Discovery of RdRp Thumb-1 as a novel broad-spectrum antiviral family of targets and MDL-001 as a potent broad-spectrum inhibitor thereof - Part I: A Bioinformatics and Deep Learning Approach (preprint)

Positive sense, single-stranded RNA (+ssRNA) viruses consist of 12+ viral families that contain mild pathogens to pandemic-causing viruses like SARS-CoV-2, yet all share a vital and highly conserved RNA-dependent RNA polymerase (RdRp). While RdRp is the target of several viral inhibitors, the active site has several pitfalls when translating in vitro inhibitors to the clinic. The highly polar residues within the active site often necessitate the use of highly polar or charged compounds, especially when designing nucleoside analog inhibitors, posing significant challenges in optimizing drug-likeness and membrane permeability for clinical efficacy. Here, we investigated the broad-spectrum potential of the allosteric Thumb-1 cryptic site of the RdRp, which to date has only been adequately studied in Hepatitis C Virus (HCV). To explore this potential antiviral target, we used a suite of bioinformatics techniques, including homology modeling and multiple sequence alignments, to reveal the conserved landscape of the Thumb-1 site across +ssRNA viruses. We then used ChemPrint, our Mol-GDL (Molecular-Geometric Deep Learning) machine learning model to predict drug inhibition of the Thumb-1 site in RdRp across +ssRNA viruses. Here, we identify MDL-001 as a promising broad-spectrum antiviral candidate with favorable properties that enable oral and once-a-day dosing. We also show how the cryptic nature of the Thumb-1 site masks itself to conventional virtual screening techniques, like docking, where activity prediction is heavily based on solving or predicting an accurate structure of the open pocket. This study demonstrates the utility of this approach in drug discovery for broad-spectrum antivirals that target the Thumb-1 site.