(BQ) Part 2 book “Biomolecular simulations in structure-based drug discovery” has contenst: Ion channel simulations, understanding allostery to design new drugs, molecular dynamics applications to GPCR ligand design, and other contents. | 163 Part III Applications and Success Stories 165 7 From Computers to Bedside: Computational Chemistry Contributing to FDA Approval Christina Athanasiou and Zoe Cournia Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece Introduction The drug design process is unequivocally a time-consuming and expensive endeavor, with recent estimates classifying it as a $ billion expenditure [1]. From target identification and validation, to hit-and-lead discovery, as well as lead optimization, preclinical and clinical, the outlay in each consecutive stage accounts for several millions of US dollars, with the financial burden surging with every unsuccessful attempt, especially in the late phases of the development. Fortunately, the rise in validated protein targets relevant to therapeutic applications deriving from large-scale genomic sequencing adjoined with proteome analysis, held the basis of systematic efforts targeting the efficacious treatment of protein-provoking diseases [2]. In addition, the advances in high-throughput screening (HTS) experiments allowed the assessment of thousands of molecules concurrently by employing robotic automation, diminished the human labor, and dominated the area of hit identification in the past two decades [3]. Nonetheless, HTS is still time consuming and expensive, with its acquisition value and operational costs being prohibitive for most laboratories. Moreover, careful decision making to decrease attrition rates and avoid costly failures, together with the tremendous advances in computational technologies led to the advent of rational, computer-aided drug design (CADD). Molecular modeling techniques have revolutionized the conventional drug discovery processes, by enabling the reduction of time and resources allocated in the hit identification, hit-to-lead optimization and lead optimization phases of the drug discovery pipeline. Novel druglike candidates are first examined in silico for their .
