Pisano, G.P. (2019).The hard truth about innovative cultures. https://hbr.org/2019/01/the‐hard‐truth‐about‐innovative‐cultures (accessed 29 September 2021).
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 advanced computational modeling has motivated large pharmaceutical companies, such as Bayer and AstraZeneca, to bring these advanced computational methods in‐house to more broadly accelerate their own discovery efforts [15–17]. These successes utilizing advanced computational modeling to accelerate discovery are also now being realized by other parties using different computational strategies, such as Exscientia and Sumitomo Dainippon Pharma, which will be jointly advancing a serotonin 5‐HT1A receptor agonist into clinical trials for obsessive compulsive disorder [18].
The success of these smaller biotech companies using advanced computational modeling to accelerate their discovery efforts, and the growing investments of large pharmaceutical companies to build out similar capabilities, raises a couple important questions: what specific modeling technologies are being most used, and how are they delivering such value? In this chapter, we will review those technologies that have made the strongest contributions to these recent successes, as well as provide opinions regarding how these technologies and their deployment may be improved in the future.
2.2 Essential Techniques
Excitingly, over the last decade, advanced computational methods have broadly matured to the point where they now can be used to accelerate all stages of preclinical drug discovery, including (i) Target Validation and Feasibility Assessment, (ii) Hit Discovery, (iii) Hit‐to‐Lead and Lead Optimization, and (iv) Preclinical Development. In this section we will review which computational techniques, in our view, have become indispensable components of computationally driven drug discovery and their primary use cases.
2.2.1 Target Validation and Feasibility Assessment
The selection of a target for a drug discovery campaign is an important and highly consequential decision. Selection of an inappropriate target may doom an otherwise highly successful discovery project to failure in clinical trials, if connection of the target to the underlying disease biology is misassessed. Likewise, there are many targets where identification of drug‐like small molecule ligands remains an unsolved challenge, and if the protein target structure presents the project team with no binding pockets appropriate to bind drug‐like small molecules, alternative therapeutic modalities, such as biologics might be required. Advanced computational methods can assist this process by refining intermediate resolution structures to a higher apparent resolution. This allows a project team considering the target to better understand the fine details of the challenges likely to be posed by the protein earlier in the discovery project than would typically be possible [19–21]. Once these improved structures are in hand, they can be used to pursue computational solvent analysis of the protein surface to identify regions of unfavorable solvation that might be exploited to develop high‐affinity small molecule ligands [22–26]. Likewise, computational fragment soak methodologies can be used both to infer the druggability of various sites at the surface of the protein, as well as investigate if cryptic pocket binding opportunities might be possible to explore [27–31]. Such analyses may provide the project team a firmer foundation from which to judge if a drug‐like small‐molecule therapeutic modality is an appropriate choice for a given target, and should further allow the project team to advance from target tractability assessment to hit discovery with confidence the identification of hit‐like small‐molecule ligands should be tractable.
2.2.2 Hit Discovery
If a project team intends to pursue a drug‐like small‐molecule therapeutic modality for a particular protein target, then an important first goal of the project will be to identify small molecules with appreciable affinity and characterizable structure–activity relationships. A wide variety of experimental techniques have historically been used to identify such developable ligand matter, including high‐throughput screening, fragment screening, and DNA encoded library screening [32–35]. In each of these methods, a sizeable pool of small molecules, perhaps a few thousand molecules in a fragment‐based screen to potentially billions of molecules in a DNA‐encoded library screen, are tested for affinity for the target protein. The most potent of these initial hits will then typically be resynthesized and re‐assayed to confirm their activity using lower‐throughput and more reliable experimental techniques. Given the long timelines and significant expense associated with pursuing these experimental hit finding strategies, it is perhaps unsurprising that virtual screening, i.e. the use of computational modeling to similarly evaluate such a candidate pool of molecules for affinity, is gaining ever broader acceptance [36, 37]. These virtual screening techniques include:
1 Molecular docking technologies where each ligand is