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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

Japanese

グラフ神経ネットワーク(GNNs)は、分子属性予測の標準ツールの1つですが、その予測の原子レベルの説明は、医薬品化学者が行動するために困難です。 我々は、新しいモデルの代わりに入力表示の変更を提案します:分子は、BRICS分解から分割レベルのグラフとして暗号化され、各ノードは合成的に有意義な断片であり、その特徴は、その原子の特徴の要素の合計である。 なぜならば、表示の変化は、メッセージを通過するGNNおよびすべてのポストホック属性付与方法は、単一の原子の代わりに機能グループの解像度で解釈を生成します。 4つのアーキテクチャを通して、4つの予

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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