Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates

Abstract

Bacterial infections are increasingly problematic due to the rise of antimicrobial resistance. Consequently, the rational design of materials naturally resistant to biofilm formation is an important strategy for preventing medical device-associated infections. Machine learning (ML) is a powerful method to find useful patterns in complex data from a wide range of fields. Recent reports showed how ML can reveal strong relationships between bacterial adhesion and the physicochemical properties of polyacrylate libraries. These studies used robust and predictive nonlinear regression methods that had better quantitative prediction power than linear models. However, as nonlinear models' feature importance is a local rather than global property, these models were hard to interpret and provided limited insight into the molecular details of material−bacteria interactions. Here, we show that the use of interpretable mass spectral molecular ions and chemoinformatic descriptors and a linear binary classification model of attachment of three common nosocomial pathogens to a library of polyacrylates can provide improved guidance for the design of more effective pathogen-resistant coatings. Relevant features from each model were analyzed and correlated with easily interpretable chemoinformatic descriptors to derive a small set of rules that give model features tangible meaning that elucidate relationships between the structure and function. The results show that the attachment of Pseudomonas aeruginosa and Staphylococcus aureus can be robustly predicted by chemoinformatic descriptors, suggesting that the obtained models can predict the attachment response to polyacrylates to identify anti-attachment materials to synthesize and test in the future.

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

“…Machine learning methods are well established in drug design, and they can also guide the design of pathogen-resistant coatings naturally resistant to biofilm formation [252]. Furthermore, machine learning methods have been utilised to predict the investigation of pathogen attachment to coating polymers for biomedical devices, thus guiding decisions towards the development of devices with less potential risk of complications [253].…”

DeepGEEP: Data-Driven Prediction of Bacterial Biofilm Gene Expression Profiles

“…In biotechnology and industrial applications, biofilms are used for various purposes, such as biofuel production [7]. Predicting the GEEP can guide the design and optimization of bioprocesses involving biofilm formation [8]. Also, engineers and researchers working in synthetic biology can use the predicted GEEPs to design and engineer biofilms with specific behaviors or responses to different environmental stimuli.…”