MolLedger用原子级分数解释药物分子预测，比传统方法更符合化学特性，药物研发人员值得关注。
MolLedger是一种新型图神经网络架构，专为小分子药物发现中的ADME属性预测设计。该模型通过原子级分数求和输出预测，在保持性能的同时提供完全可解释性。研究显示，MolLedger的归因方法比其他解释技术更符合化学特性，能更合理地解释预测属性变化。
MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions
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.