Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials

TL;DR

This paper introduces MORE-ML, a framework combining quantum-mechanical data and machine learning to predict sensing-relevant properties of molecules for artificial odor sensors, enhancing understanding and design of BOV sensing materials.

Contribution

The study develops a novel computational framework that integrates QM properties with ML models to predict electronic binding features, improving transferability and interpretability in BOV sensing.

Findings

CatBoost models outperform alternatives in predicting BFs.

Weak correlations found between QM properties and BFs.

Explainable AI identifies key QM properties influencing predictions.

Abstract

Digital sensing faces challenges in developing sustainable methods to extend the applicability of customized e-noses to complex body odor volatilome (BOV). To address this challenge, we developed MORE-ML, a computational framework that integrates quantum-mechanical (QM) property data of e-nose molecular building blocks with machine learning (ML) methods to predict sensing-relevant properties. Within this framework, we expanded our previous dataset, MORE-Q, to MORE-QX by sampling a larger conformational space of interactions between BOV molecules and mucin-derived receptors. This dataset provides extensive electronic binding features (BFs) computed upon BOV adsorption. Analysis of MORE-QX property space revealed weak correlations between QM properties of building blocks and resulting BFs. Leveraging this observation, we defined electronic descriptors of building blocks as inputs for tree-based ML models to predict BFs. Benchmarking showed CatBoost models outperform alternatives, especially in transferability to unseen compounds. Explainable AI methods further highlighted which QM properties most influence BF predictions. Collectively, MORE-ML combines QM insights with ML to provide mechanistic understanding and rational design principles for molecular receptors in BOV sensing. This approach establishes a foundation for advancing artificial sensing materials capable of analyzing complex odor mixtures, bridging the gap between molecular-level computations and practical e-nose applications.