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

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.

Figures (6)

• Figure 1: The schematic workflow for Molecular Olfactorial Receptor Engineering by Quantum mechanics (MORE-Q)more-q dataset expansion to MORE-QX dataset. (a) The bio-electronic noses (top right panel) are designed as an electronic equivalent to the olfactory system (top left panel), e.g., for sensing BOV molecules (or odorant molecules, OM). (b) The building blocks at different design stages for the bio-mimetic sensor from QM perspective including OM molecules, molecular receptor (REC), OM-REC dimer molecule (DM), REC-graphene substrate (SUB), OM-REC-graphene complex system (CPLX) and eventually these systems deposited on the gold electrode (mCPLX). These abbreviations are used throughout this manuscript. (c) The QM properties of the relevant building blocks were calculated and incorporated into the MORE-Q dataset, which includes monomer systems of 102 OM and 18 REC molecules, 23,838 DM systems, and 1,836 CPLX systems derived from the most stable DM configurations. Sampling multiple low-energy DM conformers expanded the CPLX subset, yielding the MORE-QX dataset with 10,411 CPLX systems. (d) 2D projection of the high-dimensional MORE-QX property space defined by the work function change $\Delta \phi$ and DM interaction energy $E_{\rm int}$. The conformers of two DM systems (red and blue) are labeled, where the most stable one (MORE-Q) is marked as star while the other low-energy conformers (MORE-QX) are marked with circles. The atomic structure associated to the maximal and minimal values are depicted on the plot for each DM system.
• Figure 2: (a) Two-dimensional (2D) projections of the high-dimensional property space spanned by MORE-QX dataset. We show the correlation plots for seven dimer properties (DP, brown) and three binding features (BF, purple) from MORE-QX. The detailed description of the properties can be viewed in Table \ref{['ORCA-table']}. Some interesting projections are marked by yellow frames and discussed in the manuscript. (b) The count measurement of absolute value of Spearman correlation coefficient $|\rho_s|$ for DP (upper panel) and BF (lower panel) 2D projections. The $|\rho_s|$ values result in three distinct clusters: weakly correlated $|\rho_s| \leq 0.5$, moderately correlated $0.5 < |\rho_s| \leq 0.8$, and strongly correlated $|\rho_s| > 0.8$ covering by blue, gray, red frames, respectively.
• Figure 3: (a) Scheme of the MORE-ML framework, which stands for Molecular Olfactorial Receptor Engineering by Machine Learning, which integrates QM properties of molecular building blocks ($\rm D_{ele}$) with ML techniques for the prediction of binding features (BFs) such as $E_{\rm ads}$, $\Delta \phi$ and $\Delta Q$. MORE-ML framework aims at regression and model explanation tasks. (b) ML model training in MORE-ML starts with anomaly detection (see SI), followed by farthest point sampling (see Methods) to construct the training and test sets. Bayesian optimization with 100 iterations and 10-fold cross-validation on the training set is used for hyperparameter tuning. Final model performance is evaluated on the test set.
• Figure 4: Model benchmarking and feature engineering for the prediction of adsorption energy ($E_{\rm ads}$). (a) Coefficient of determination ($R^2$) and mean absolute error (MAE) evaluated on the test (TE) set for tree-based models: random forest (RF), gradient boosting decision tree (GB), LightGBM (LGBM), CatBoost (CAT), and XGBoost (XGB). The best-performing model is indicated by red stars. (b) Evolution of $R^2$ (upper panel) and MAE (lower panel) during feature engineering (FE) of the CatBoost model for predicting $E_{\rm ads}$. The number of features is increased incrementally in steps of five, ranked by SHAP analysis (see Methods). Model performance is shown for the training (TR, black) and test (TE, brown) sets. Dashed lines indicate performance obtained using the full set of QM properties as features.
• Figure 5: Correlation plots between DFT calculated and ML predicted values are shown for the best-performing models used to predict (a) $E_{\rm ads}$, (b) $\Delta \phi$, and (c) $\Delta Q$. Orange and blue bars/points represent the training (TR) and test (TE) sets, respectively. The lateral panels display the distributions for each binding feature. Panels (d–f) show the corresponding SHAP beeswarm plots (see Methods) for (d) $E_{\rm ads}$, (e) $\Delta \phi$, and (f) $\Delta Q$. In each beeswarm plot, features are ranked in ascending order of importance from top to bottom, with SHAP values distributed around the zero baseline. Each point is colored according to the corresponding feature value. Only the top nine features are shown; the cumulative SHAP value of all remaining features is reported in the final column ($10^{\rm th}$ position).