Free energy simulations have begun to impact real-world drug discovery due to recent advances in force fields and sampling algorithms, along with the availability of low-cost parallel computing environments. The ability to profile thousands of compounds with these rigorous methods now necessitates more sophisticated, chemically-aware enumeration strategies to generate synthetically-tractable design ideas. To fill this need in our drug discovery programs, we have developed Pathfinder, an algorithm that uses automated retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically-accessible chemical space. Here, we present technical and methodological developments in free energy simulations that have increased the scope of chemical space that can be explored, including the ability to profile covalent inhibitors, macrocycles, and charge perturbations. We also present case studies of combining Pathfinder enumeration, active machine-learning algorithms, and advanced free energy methods to accelerate the discovery of potent, synthetically-tractable leads and meaningfully impact kinase drug discovery campaigns.

Leveraging synthetically-aware enumeration strategies and free energy simulations in drug discovery campaigns

2019 Fall National Meeting

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