Drug Discovery Overview

Principles

Definition

Drug discovery is a multifaceted research field and systematic process focused on identifying and developing potential new therapeutic agents. This endeavor encompasses target identification, compound screening, lead optimization, and preclinical evaluation, ultimately aiming to translate fundamental biological understanding into effective treatments for disease.

Ontological type

Target Identification

Preclinical Evaluation

Screening Technologies

Key developments

Key Figures

Mechanism and PK Foundations era

John R. Vane [1] was affiliated with Cornell University [3] and Yale University [4] during the Mechanism and PK Foundations era, contributing to the mechanistic framing of drug action. His 1971 paper Inhibition of Prostaglandin Synthesis as a Mechanism of Action for Aspirin-like Drugs [7] delineated a mechanism-based basis for NSAID effects and spurred quantitative approaches to potency and selectivity in pharmacology. G. Wilkinson [2] was associated with Harvard University [5] and Massachusetts Institute of Technology [6] in this period, reflecting the PK-centered emphasis across leading research centers. His 1975 paper A physiological approach to hepatic drug clearance [8] helped formalize hepatic clearance concepts, while the 1985 paper Purification and characterization of the human liver cytochromes P-450 involved in debrisoquine 4-hydroxylation and phenacetin O-deethylation, two prototypes for genetic polymorphism in oxidative drug metabolism [9], and their implications for dosing and drug interactions in this era.

Integrated ADME and Design era

Arthur J. Olson[1], affiliated with Rockefeller University[3] and Washington University in St. Louis[4] during this era, helped define the integrated ADME design paradigm. His key contribution—AutoDock4 and AutoDockTools4[5]—advanced automated docking with selective receptor flexibility, enabling structure-guided lead optimization and improved triage within ADME-informed discovery. Paul J. Feeney[2] contributed to foundational solubility and permeability estimation in drug discovery and development settings, as detailed in Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings[6]. This work helped establish early in vitro–to–in vivo extrapolation and inform developability filters, aligning solubility and permeability metrics with PK/PD modeling for candidate triage in the Integrated ADME era[6].

Data- and AI-Driven Discovery era

Stephan Harbarth [1] is associated with the University of Pennsylvania [3] and Radboud University Nijmegen [4] in this era of data- and AI-driven discovery. His key contributions in this era, as highlighted by the 2018 paper [7], include shaping the WHO priority list of antibiotic-resistant bacteria and tuberculosis and outlining discovery, research, and development pathways for new antibiotics, which were crucial for data-driven prioritization in antimicrobial R&D. David L. Paterson [2] is associated with the Nanyang Technological University [5] and the University of Pittsburgh [6] in this era. The same 2018 paper [7] shapes antibiotic-discovery priorities in this era, underscoring data-driven triage and translational potential.