新图神经网络可精准解释药物分子属性，原子级归因更符合化学原理。
MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions
- 采用加性框架，分子预测由原子贡献值相加得出，实现无需代价的精确可解释性。
- 通过化学属性辅助损失函数，使原子归因更贴近真实化学特性。
- 在分子对比案例中，解释结果比其他方法更合理，适合药物研发人员使用。
优化小分子药物的吸收、分布、代谢和排泄（ADME）是药物发现中的关键环节。尽管已有多种机器学习模型用于预测ADME性质以辅助优化，但模型预测的解释仍具挑战性。本文提出一种新型图神经网络架构MolLedger，其输出为各原子得分的累加，具备内置的原子级可解释性。该加性框架在不牺牲性能的前提下实现了精确可解释性，因为全局上下文向量为加性头提供了足够信息以生成高质量的原子得分。此外，通过引入辅助损失函数，MolLedger将原子得分锚定在化学属性上，使其归因更忠实于化学规律。对多个分子对的案例研究显示，与其他解释方法相比，MolLedger能生成更具化学合理性的预测变化解释。
原文摘要 · Abstract (English)
Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning models have been built to predict ADME properties to facilitate this optimization process, but explaining model predictions is challenging. We propose a new graph neural network architecture with built-in meaningful per-atom attributions. Our model MolLedger outputs predictions that are the sum of per-atom scores. MolLedger's additive framework obtains exact interpretability at no cost to performance because the global context vector gives the additive head enough context to produce good per-atom scores. Furthermore, MolLedger produces attributions that are more faithful to chemical properties than other interpretability methods because the auxiliary loss in MolLedger anchors the atom scores to chemical properties. Our case studies comparing interpretations from multiple methods on molecular pairs reveal that MolLedger is much better at producing sensible explanations for predicted property changes.
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