Recommender systems in antiviral drug discovery

Ekaterina A. Sosnina*, Sergey Sosnin, Anastasia A. Nikitina, Ivan Nazarov, Dmitry I. Osolodkin, Maxim V. Fedorov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
28 Downloads (Pure)

Abstract

Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: Collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes ("interactions") for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery.

Original languageEnglish
Pages (from-to)15039-15051
Number of pages13
JournalACS Omega
Volume5
Issue number25
Early online date21 Jun 2020
DOIs
Publication statusPublished - 30 Jun 2020

Funding

The authors acknowledge the usage of the Skoltech CDISE HPC clusters Arkuda and Zhores for obtaining the results presented in this manuscript. The authors are thankful to Maxim Panov and Evgeny Frolov from the Center for Computational and Data-Intensive Science and Engineering, Skoltech, for fruitful discussions. The reported study was funded by the Russian Foundation of Basic Research (according to the research project no. 19-33-90290, MVF and EAS—computational experiments and results assessment) and the State research funding for FSBSI “Chumakov FSC R&D IBP RAS” (topic no. 0837-2019-0002, AAN and DIO—database curation and data assessment).

Keywords

  • recommender systems
  • drug discovery
  • interactions
  • compound profiles

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