COSMO-RS-based descriptors for the machine learning-enabled screening of nucleotide analogue drugs against SARS-CoV-2

From National Research Council Canada

Download
  1. Available on October 26, 2021
DOIResolve DOI: https://doi.org/10.1021/acs.jpclett.0c02836
AuthorSearch for: 1; Search for: ORCID identifier: https://orcid.org/0000-0002-1878-4216
Name affiliation
  1. National Research Council of Canada. Nanotechnology
FormatText, Article
Journal titleThe Journal of Physical Chemistry Letters
ISSN1948-7185
1948-7185
Volume11
Issue21
Pages9408–9414
SubjectDNA replication; monomers; drug history; noncovalent interactions; molecular structure
Abstract
Chemical similarity-based approaches employed to repurpose or develop new treatments for emerging diseases, such as COVID-19, correlates molecular structure-based descriptors of drugs with those of a physiological counterpart or clinical phenotype. We propose novel descriptors based on a COSMO-RS (short for conductor-like screening model for real solvents) σ-profiles for enhanced drug screening enabled by machine learning (ML). The descriptors’ performance is hereby illustrated for nucleotide analogue drugs that inhibit the ribonucleic acid-dependent ribonucleic acid polymerase, key to viral transcription and genome replication. The COSMO-RS-based descriptors account for both chemical reactivity and structure, and are more effective for ML-based screening than fingerprints based on molecular structure and simple physical/chemical properties. The descriptors are evaluated using principal component analysis, an unsupervised ML technique. Our results correlate with the active monophosphate forms of the leading drug remdesivir and the prospective drug EIDD-2801 with nucleotides, followed by other promising drugs, and are superior to those from molecular structure-based descriptors and molecular docking. The COSMO-RS-based descriptors could help accelerate drug discovery for the treatment of emerging diseases.
Publication date
PublisherAmerican Chemical Society
LanguageEnglish
Peer reviewedYes
NRC numberNRC-NANO-097
Export citationExport as RIS
Report a correctionReport a correction
Record identifierb72daeb7-ed50-42e7-a51b-df1e7ab6cde5
Record created2021-02-22
Record modified2021-02-22
Report a problem on this page
Date modified: