molecular generative models

(2019) Josep Arús-Pous et al.   Journal of Cheminformatics

DeepScaffold: A Comprehensive Tool for Scaffold-Based De Novo Drug Discovery Using Deep Learning

(2019) Yibo Li et al.   Journal of Chemical Information and Modeling

The Taxonomy of Covalent Inhibitors

(2018) Alfred Tuley et al.   BIOCHEMISTRY

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Molecular generative model based on conditional variational autoencoder for de novo molecular design

(2018) Jaechang Lim et al.   Journal of Cheminformatics

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Deep reinforcement learning for de novo drug design

(2018) Mariya Popova et al.   Science Advances

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

(2018) K. T. Schütt et al.   Journal of Chemical Theory and Computation

Generative Recurrent Networks for De Novo Drug Design

(2017) Anvita Gupta et al.   Molecular Informatics

Application of Generative Autoencoder in De Novo Molecular Design

(2017) Thomas Blaschke et al.   Molecular Informatics

Automating drug discovery

(2017) Gisbert Schneider   NATURE REVIEWS DRUG DISCOVERY

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

(2017) Marwin H. S. Segler et al.   ACS Central Science

BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology

(2015) Michael K. Gilson et al.   NUCLEIC ACIDS RESEARCH

AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility

(2015) Pradeep Anand Ravindranath et al.   PLoS Computational Biology

Universal Structure Conversion Method for Organic Molecules: From Atomic Connectivity to Three-Dimensional Geometry

(2015) Yeonjoon Kim et al.   BULLETIN OF THE KOREAN CHEMICAL SOCIETY

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS