Technology Category
Reasoning under Uncertainty: Graphical ModelsMachine Learning: Probabilistic Circuits and Graphical ModelsKnowledge Representation and Reasoning: Computational Complexity of Reasoning
Prime Implicant Explanations for Reaction Feasibility Prediction
🤖 AI Summary
To address the limited interpretability of machine learning models in chemical reaction feasibility prediction, this work introduces prime implicant explanations—novel to this domain—and defines a minimal sufficient reason tailored to molecular graph structures. Our method integrates symbolic computation with subgraph analysis, modeling atom- and bond-level feature dependencies via chemical reaction rules to automatically extract the most concise critical substructures determining prediction outcomes. Experiments demonstrate stable identification of core structural features governing reaction feasibility; the explanations are chemically meaningful and conservatively faithful. Validated on small-scale tasks, the approach achieves both high interpretability and competitive predictive accuracy. Key contributions are: (1) the first prime implicant explanation framework for reaction feasibility prediction; (2) a formally grounded definition of minimal sufficient reason adapted to molecular graphs; and (3) a principled trade-off between model transparency and predictive performance.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Predicting chemical reaction feasibility lacks transparency and interpretability in models
Introducing prime implicant explanations tailored for reaction prediction tasks
Capturing essential molecular attributes while identifying redundant bonds and atoms
Innovation
Methods, ideas, or system contributions that make the work stand out.
Prime implicant explanations tailored for chemical reactions
Algorithm computes minimal sufficient reasons for predictions
Conservatively captures essential molecular attributes for feasibility
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