Prime Implicant Explanations for Reaction Feasibility Prediction
Klaus Weinbauer, Tieu-Long Phan, Peter F. Stadler, Thomas Gärtner, Sagar Malhotra
TL;DR
The paper tackles the opacity of RF predictions in computer-aided synthesis planning by introducing formally grounded prime implicant (PI) explanations tailored to reaction graphs encoded as Imaginary Transition State (ITS) graphs. It defines PI reaction explanations as minimally sufficient rooted connected subgraphs containing the reaction center, and leverages an extension DAG to systematically enumerate and prune subgraphs during explanation search. Experiments with a Graph Isomorphism Network on USPTO-derived data show that PI explanations tend to capture core mechanistic factors but often include extra nonessential elements, highlighting both interpretability potential and limitations. The work discusses computational intractability, lack of benchmarks, and directions for improving explanatory quality and efficiency for practical CASP applications.
Abstract
Machine learning models that predict the feasibility of chemical reactions have become central to automated synthesis planning. Despite their predictive success, these models often lack transparency and interpretability. We introduce a novel formulation of prime implicant explanations--also known as minimally sufficient reasons--tailored to this domain, and propose an algorithm for computing such explanations in small-scale reaction prediction tasks. Preliminary experiments demonstrate that our notion of prime implicant explanations conservatively captures the ground truth explanations. That is, such explanations often contain redundant bonds and atoms but consistently capture the molecular attributes that are essential for predicting reaction feasibility.
