Application of FastGrow to the Design and Synthesis of Drug Like Compounds

Jeremy Edmunds introduced the concepts of medicinal chemistry in modern drug discovery campaign with a focus on appropriate specificity achieved by computational methods. Working simultaneously on several compound series, 3,046 compounds were assessed for the study of PKCθ inhibitors.

With the aim to answer the question of medicinal chemists, what to make next, and how to reduce the number of compounds to be synthesized, a computational workflow was established:

Starting from a seed compound in a structure-based approach to address unoccupied binding cavities, an early version of FastGrow by the Rarey Lab (see above) was used to systemtically explore all applicable exit vectors of a template molecule with building blocks originating from in-house knowledge to create a library of possible evolved compounds. Subsequently, after database generation, a chemical space of 3.6 million fragments was investigated with FastGrow within only 2.3 hours (limit 10,000 compounds) and 3.7 hours (limit 100,000 compounds) on Amazon Web Services (Intel® Xeon® Platinum 8275CL CPU @ 3.00GHz; Thread(s) per core: 2, Core(s) per socket: 8). 23% and 19% percent of the suggested compounds (for 10,000 and 100,000 results, respectively) displayed a predicted affinity under 10 nM with preferred torsions.

Further Edmunds pointed out that compounds should be synthesized based on what is needed instead of what is the simplest to make to avoid bias by medicinal chemistry experience.

In a current project FastGrow was successfully used to complete several weeks of standard CADD procedures within hours. Strikingly, all four compounds selected for synthesis suggested by FastGrow exhibited activity at the target with KD values of 0.002, 0.05, 0.2, and 7 µM, respectively.

BioSolveIT DrugSpace 2022 "It's a small world!"
Speaker: Jeremy Edmunds (AbbVie)