Speeding up Early Drug Discovery in Antiviral Research: A Fragment-Based in Silico Approach for the Design of Virtual Anti-Hepatitis C Leads

Hepatitis C constitutes
an unresolved global health problem. This
infectious disease is caused by the hepatotropic hepatitis C virus
(HCV), and it can lead to the occurrence of life-threatening medical
conditions, such as cirrhosis and liver cancer. Nowadays, major clinical
concerns have arisen because of the appearance of multidrug resistance
(MDR) and the side effects especially associated with long-term treatments.
In this work, we report the first multitasking model for quantitative
structure-biological effect relationships (mtk-QSBER), focused on
the simultaneous exploration of anti-HCV activity and in vitro safety
profiles related to the absorption, distribution, metabolism, elimination,
and toxicity (ADMET). The mtk-QSBER model was created from a data
set formed by 40 158 cases, displaying accuracy higher than
95% in both training and prediction (test) sets. Several molecular
fragments were selected, and their quantitative contributions to anti-HCV
activity and ADMET profiles were calculated. By combining the analysis
of the fragments with positive contributions and the physicochemical
meanings of the different molecular descriptors in the mtk-QSBER,
six new molecules were designed. These new molecules were predicted
to exhibit potent anti-HCV activity and desirable in vitro ADMET properties.
In addition, the designed molecules have good druglikeness according
to the Lipinski’s rule of five and its variants.