Desirability-based multi-criteria virtual screening of selective antimicrobial cyclic β-hairpin cationic peptidomimetics.

Today, emerging and increasing resistance to antibiotics has become a threat to public health worldwide. Antimicrobial peptides own unique action mechanisms making peptide antibiotics an attractive therapeutic option against resistant bacteria. However, their high haemolytic activity lacks the selectivity required for a human antibiotic. Therefore, additional efforts are needed to develop new antimicrobial peptides that possess greater selectivity for bacterial cells over erythrocytes. In this article, we introduce a chemoinformatics approach to simultaneously deal with these two conflicting properties consisting on a multi-criteria virtual screening strategy based on the use of a desirability-based multi-criteria classifier combined with similarity and chemometrics concepts. Here we propose a new quantitative feature encoding information related to the desirability, the degree of credibility ascribed to this desirability and the similarity of a candidate to a highly desirable query, which can be used as ranking criterion in a virtual screening campaign, the Desirability-Credibility- Similarity (DCS) Score. The enrichment ability of a multi-criteria virtual screening strategy based on the use of the DCS Score it is also assessed and compared to other virtual screening options. The results obtained evidenced that the use of the DCS score seems to be an efficient virtual screening strategy rendering promising overall and initial enrichment performance. Specifically, by using the DCS score it was possible to rank a selective antibacterial peptidomimetic earlier than a biologically inactive or non selective antibacterial peptidomimetic with a probability of ca. 0.9.