A-iMPO: A Design-Time Prediction Toolbox for Blood Brain Barrier Permeation Using an Integrated and Interpretable Multiparameter Optimization Framework

IF 3.9 3区医学 Q2 CHEMISTRY, MEDICINAL

ACS Medicinal Chemistry Letters Pub Date : 2026-09-10 Epub Date: 2026-08-27 DOI:10.1021/acsmedchemlett.6c00347

Samiron Phukan, Ashis Nandy, Manjunath Ramarao

{"title":"A-iMPO: A Design-Time Prediction Toolbox for Blood Brain Barrier Permeation Using an Integrated and Interpretable Multiparameter Optimization Framework","authors":"Samiron Phukan, Ashis Nandy, Manjunath Ramarao","doi":"10.1021/acsmedchemlett.6c00347","DOIUrl":null,"url":null,"abstract":"<p>Design\r\ntime prioritization of central nervous system\r\n(CNS) drug\r\ncandidates remains a challenge due to the restrictive nature of the\r\nblood–brain barrier (BBB) governing brain exposure. Although\r\nvarious multiparameter optimization (MPO) strategies have guided CNS\r\nmedicinal chemistry for over a decade, existing frameworks rely heavily\r\non heuristic cutoffs and offer limited interpretability across chemically\r\ndiverse scaffolds. Here, we introduce a next generation CNS-MPO framework─Aragen-iMPO,\r\n(A-iMPO)─which was developed using 5,129 curated compounds\r\nthrough an integrated workflow combining explainable machine learning,\r\nrigorous descriptor selection, and low-dimensional discriminant mapping.\r\nThis yielded six chemically intuitive features capturing polarity,\r\nionization, size, rigidity, and electronic distribution. The resulting\r\nscore provides a transparent discriminant function enabling direct\r\ncompound prioritization through a simple threshold rule. Across internal\r\nand external validation sets, A-iMPO outperformed established CNS-focused\r\nscoring methods while maintaining mechanistic interpretability, providing\r\na practical and design ready tool for CNS drug discovery.</p>","PeriodicalId":20,"journal":{"name":"ACS Medicinal Chemistry Letters","volume":"17 9","pages":"2055–2065"},"PeriodicalIF":3.9000,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Medicinal Chemistry Letters","FirstCategoryId":"3","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsmedchemlett.6c00347","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/8/27 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}

引用次数: 0

Abstract

Design time prioritization of central nervous system (CNS) drug candidates remains a challenge due to the restrictive nature of the blood–brain barrier (BBB) governing brain exposure. Although various multiparameter optimization (MPO) strategies have guided CNS medicinal chemistry for over a decade, existing frameworks rely heavily on heuristic cutoffs and offer limited interpretability across chemically diverse scaffolds. Here, we introduce a next generation CNS-MPO framework─Aragen-iMPO, (A-iMPO)─which was developed using 5,129 curated compounds through an integrated workflow combining explainable machine learning, rigorous descriptor selection, and low-dimensional discriminant mapping. This yielded six chemically intuitive features capturing polarity, ionization, size, rigidity, and electronic distribution. The resulting score provides a transparent discriminant function enabling direct compound prioritization through a simple threshold rule. Across internal and external validation sets, A-iMPO outperformed established CNS-focused scoring methods while maintaining mechanistic interpretability, providing a practical and design ready tool for CNS drug discovery.

查看原文本刊更多论文

A- impo：设计时血脑屏障渗透预测工具箱，使用集成和可解释的多参数优化框架

由于控制脑暴露的血脑屏障（BBB）的限制性，中枢神经系统（CNS）候选药物的设计优先级仍然是一个挑战。尽管各种多参数优化（MPO）策略已经指导了cns药物化学超过十年，但现有的框架严重依赖启发式截断，并且在化学不同的支架之间提供有限的可解释性。在这里，我们介绍了下一代cins - mpo框架──Aragen-iMPO，（a - impo）──它是通过结合可解释的机器学习、严格的描述符选择和低维判别映射的集成工作流程，使用5,129种经过筛选的化合物开发的。这产生了六个直观的化学特征，包括极性、电离、尺寸、刚性和电子分布。结果得分提供了一个透明的判别函数，通过一个简单的阈值规则实现直接复合优先级。在内部和外部验证集中，A-iMPO优于现有的以中枢神经系统为中心的评分方法，同时保持了机制的可解释性，为中枢神经系统药物发现提供了实用和设计就绪的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

ACS Medicinal Chemistry Letters CHEMISTRY, MEDICINAL-

CiteScore

7.30

自引率

2.40%

发文量

328

审稿时长

1 months

期刊介绍： ACS Medicinal Chemistry Letters is interested in receiving manuscripts that discuss various aspects of medicinal chemistry. The journal will publish studies that pertain to a broad range of subject matter, including compound design and optimization, biological evaluation, drug delivery, imaging agents, and pharmacology of both small and large bioactive molecules. Specific areas include but are not limited to: Identification, synthesis, and optimization of lead biologically active molecules and drugs (small molecules and biologics) Biological characterization of new molecular entities in the context of drug discovery Computational, cheminformatics, and structural studies for the identification or SAR analysis of bioactive molecules, ligands and their targets, etc. Novel and improved methodologies, including radiation biochemistry, with broad application to medicinal chemistry Discovery technologies for biologically active molecules from both synthetic and natural (plant and other) sources Pharmacokinetic/pharmacodynamic studies that address mechanisms underlying drug disposition and response Pharmacogenetic and pharmacogenomic studies used to enhance drug design and the translation of medicinal chemistry into the clinic Mechanistic drug metabolism and regulation of metabolic enzyme gene expression Chemistry patents relevant to the medicinal chemistry field.