  Citation: Brogi, S.; Quimque, M.T.; Notarte, K.I.; Africa, J.G.; Hernandez, J.B.; Tan, S.M.; Calderone, V.; Macabeo, A.P. Virtual Combinatorial Library Screening of Quinadoline B Derivatives against SARS-CoV-2 RNA-Dependent RNA Polymerase. Computation 2022, 10, 7. https:// doi.org/10.3390/computation 10010007 Academic Editor: Rainer Breitling Received: 13 December 2021 Accepted: 10 January 2022 Published: 12 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). computation Article Virtual Combinatorial Library Screening of Quinadoline B Derivatives against SARS-CoV-2 RNA-Dependent RNA Polymerase Simone Brogi 1, * , Mark Tristan Quimque 2,3,4 , Kin Israel Notarte 5 , Jeremiah Gabriel Africa 2 , Jenina Beatriz Hernandez 2 , Sophia Morgan Tan 6 , Vincenzo Calderone 1 and Allan Patrick Macabeo 2, * 1 Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy; vincenzo.calderone@unipi.it 2 Laboratory for Organic Reactivity, Discovery and Synthesis (LORDS), Research Center for the Natural and Applied Sciences, University of Santo Tomas, España Blvd., Manila 1015, Philippines; mjtquimque@gmail.com (M.T.Q.); jeremiahgabriel.africa.sci@ust.edu.ph (J.G.A.); jeninabeatriz.hernandez.sci@ust.edu.ph (J.B.H.) 3 The Graduate School, University of Santo Tomas, España Blvd., Manila 1015, Philippines 4 Chemistry Department, College of Science and Mathematics, Mindanao State University—Iligan Institute of Technology, Tibanga, Iligan City 9200, Philippines 5 Faculty of Medicine and Surgery, University of Santo Tomas, Espana Blvd., Manila 1015, Philippines; kinotarte@gmail.com 6 Department of Biological Sciences, College of Science, University of Santo Tomas, España Blvd., Manila 1015, Philippines; sophiamorgan.tan.sci@ust.edu.ph * Correspondence: simone.brogi@unipi.it (S.B.); agmacabeo@ust.edu.ph (A.P.M.) Abstract: The unprecedented global health threat of SARS-CoV-2 has sparked a continued interest in discovering novel anti-COVID-19 agents. To this end, we present here a computer-based protocol for identifying potential compounds targeting RNA-dependent RNA polymerase (RdRp). Starting from our previous study wherein, using a virtual screening campaign, we identified a fumiquinazolinone alkaloid quinadoline B (Q3), an antiviral fungal metabolite with significant activity against SARS- CoV-2 RdRp, we applied in silico combinatorial methodologies for generating and screening a library of anti-SARS-CoV-2 candidates with strong in silico affinity for RdRp. For this study, the quinadoline pharmacophore was subjected to structural iteration, obtaining a Q3-focused library of over 900,000 unique structures. This chemical library was explored to identify binders of RdRp with greater affinity with respect to the starting compound Q3. Coupling this approach with the evaluation of physchem profile, we found 26 compounds with significant affinities for the RdRp binding site. Moreover, top-ranked compounds were submitted to molecular dynamics to evaluate the stability of the systems during a selected time, and to deeply investigate the binding mode of the most promising derivatives. Among the generated structures, five compounds, obtained by inserting nucleotide- like scaffolds (1, 2, and 5), heterocyclic thiazolyl benzamide moiety (compound 3), and a peptide residue (compound 4), exhibited enhanced binding affinity for SARS-CoV-2 RdRp, deserving further investigation as possible antiviral agents. Remarkably, the presented in silico procedure provides a useful computational procedure for hit-to-lead optimization, having implications in anti-SARS-CoV-2 drug discovery and in general in the drug optimization process. Keywords: quinadoline B; SARS-CoV-2; RNA-dependent RNA polymerase inhibitors; virtual screen- ing; combinatorial screening; molecular dynamics 1. Introduction The continued rise in COVID-19 cases worldwide despite the availability of vaccines sustains the demand to discover treatment and prophylactic regimens, particularly through natural products’ repurposing and design [1–3]. Computational strategies play a crucial role in accelerating the discovery of effective anti-SARS-CoV-2 agents [4–8], as in silico Computation 2022, 10, 7. https://doi.org/10.3390/computation10010007 https://www.mdpi.com/journal/computation