Ligand-based discovery of coronavirus main protease inhibitors using MACAW molecular embeddings

Abstract

Ligand-based drug design methods are thought to require large experimental datasets to become useful for virtual screening. In this work, we propose a computational strategy to design novel inhibitors of coronavirus main protease, M^pro. The pipeline integrates publicly available screening and binding affinity data in a two-stage machine-learning model using the recent MACAW embeddings. Once trained, the model can be deployed to rapidly screen large libraries of molecules in silico. Several hundred thousand compounds were virtually screened and 10 of them were selected for experimental testing. From these 10 compounds, 8 showed a clear inhibitory effect on recombinant M^pro, with half-maximal inhibitory concentration values (IC₅₀) in the range 0.18–18.82 μM. Cellular assays were also conducted to evaluate cytotoxic, haemolytic, and antiviral properties. A promising lead compound against coronavirus M^pro was identified with dose-dependent inhibition of virus infectivity and minimal toxicity on human MRC-5 cells.

Keywords:

• Coronavirus
• ligand-based drug design
• machine learning
• cheminformatics
• drug discovery

Acknowledgements

The authors thank Dr. Alejandro Cabrera-García (Universidad de La Laguna, Spain), Prof. Miguel Ángel González-Cardenete (Instituto de Tecnología Química, UPV-CSIC, Spain), Dr. Ariadna Montero-Blay and Prof. Luis Serrano (Centre for Genomic Regulation, Spain), and Dr. Nuria Izquierdo-Useros and Prof. Bonaventura Clotet (IrsiCaixa – Institute for AIDS Research, Spain) for helpful discussions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was financially supported by grants from the Project of Innovation-driven Plan in Central South University. The authors gratefully acknowledge the University of Lorraine, CNRS and the FEDER-FSE “Fire Light”.