Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

Overview

Journal Pharmaceuticals (Basel)

Publisher MDPI

Specialty Chemistry

Date 2023 Jun 1

PMID 37259459

Authors

Chia-Ju Hsieh

Sam Giannakoulias

E James Petersson

Robert H Mach

Affiliations

Soon will be listed here.

Abstract

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).

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