Newest papers

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

German

Graph-Neural-Netzwerke (GNNs) gehören zu den Standard-Tools für molekulare Eigenschaften Vorhersage, aber die Atom-Ebene Erklärungen ihrer Vorhersagen sind schwierig für medizinische Chemiker zu handeln. Wir schlagen eine Änderung der Eingabe-Repräsentation anstelle eines neuen Modells vor: Moleküle werden als Fragment-Ebene-Diagramme von der BRICS-Zersetzung codiert, wo jeder Knoten ist ein synthetisch bedeutendes Fragment, dessen Merkmale sind die elementarische Summe der Eigenschaften seiner Atome. Weil nur die Repräsentation Änderungen, jede Nachricht-passende GNN und jede post-hoc Attributions-Methode Erklärungen bei der Auflösung von Funktionsgruppen anstelle von einzelnen Atomen

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

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

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

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange