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2 Advanced Computational Modeling Accelerating Small‐Molecule Drug Discovery: A Growing Track Record of Success
Robert Abel
Drug Discovery Group, Schrödinger Inc., 120 West 45th Street, NY 10036‐4041, New York, NY, USA
2.1 Introduction
The last 10 years have marked a turning point in advanced computational modeling clearly demonstrating its value accelerating the discovery of newer and more efficacious small‐molecule drug therapies suitable to be advanced into clinical trials. Much of this important history has not yet entered the peer‐reviewed literature, and hopefully this chapter will serve as a reference to those wishing to better understand how the idea that computational analysis could drive pharmaceutical drug discovery forward from a position of hopeful optimism to a statement of objective fact.
It is important to acknowledge the hopeful optimism that computational modeling could accelerate drug discovery has been long standing. It has been almost 40 years since Fortune magazine in October of 1981 famously published the cover article “The next industrial revolution: designing drugs by computer at Merck” [1]. From the publication of that article to at least 2010, despite the efforts of many research groups and companies, disconcertingly little progress was made toward achieving these goals [2].
Evidence that computational modeling was starting to live up to its promise began to emerge in 2013 when Bruce Booth announced an innovative company he co‐founded, Nimbus Discovery, having benefitted from advanced computational modeling technologies made available through a strategic partnership with Schrödinger, a novel virtual and globally distributed operating model and an innovative LLC‐based asset centric business model, had succeeded in discovering an acetyl‐CoA carboxylase (ACC) inhibitor suitable for advancement into clinical studies for the treatment of non‐alcoholic steatohepatitis (NASH) in only 16 months [2, 3]. That announcement was followed by Nimbus initiating phase I clinical trials of this ACC inhibitor, and Gilead acquiring the asset to further advance the matter into phase II studies [4]. Post this watershed event, Nimbus has continued to extensively use advanced computational modeling to advance its discovery programs and has repeated this early success by developing a highly selective clinical stage tyrosine kinase 2 (TYK2) inhibitor, which it expects to advance into a phase IIB clinical trial for psoriasis pathogenesis in 2021 [5, 6].
Following in the footsteps of Nimbus, Morphic Therapeutics also utilized Schrödinger's advanced computational modeling techniques and their own considerable biology, structural biology, and medicinal chemistry expertise to advance their discovery pipeline for a broad range of highly challenging integrin targets [7]. Those efforts have resulted in a clinical stage α4β7 integrin inhibitor that Morphic Therapeutics is advancing into clinical studies for inflammatory bowel disease [8, 9]. Similar success was also achieved by Relay Therapeutics utilizing computational methods to develop a clinical stage protein tyrosine phosphatase SHP2 inhibitor for patients with advanced or metastatic solid tumors [10–14]. The ability of these smaller biotech companies to successfully and rapidly identify chemical matter suitable for clinical studies using
