Combined Machine Learning and Molecular Modelling Workflow for the Recognition of Potentially Novel Fungicides

Abstract

Novel machine learning and molecular modelling filtering procedures for drug repurposing have been carried out for the recognition of the novel fungicide targets of Cyp51 and Erg2. Classification and regression approaches on molecular descriptors have been performed using stepwise multilinear regression (FS-MLR), uninformative-variable elimination partial-least square regression, and a non-linear method called Forward Stepwise Limited Correlation Random Forest (FS-LM-RF). Altogether, 112 prediction models from two different approaches have been built for the descriptor recognition of fungicide hit compounds. Aiming at the fungal targets of sterol biosynthesis in membranes, antifungal hit compounds have been selected for docking experiments from the Drugbank database using the Autodock4 molecular docking program. The results were verified by Gold Protein-Ligand Docking Software. The best-docked conformation, for each high-scored ligand considered, was submitted to quantum mechanics/molecular mechanics (QM/MM) gradient optimization with final single point calculations taking into account both the basis set superposition error and thermal corrections (with frequency calculations). Finally, seven Drugbank lead compounds were selected based on their high QM/MM scores for the Cyp51 target, and three were selected for the Erg2 target. These lead compounds could be recommended for further in vitro studies.

Antimicrobial activity and molecular docking screening of bioactive components of Antirrhinum majus (snapdragon) aerial parts

“…Finally, terpenes ( T01-T10 ) did not interact well with the enzyme, judging by their relatively higher LEB values and lack of hydrogen bond interactions. Fluconazole, the positive control, a known inhibitor of sterol 14-demethylase had a LEB of -7.12 kcal mol − 1 , which agrees with the previous finding ( Jović and Šmuc, 2020 ), and was able to form hydrogen bonds with HIS377, SER378, PHE380, MET508.…”

“…Furthermore, high temperature during the hot-reflux process most likely deteriorated or altered some phytochemicals (San Chang et al, 2013;Alabri et al, 2014), which eventually affected the phenolic content of the extracts. The higher total phenolic content of the flowers is probably due to the presence of anthocyanins, carotenoids, and aurones that do not commonly exist in the leaves (Kaufmann and El Baya, 1969;Gonz alez-Barrio et al, 2018). Although there is no difference in the qualitative phytochemical variation between the flowers and the leaves, there are apparent quantitative differences that contributed to the total phenolic content.…”

In Vitro Confirmation of Siramesine as a Novel Antifungal Agent with In Silico Lead Proposals of Structurally Related Antifungals

“…The latter was the aim of our present study. Based on our previous study [7], we carried out drug repurposing of the Drugbank database in search of novel antifungals. Since there are many antifungals with varying modes of action, the focus was on inhibitors of sterol biosynthesis in membranes.…”