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Fragment-Level Graph Encoding for Interpretable Molecular Property Prediction

Rafael Lopes Almeida, Ina Pöhner · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29589 · cited by 0 works (OpenAlex) · open-access PDF

French

Les réseaux neuronaux graphiques simples (GNN) sont parmi les outils standard pour la prédiction des propriétés moléculaires, mais les explications au niveau atomique de leurs prédictions sont difficiles pour les chimistes médicaux à agir. Nous proposons un changement de représentation d'entrée plutôt qu'un nouveau modèle: les molécules sont codées en tant que graphiques au niveau des fragments de la décomposition des BRICS, où chaque nœud est un fragment synthétiquement significatif dont les caractéristiques sont la somme stable des caractéristiques de ses atomes. Parce que seul le changement de représentation, tout message-passant GNN et toute méthode d'attribution post-hoc produit des explications à la résolution des groupes fonctionnels au lieu d'atomes

Machine translation (Meta M2M100); the English original follows.

Abstract

Graph neural networks (GNNs) are among the standard tools for molecular property prediction, but the atom-level explanations of their predictions are hard for medicinal chemists to act on. We propose a change of input representation rather than a new model: molecules are encoded as fragment-level graphs from BRICS decomposition, where each node is a synthetically meaningful fragment whose features are the element-wise sum of its atoms' features. Because only the representation changes, any message-passing GNN and any post-hoc attribution method produces explanations at the resolution of functional groups instead of single atoms. Across four architectures, four prediction tasks, and two explainers under scaffold splits, fragment-level encoding matches atom-level test accuracy on roughly five-fold smaller graphs. Integrated-Gradients attributions recover known pharmacophores above chance on two enzyme targets, namely BACE at 1.5 times the chance rate, and PTR1 at 3.2 times. A like-for-like comparison against an aggregated atom-level baseline shows that post-hoc aggregation is not a stable substitute: its BACE hit rate range both exceeds and falls below the fragment model's hit rate depending on two arbitrary aggregation choices, whereas fragment-level training yields a single convention-free attribution at no predictive cost. The code is available at: https://github.com/4D-Lab/frame

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Topics: Property (philosophy), Fragment (logic), Encoding (memory), Computer science, Representation (politics), Attribution

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