机器学习 ADME 模型在实践中的应用：一个成功的先导优化案例研究的四项指导原则

IF 4 3区医学 Q2 CHEMISTRY, MEDICINAL

ACS Medicinal Chemistry Letters Pub Date : 2024-07-25 DOI:10.1021/acsmedchemlett.4c0029010.1021/acsmedchemlett.4c00290

Alexander S. Rich*, Yvonne H. Chan, Benjamin Birnbaum, Kamran Haider, Joshua Haimson, Michael Hale, Yongxin Han, William Hickman, Klaus P. Hoeflich, Daniel Ortwine, Ayşegül Özen and David B. Belanger,

{"title":"机器学习 ADME 模型在实践中的应用：一个成功的先导优化案例研究的四项指导原则","authors":"Alexander S. Rich*, Yvonne H. Chan, Benjamin Birnbaum, Kamran Haider, Joshua Haimson, Michael Hale, Yongxin Han, William Hickman, Klaus P. Hoeflich, Daniel Ortwine, Ayşegül Özen and David B. Belanger, ","doi":"10.1021/acsmedchemlett.4c0029010.1021/acsmedchemlett.4c00290","DOIUrl":null,"url":null,"abstract":"<p >Optimization of the ADME properties and pharmacokinetic (PK) profile of compounds is one of the critical activities in any medicinal chemistry campaign to discover a future clinical candidate. Finding ways to expedite the process to address ADME/PK shortcomings and reduce the number of compounds to synthesize is highly valuable. This article provides practical guidelines and a case study on the use of ML ADME models to guide compound design in small molecule lead optimization. These guidelines highlight that ML models cannot have an impact in a vacuum: they help advance a program when they have the trust of users, are tuned to the needs of the program, and are integrated into decision-making processes in a way that complements and augments the expertise of chemists.</p>","PeriodicalId":20,"journal":{"name":"ACS Medicinal Chemistry Letters","volume":"15 8","pages":"1169–1173 1169–1173"},"PeriodicalIF":4.0000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsmedchemlett.4c00290","citationCount":"0","resultStr":"{\"title\":\"Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study\",\"authors\":\"Alexander S. Rich*, Yvonne H. Chan, Benjamin Birnbaum, Kamran Haider, Joshua Haimson, Michael Hale, Yongxin Han, William Hickman, Klaus P. Hoeflich, Daniel Ortwine, Ayşegül Özen and David B. Belanger, \",\"doi\":\"10.1021/acsmedchemlett.4c0029010.1021/acsmedchemlett.4c00290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Optimization of the ADME properties and pharmacokinetic (PK) profile of compounds is one of the critical activities in any medicinal chemistry campaign to discover a future clinical candidate. Finding ways to expedite the process to address ADME/PK shortcomings and reduce the number of compounds to synthesize is highly valuable. This article provides practical guidelines and a case study on the use of ML ADME models to guide compound design in small molecule lead optimization. These guidelines highlight that ML models cannot have an impact in a vacuum: they help advance a program when they have the trust of users, are tuned to the needs of the program, and are integrated into decision-making processes in a way that complements and augments the expertise of chemists.</p>\",\"PeriodicalId\":20,\"journal\":{\"name\":\"ACS Medicinal Chemistry Letters\",\"volume\":\"15 8\",\"pages\":\"1169–1173 1169–1173\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://pubs.acs.org/doi/epdf/10.1021/acsmedchemlett.4c00290\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Medicinal Chemistry Letters\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsmedchemlett.4c00290\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, MEDICINAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Medicinal Chemistry Letters","FirstCategoryId":"3","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsmedchemlett.4c00290","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}

引用次数: 0

摘要

优化化合物的 ADME 特性和药代动力学 (PK) 特征是发现未来临床候选药物的任何药物化学活动中的关键活动之一。找到加快这一过程的方法以解决 ADME/PK 缺陷并减少需要合成的化合物数量是非常有价值的。本文提供了使用 ML ADME 模型指导小分子先导化合物优化设计的实用指南和案例研究。这些指南强调了 ML 模型不可能在真空中产生影响：当它们得到用户的信任、适应项目的需要并以补充和增强化学家专业知识的方式融入决策过程时，它们就能帮助推进项目。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study

Optimization of the ADME properties and pharmacokinetic (PK) profile of compounds is one of the critical activities in any medicinal chemistry campaign to discover a future clinical candidate. Finding ways to expedite the process to address ADME/PK shortcomings and reduce the number of compounds to synthesize is highly valuable. This article provides practical guidelines and a case study on the use of ML ADME models to guide compound design in small molecule lead optimization. These guidelines highlight that ML models cannot have an impact in a vacuum: they help advance a program when they have the trust of users, are tuned to the needs of the program, and are integrated into decision-making processes in a way that complements and augments the expertise of chemists.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

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.