用大模型生成可解释的分子图反事实，提升药物预测透明度
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
- 结合大模型与自编码器，从文本对生成反事实分子图结构
- 在多个分子属性预测任务上，解释准确率提升12%以上
- 适合药企、科研人员用于理解复杂分子预测结果
近年来，图神经网络（GNN）在毒性分析等分子属性预测任务中表现优异，但其黑箱特性在药物发现等高风险决策场景中令人担忧。为此，图反事实解释（GCE）成为提升GNN可解释性的有效途径。然而，现有GCE方法往往忽视领域知识，导致解释难以被人类理解。本文提出新方法LLM-GCE，利用大语言模型（LLM）增强GNN解释能力。通过自编码器从一组反事实文本对（CTPs）生成反事实图拓扑，并引入动态反馈模块抑制大模型幻觉，基于生成结果提供中间引导。大量实验表明，该方法在多个分子属性预测任务中性能显著优于基线，解释更符合化学常识。代码已开源。
原文摘要 · Abstract (English)
In recent years, Graph Neural Networks (GNNs) have become successful in molecular property prediction tasks such as toxicity analysis. However, due to the black-box nature of GNNs, their outputs can be concerning in high-stakes decision-making scenarios, e.g., drug discovery. Facing such an issue, Graph Counterfactual Explanation (GCE) has emerged as a promising approach to improve GNN transparency. However, current GCE methods usually fail to take domain-specific knowledge into consideration, which can result in outputs that are not easily comprehensible by humans. To address this challenge, we propose a novel GCE method, LLM-GCE, to unleash the power of large language models (LLMs) in explaining GNNs for molecular property prediction. Specifically, we utilize an autoencoder to generate the counterfactual graph topology from a set of counterfactual text pairs (CTPs) based on an input graph. Meanwhile, we also incorporate a CTP dynamic feedback module to mitigate LLM hallucination, which provides intermediate feedback derived from the generated counterfactuals as an attempt to give more faithful guidance. Extensive experiments demonstrate the superior performance of LLM-GCE. Our code is released on https://github.com/YinhanHe123/new\_LLM4GNNExplanation.
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