Interpretable Molecular Property Predictions Using Marginalized Graph Kernels

Abstract

Marginalized graph kernels have shown competitive performance in molecular machine learning tasks but currently lack measures of interpretability, which are important to improve trust in the models, detect biases, and inform molecular optimization campaigns. We here conceive and implement two interpretability measures for Gaussian process regression using a marginalized graph kernel (GPR-MGK) to quantify (1) the contribution of specific training data to the prediction and (2) the contribution of specific nodes of the graph to the prediction. We demonstrate the applicability of these interpretability measures for molecular property prediction. We compare GPR-MGK to graph neural networks on four logic and two real-world toxicology data sets and find that the atomic attribution of GPR-MGK generally outperforms the atomic attribution of graph neural networks. We also perform a detailed molecular attribution analysis using the FreeSolv data set, showing how molecules in the training set influence machine learning predictions and why Morgan fingerprints perform poorly on this data set. This is the first systematic examination of the interpretability of GPR-MGK and thereby is an important step in the further maturation of marginalized graph kernel methods for interpretable molecular predictions.

Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure–Activity Relationships

“…Interpretation approaches can be classified into two categories: global and local, corresponding to focusing on the prediction model itself and input instances, respectively. Various studies were reported on the local interpretation methods and associated applications, such as the identification of important substructures (atoms) in a molecule, − compound optimization , and finding a potential artifact of a model . On the other hand, methods for global interpretations are limited.…”