English(EN) MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions

MolLedger：新的GNN提高了药物发现的可解释性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了MolLedger，这是一种新颖的图神经网络架构，旨在提高小分子药物发现中预测的可解释性。该模型提供内置的、每原子的归因，从而更清晰地理解分子特性如何影响ADME（吸收、分布、代谢和排泄）预测。MolLedger的加性框架在不牺牲性能的情况下实现了精确的可解释性，并且辅助损失函数将原子分数锚定到化学性质上，与现有方法相比，产生了更符合化学原理的解释。 AI

影响增强了药物发现的可解释性，有望加速新型小分子疗法的识别。

排序理由该集群包含一篇详细介绍特定科学领域新模型架构的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Christina X. Ji · 2026-09-01 04:00

MolLedger: 一种具有化学基础ADME归因的加性图神经网络

arXiv:2608.30636v1 Announce Type: new Abstract: 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 pro…

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MolLedger: 一种具有化学基础ADME归因的加性图神经网络

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