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.

近年来，计算机辅助药物设计发展迅速，计算机辅助设计的分子进入临床的多个实例证明了该领域对医学的贡献。正确设计和实施的平台可以大大缩短药物开发时间和成本。虽然这些努力最初主要集中在目标亲和力/活性上，但现在人们认识到，其他参数对于药物的成功开发及其向临床的进展同样重要，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，已经开发了多个程序，将这些特性并不同程度地纳入药物设计和优化过程中，从而实现多参数优化。在这里，我们介绍人工智能驱动的药物设计 (AIDD) 平台，该平台通过将高通量基于生理学的药代动力学模拟（由 GastroPlus 提供支持）和 ADMET 预测（由 ADMET Predictor 提供支持）与先进的进化技术相结合，实现药物设计过程的自动化。与当前生成模型完全不同的算法。AIDD 使用这些和其他估计来迭代执行多目标优化，以产生具有活性和类先导化合物的新型分子。在这里，我们描述了 AIDD 工作流程以及其中涉及的方法的详细信息。我们使用来自恶性疟原虫的二氢乳清酸脱氢酶的三唑并嘧啶抑制剂数据集来说明 AIDD 如何产生新的分子组。