Exploring the antiviral activity of α-ketoamides compounds through electronic structure calculations: a structure-activity relationship study

Abstract

In recent years, the scientific community has worked intensively in the search and development of new drugs to suppress viral infections, such as COVID-19. In fact, a number of active compounds have been tested; however, the absence of significant structure-activity relationships hinders the production of optimized drugs. In this study, molecular modeling techniques were employed to investigate the electronic, structural and chemical reactivity properties of a set α-ketoamides whose antiviral activities have been reported in the literature, aiming to propose new promising derivatives. The local reactivity of the compounds was evaluated via condensed-to-atoms Fukui indexes and molecular electrostatic potential. Multivariate data analysis and random forests machine learning techniques were employed to correlate the antiviral properties and electronic and structural descriptors and identify relevant variables. A series of new derivatives were then proposed and evaluated via density functional theory-based calculations, and docking/molecular dynamics with the target protein of the virus. The results suggest that active derivatives present reduced reactivity towards electrophilic agents on the central core of the molecules and high reactivity on R¹ ligands. Derivatives with higher predicted antiviral activities were proposed based on simple electronic descriptors, and their efficacies are reinforced by docking and molecular dynamics simulations.

Communicated by Ramaswamy H. Sarma

Keywords:

• Antivirals
• reactivity indexes
• structure-activity relationships
• electronic structure calculations
• α-ketoamides

Acknowledgments

The authors thank the Brazilian National Council for Scientific and Technological Development (CNPq) (grant 420449/2018-3), the Coordination for the Improvement of Higher Education Personnel (CAPES) (proc. 88887.642942/2021-00 Capes-Proex), and the Brazilian Financial Agency for Studies and Projects (FINEP) (01.22.0292.00 (0122/21) - 3290/2022) for the financial support. This research was also supported by resources supplied by the Center for Scientific Computing (NCC/Grid-UNESP) of the São Paulo State University (UNESP).

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, ABN, upon reasonable request.

Author contributions

A. C. Deienno: Conceptualization, Formal analysis, Investigation, Writing - original draft. R. H. M. Gomes: Formal analysis, Investigation, Methodology, Writing - original draft. A. L. D. Rossi: Formal analysis, Investigation, Methodology, Writing - review & editing. R. P. Simões: Formal analysis, Investigation, Methodology, Writing - review & editing. A. Batagin-Neto: Conceptualization, Formal analysis, Funding acquisition, Methodology, Resources, Supervision, Writing - original draft, Writing - review & editing.

Additional information

Funding

This work was supported by the Brazilian National Council for Scientific and Technological Development (CNPq) (grant 420449/2018-3), the Coordination for the Improvement of Higher Education Personnel (CAPES) (proc. 88887.642942/2021-00 Capes-Proex), the Brazilian Financial Agency for Studies and Projects (FINEP) (01.22.0292.00 (0122/21) − 3290/2022) and the Center for Scientific Computing (NCC/Grid-UNESP) of the São Paulo State University (UNESP).