Recommender Systems in Antiviral Drug Discovery

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.