Author(s): Machado, Inês ; Miguel Inácio, João ; Jorge, Paula ; Teixeira, Filipe
Date: 2023
Persistent ID: https://hdl.handle.net/1822/88517
Origin: RepositóriUM - Universidade do Minho
Subject(s): polymyxins; antimicrobial resistance; drug design; QSAR; machine learning
Antimicrobial resistance (AMR) is a silent pandemic that presents an urgent threat to human health. Recently, polymyxins have been revived as a last-line therapeutic option, despite their toxicity. As such, there is a need for fast and reliable approaches to devise novel polymyxin analogues. In this work, machine learning was employed to devise a semi-quantitative model of the activity of polymyxin-like molecules. Four learning algorithms and ten families of molecular descriptors were explored. Top performance was observed for an AdaBoost model using the Kier and Hall topological indexes, allowing for the exploration of the systematic changes in the structure of polymyxin B.
