Prime Implicant Explanations for Reaction Feasibility Prediction
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.
Cite
@article{arxiv.2510.09226,
title = {Prime Implicant Explanations for Reaction Feasibility Prediction},
author = {Klaus Weinbauer and Tieu-Long Phan and Peter F. Stadler and Thomas Gärtner and Sagar Malhotra},
journal= {arXiv preprint arXiv:2510.09226},
year = {2025}
}
Comments
Presented at AIMLAI workshop at ECMLPKDD 2025