Multi-reference Computational Method for De-novo Design, Optimization, and Repositioning of Pharmaceutical Compounds Illustrated by Identifying Multi-target SARS-CoV-2 Ligands

In this work a novel computational multi-reference poly-conformational algorithm is presented for design, optimization, and repositioning of pharmaceutical compounds. The algorithm searches for candidates by comparing similarities between conformers of the same compound and identifies target compounds whose conformers are simultaneously “close” to the conformers for each of the compounds in a reference set. The reference compounds can have very different MoAs, which directly and simultaneously shapes the properties of the target candidate compounds. The algorithm functionality has been validated in silico by scoring ChEMBL drugs against FDA-approved reference compounds which either had the highest predicted binding affinity to our chosen SARS-COV-2 targets or confirmed to be inhibiting such targets in-vivo. All our top scoring ChEMBL compounds also turned out to be either high-affinity ligands to the chosen targets (as confirmed separately in other studies) or showing significant efficacy in-vivo against those selected targets.In addition to method validation in silico search for new compounds within two virtual libraries from the Enamine database is presented. The library’s virtual compounds have been compared to the same set of reference drugs that we used for validation: Olaparib, Tadalafil, Ergotamine and Remdesivir. The large reference set of four potential SARS-CoV-2 compounds have been selected, since no drug has been identified to be 100% effective against the virus so far, possibly because each candidate drug was targeting only one particular MoA. The goal here was to introduce methodology for identifying potential candidate(s) that cover multiple MoA-s presented within a set of reference compounds.