Computational modelling of some phenolic diterpenoid compounds as anti-influenza A virus agents

Influenza A virus (IAV) infection is a contagious respiratory disease that causes many deaths due to the advent of drug-resistant strains in recent times. This led to the quest for the in-silico identification of potential hit scaffolds as anti-influenza A agents. In this re-search, two quantitative structure-activity relationships (QSAR) modelling approaches were performed to relate the molecular descriptors of some phenolic diterpenoids based on their 2D and 3D structural representations with their anti-IAV activities. Subsequently, molecular docking simulation and ADMET evaluations of the compounds were performed to virtually screen and identify the best hits accordingly. The genetic function approxi-mation (GFA) based linear and non-linear regression such as multiple linear regression (MLR) and artificial neural network (ANN) regression models were built in the 2D-QSAR modelling, and the results showed GFA-MLR (R2 train = 0.9102, Q 2 = 0.8701) and GFA-ANN (R2 train= 0.9215, Q 2 = 0.9216) models for predicting the anti-IAV activities of the com-pounds which have passed the global criteria of accepting QSAR models. The 3D-QSAR modelling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA), and the results revealed CoMFA_ES (R2 train = 0.948, Q 2 = 0.590) and CoMSIA_EDH (R2 train = 0.980, Q 2 = 0.754) models for re-liable predictions of anti-IAV activities. The compounds were also virtually screened based on their binding scores through molecular docking with an active site of human hemag-glutinin (HA) target which confirms their resilient potency. Furthermore, the drug-likeness and ADMET predictions of the compounds showed the non-violation of Lipinski's rule and good ADMET profiles as part of the rational strategy for future in-silico drug design and discovery. The outcome of this research provides theoretical support to affirm the rele-vance of totarol as a promising scaffold and set a route for the in-silico design of new diterpenoid inhibitors with improved potency.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of African Institute of Mathematical Sciences / Next Einstein Initiative.This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )