Preprint Article Version 1 This version is not peer-reviewed

Machine Learning for the Design and Development of Biofilm Regulators

Version 1 : Received: 14 March 2018 / Approved: 15 March 2018 / Online: 15 March 2018 (06:37:58 CET)

How to cite: Stone, B.; Sapper, E. Machine Learning for the Design and Development of Biofilm Regulators. Preprints 2018, 2018030118 (doi: 10.20944/preprints201803.0118.v1). Stone, B.; Sapper, E. Machine Learning for the Design and Development of Biofilm Regulators. Preprints 2018, 2018030118 (doi: 10.20944/preprints201803.0118.v1).

Abstract

Biofilms are congregations of bacteria on a surface, and they grow into obstacles for the functionalities of any device or machinery involves anything biological. Biofilms are developed through a biochemical system known as ‘Quorum Sensing’ that accounts for the chemical signaling that direct either biofilm formation or inhibition. Computational models that relate chemical and structural features of compounds to their performance properties have been used to aide in the discovery of active small molecules for many decades. These quantitative structure-activity relationship (QSAR) models are also important for predicting the activity of molecules that can have a range of effectiveness in biological systems. This study uses QSAR methodologies combined with and different machine learning algorithms to predict and assess the performance of several different compounds acting in Quorum Sensing. Through computational probing of the quorum sensing molecular interaction, new design rules can be elucidated for countering biofilms.

Subject Areas

machine learning; biochemistry; QSAR; molecules; neural networks

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