剂量决定毒性：生成式药物设计的暴露量感知评估
The Dose Makes the Poison: Exposure-Aware Evaluation of Generative Drug Design
AI 导读
一项 NeurIPS 2026 AI4DD 工作坊接收的研究用生理药代动力学（PBPK）模拟，重新评估了来自三条 PARP1 靶向生成式管线的候选分子。60 个入围分子中 7 个无法达到足够的靶点暴露量，28% 的其余候选分子在所需剂量下存在预测安全风险。结果表明，纳入剂量与暴露量考量会显著改变哪些生成分子被认为有开发前景。
正文 · 原文
Abstract: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.
| Comments: | 14 pages, 3 figures. Accepted at the AI4DD workshop (AI for Drug Discovery: Bridging the Translation Gap), NeurIPS 2026 |
| Subjects: | Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.13398 [q-bio.QM] |
| (or arXiv:2611.13398v1 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13398 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Maria Franz [view email]
[v1]
Tue, 10 Nov 2026 00:20:01 UTC (148 KB)
来源：arXiv cs.AI · arxiv.org