Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

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).

Automated summary

The use of computer-aided drug design (CADD) in radiotracer development is increasing. Traditional CADD methods, such as virtual screening and optimization, have been successfully used in drug discovery programs. This review discusses the use of virtual screening for hit identification, filtering and culling hits for in vitro assays, optimizing hit compounds for PET, and the latest techniques in CADD employing machine learning.