The system of self-consistent semi-correlations as one of the tools of cheminformatics for designing antiviral drugs. (27th October 2021)

• Record Type: Journal Article
• Authors: Toropov, Andrey A.; Toropova, Alla P.; Roncaglioni, Alessandra; Benfenati, Emilio
• Is Part Of: New journal of chemistry. Volume 45:Number 44(2021)
• Page Start: 20713
• Page End: 20720
• Publication Date: 2021-10-27
• Subjects: Chemistry -- Periodicals; Chimie -- Périodiques; 540
• DOI: 10.1039/d1nj03394h
• Languages: English
• ISSNs: 1144-0546

Abstract

Abstract : The development of antiviral agents against SARS-CoV-2 is necessary. Abstract : The development of antiviral agents against SARS-CoV-2 is necessary. Specific drugs for SARS-CoV-2 are not available. In such circumstances, the computational methods of drug discovery can be an attractive addition to usual experiments for drug discovery. Here, the so-called system of semi-correlations was applied as a tool to build up predictive models for biological activity. The semi-correlation system has one traditional continuous variable and a second variable with two values: 1 for active compounds, and 0 for inactive compounds. Therefore, the semi-correlation system is a tool for building a categorical (active/inactive) model for biological activity. The semi-correlation system used to build models for providing antiviral effects for SARS-CoV-2 inhibitors has demonstrated adherence to statistical norms. For the validation set, the best model has a Matthew correlation coefficient of 0.95. Checking the predictive potential of the models built with random splits confirms that most of them exhibit quite satisfactory statistical quality.