Development of Machine Learning Models for Ion-Selective Electrode Cation Sensor Design.

ABSTRACT: Polyvinyl chloride (PVC) membrane-based ion-selective electrode (ISE) sensors are common tools for water assessments, but their development relies on time-consuming and costly experimental investigations. To address this challenge, this study combines machine learning (ML), Morgan fingerprint, and Bayesian optimization technologies with experimental results to develop high-performance PVC-based ISE cation sensors. By using 1745 data sets collected from 20 years of literature, appropriate ML models are trained to enable accurate prediction and a deep understanding of the relationship between ISE components and sensor performance (R ² = 0.75). Rapid ionophore screening is achieved using the Morgan fingerprint based on atomic groups derived from ML model interpretation. Baye

SUBMITTER: Huang Y 

PROVIDER: S-EPMC11250033 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature