The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of Computer-Aided Molecular Design Pub Date : 2024-03-19 DOI:10.1007/s10822-024-00552-6
Jeremy Jones, Robert D. Clark, Michael S. Lawless, David W. Miller, Marvin Waldman
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Abstract

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.

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人工智能驱动的药物设计(AIDD)平台:一个交互式多参数优化系统,将分子进化与基于生理学的药代动力学模拟融为一体。
近年来,计算机辅助药物设计发展迅猛,硅学设计分子进入临床的多个实例证明了这一领域对医学的贡献。设计和实施得当的平台可以大大缩短药物开发的时间和成本。虽然这些工作最初主要集中在靶点亲和力/活性上,但现在人们认识到,其他参数对药物的成功开发和进入临床同样重要,包括药代动力学特性以及吸收、分布、代谢、排泄和毒理学(ADMET)特性。在过去的十年中,已经有多个程序将这些特性纳入了药物设计和优化过程,并在不同程度上实现了多参数优化。在此,我们介绍人工智能驱动药物设计(AIDD)平台,该平台通过将基于生理的高通量药代动力学模拟(由 GastroPlus 提供支持)和 ADMET 预测(由 ADMET Predictor 提供支持)与先进的进化算法相结合,实现了药物设计过程的自动化。AIDD 在迭代执行多目标优化时使用这些和其他估计值,以产生具有活性和先导性的新分子。在此,我们将介绍 AIDD 的工作流程以及相关方法的细节。我们使用恶性疟原虫二氢烟酸脱氢酶的三唑并嘧啶抑制剂数据集来说明 AIDD 如何生成新分子集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
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