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 mol

SUBMITTER: Jovic O 

PROVIDER: S-EPMC7249108 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature