Hsieh Chia-Ju, Giannakoulias Sam, Petersson E James, Mach Robert H
Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA.
Pharmaceuticals (Basel). 2023 Feb 18;16(2):317. doi: 10.3390/ph16020317.
The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).
计算机辅助药物设计（CADD）在放射性示踪剂开发中用于鉴定先导化合物的应用正在稳步增加。传统的CADD方法，如基于结构和基于配体的虚拟筛选与优化，已在许多药物发现项目中成功应用，并在本综述中重点介绍。首先，我们讨论在药物发现项目开始时使用虚拟筛选来识别活性化合物。接下来分析如何对虚拟筛选得到的活性化合物进行过滤和筛选，以获得极有可能在体外试验中进行测试的候选化合物。然后，我们说明如何使用CADD来优化虚拟筛选中经实验验证的活性化合物的效力，以用于正电子发射断层扫描（PET）。最后，我们通过对采用机器学习（ML）的CADD最新技术的调查来结束本文。
