Among the biomedical efforts in response to the current coronavirus (
COVID-19 MESHD) 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 PROTEIN (also called
Mpro PROTEIN), 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 PROTEIN, as well as by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics simulation, machine learning and in vitro experimental validation analyses which have led to the identification of small molecule inhibitors of
3CLpro PROTEIN with micromolar activity, and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the
3CLpro PROTEIN binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 mcM), and synthetic compounds previously not characterized (e.g. compound CID 46897844, IC50 = 31 mcM). In combination with the developed pharmacophore model, these and other confirmed
3CLpro PROTEIN inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts, to identify
3CLpro PROTEIN ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, molecular dynamics simulations and machine learning can facilitate
3CLpro PROTEIN-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 identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small molecule inhibitors for
3CLpro PROTEIN provide resources to support follow-up computational screening efforts for this drug target.