Δ-Quantum machine-learning for medicinal chemistry-Reference-Cited by-同舟云学术

Δ-Quantum machine-learning for medicinal chemistry

Published:2022 Issue:18 Volume:24 Page:10775-10783
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Atz Kenneth¹^ORCID,Isert Clemens¹^ORCID,Böcker Markus N. A.¹^ORCID,Jiménez-Luna José¹²^ORCID,Schneider Gisbert¹³^ORCID

Affiliation:

1. ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland

2. Department of Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Straße 65, 88397 Biberach an der Riss, Germany

3. ETH Singapore SEC Ltd., 1 CREATE Way, #06-01 CREATE Tower, Singapore 138602, Singapore

Abstract

Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. 3D message-passing neural networks for Δ-quantum machine-learning enable fast access to DFT-level QM properties for drug-like molecules.

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Boehringer Ingelheim

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2022/CP/D2CP00834C

Reference81 articles.

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2. Exploring Strong Interactions in Proteins with Quantum Chemistry and Examples of Their Applications in Drug Design

3. Inhibition of the checkpoint protein PD-1 by the therapeutic antibody pembrolizumab outlined by quantum chemistry

4. Modeling the Photochemistry of the Reference Phototoxic Drug Lomefloxacin by Steady-State and Time-Resolved Experiments, and DFT and Post-HF Calculations

5. Theoretical study of phototoxic reactions of psoralens

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