Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models

Quantitative structure–property
relationships
(QSPRs) are
important tools to facilitate and accelerate the discovery of compounds
with desired properties. While many QSPRs have been developed, they
are associated with various shortcomings such as a lack of generalizability
and modest accuracy. Albeit various machine-learning and deep-learning
techniques have been integrated into such models, another shortcoming
has emerged in the form of a lack of transparency and interpretability
of such models. In this work, two interpretable graph neural network
(GNN) models (attentive group-contribution (AGC) and group-contribution-based
graph attention (GroupGAT)) are developed by integrating fundamentals
using the concept of group contributions (GC). The interpretability
consists of highlighting the substructure with the highest attention
weights in the latent representation of the molecules using the attention
mechanism. The proposed models showcased better performance compared
to classical group-contribution models, as well as against various
other GNN models describing the aqueous solubility, melting point,
and enthalpies of formation, combustion, and fusion of organic compounds.
The insights provided are consistent with insights obtained from the
semiempirical GC models confirming that the proposed framework allows
highlighting the important substructures of the molecules for a specific
property.