Pharmacophore Model for SARS-CoV-2 3CLpro Small-Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors

Enrico Glaab*, Ganesh Babu Manoharan, Daniel Abankwa

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

20 Citations (Scopus)


Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic, and steric features that characterize small-molecule inhibitors binding stably to 3CLpro and by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics (MD) simulation, machine learning, and in vitro experimental validation analyses, which have led to the identification of small-molecule inhibitors of 3CLpro with micromolar activity and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with the available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 μM) and synthetic compounds previously not characterized (e.g., compound CID 46897844, IC50 = 31 μM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts to identify 3CLpro ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, MD simulations, and machine learning can facilitate 3CLpro-targeted small-molecule screening investigations. Different receptor-, ligand-, and machine learning-based screening strategies provided complementary information, helping to increase the number and diversity of the identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small-molecule inhibitors for 3CLpro provide resources to support follow-up computational screening efforts for this drug target.

Original languageEnglish
Pages (from-to)4082-4096
Number of pages15
JournalJournal of Chemical Information and Modeling
Issue number8
Publication statusPublished - 23 Aug 2021
Externally publishedYes


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