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Figure 2.4 (a) Inhibitors known to bind to the switch‐II pocket of KRAS^G12C. (b) Fragmentation of the inhibitor structure before enumeration, including a depiction of the nature and size of the used fragment libraries. (c) Enumeration and prioritization workflow [125].

      Source: Reproduced under the terms of the Creative Commons Attribution License.

      2.3.4 Supporting Hit to Lead Exploration for a Series of Phosphodiesterase 2A Inhibitors

Phosphodiesterase enzymes are important regulators of intracellular signal transduction and can be divided into 12 families [125–127]. Phosphodiesterase 2A (PDE2A) is expressed within the brain, and it is believed that inhibition of PDE2A may improve cognitive function [128, 129]. In order to exploit this emerging biology, researchers at Janssen Pharmaceutical utilized free energy calculations to optimize a hit compound identified in a high‐throughput experimental screen to a lead series suitable for advancement into lead optimization [130]. The initial hit compound was found to have an IC₅₀ value of 66 nM, but only an 8× selectivity versus antitarget PDE10A. As such, this hit compound was crystalized to facilitate a computational modeling drive design campaign where free energy calculations would be utilized to improve the properties of derivative molecules to establish a lead series suitable for initiation of lead optimization. During this campaign, 250 putative design ideas were scored with free energy calculations, and 100 of the top performing compounds were advanced to synthesis and assay (Figure 2.5). The calculations were found to be highly accurate with an R ² value of 0.57 and mean unsigned error of 0.79 kcal/mol. In some ways more important than the accuracy of the calculations, these modeling efforts lead directly to the identification of a lead compound with single digit nanomolar potency and 100× selectivity versus antitarget PDE10A (Figure 2.6). This lead compound was further shown to have in vivo target occupancy and was of sufficient quality to justify initiation of lead optimization.

Figure 2.5 Correlation of experimental and calculated binding activity (∂G) for the compounds synthesized in each round of the FEP‐guided design. The compound data points are shaded and shaped according to the rounds of FEP and synthesis. The diagonal lines of unity and ±1 or 2 kcal/mol errors are shown. The trendline (black, partly obscured by the line of unity), equation, and R² were calculated based on all the data combined, showing almost linear slope and very little offset.

      Source: Reproduced with permission. Copyright© 2020, American Chemical Society [130].

Figure 2.6 Structures of initial hit and lead compound [130].

2.4 Conclusion and Future Outlook

      For the many researchers who have dedicated decades of their professional lives to the invention, development, and improvement of computational methods to aid drug discovery, it is deeply gratifying to now routinely see reports of these methods accelerating the discovery of novel and more effective drug therapies. These computational methods enable discovery teams to more comprehensively vet protein targets under consideration for project tractability and rapidly discover novel hits as was demonstrated for ACC and KRAS, and also accelerate hit‐to‐lead and lead optimization, as was shown for PDE2A and TYK2. Yet, despite these successes, there is clearly a great deal of work yet to do. We anticipate a major focus of future work to include the development of mixed experimental/computational approaches such as machine learning enhanced DNA‐encoded library screening and FEP‐guided fragment linking. We would also highlight that the development of predictive ADMET methods, especially related to rate of clearance, is an area clearly in need of improvement. We are excited though by the progress that has been made and look forward to what will be accomplished in the future.

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