PBPK模拟揭示生成式药物设计的剂量暴露盲区
The Dose Makes the Poison: Exposure-Aware Evaluation of Generative Drug Design
做 AI 制药或分子生成的朋友可以看看，这篇用 PBPK 模拟给生成管线的筛选流程补了剂量暴露这一环，60 个 PARP1 候选里近三分之一会在这一步被筛掉或翻盘。如果你只依赖预测效力和毒性打分来选分子，这篇会改变你的评估思路。
一项研究指出，生成式模型在药物发现中产出的候选分子大多仅凭预测效力和毒性等静态性质来筛选，忽略了这些性质在给定剂量下能否转化为足够的药物暴露量。
研究团队用生理药代动力学（PBPK）模拟，重新评估了三条 PARP1 靶向生成管线筛选出的 60 个分子。结果显示，7 个分子无法达到足够的靶点暴露，28% 的剩余候选分子在所需剂量下存在预测安全风险。
更关键的是，在静态筛选中被忽略的分子里，有 85% 的可实现剂量候选表现优于原有的入选分子。这说明把剂量和暴露纳入评估，会实质性改变哪些生成分子值得推进。
The Dose Makes the Poison: Exposure-Aware Evaluation of Generative Drug Design
Generative models are increasingly used to propose molecules for drug discovery. They can produce far more candidates than can be experimentally tested, making computational selection essential. Most selection methods rely on predicted molecular properties, such as potency and toxicity, but overlook how these properties translate into drug exposure at a given dose. As a result, molecules that appear promising on paper may require impractical doses or raise safety concerns. We investigate this gap by using physiologically based pharmacokinetic (PBPK) simulations to re-evaluate molecules selected from three PARP1-targeted generative pipelines. Of 60 shortlisted molecules, seven fail to achieve sufficient target exposure, while 28% of the remaining candidates show predicted safety risks at their required dose. Moreover, 85% of candidates with an achievable dose are outperformed by molecules overlooked during static selection. These findings suggest that accounting for dose and exposure can substantially change which generated molecules are considered promising for drug development.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include 药物发现, PBPK, PARP1, 生成模型.
Source attribution: curated from arXiv cs.LG (@Maria Franz, N. Benjamin Erichson).
