AutoDesigner - Core Design, a De Novo Design Algorithm for Chemical Scaffolds: Application to the Design and Synthesis of Novel Selective Wee1 Inhibitors

The hit identification stage of a drug discovery program generally involves the design of novel chemical scaffolds with desired biological activity against the target(s) of interest. One common approach is scaffold hopping, which is the manual design of novel scaffolds based on known chemical matter. One major limitation of this approach is narrow chemical space exploration, which can lead to difficulties in maintaining or improving biological activity, selectivity, and favorable property space. Another limitation is the lack of preliminary structure-activity relationship (SAR) data around these designs, which could lead to selecting suboptimal scaffolds to advance lead optimization. To address these limitations, we propose AutoDesigner - Core Design (CoreDesign), a de novo scaffold design algorithm. Our approach is a cloud-integrated, de novo design algorithm for systematically exploring and refining chemical scaffolds against biological targets of interest. The algorithm designs, evaluates, and optimizes a vast range - from millions to billions - of molecules in silico, following defined project parameters encompassing structural novelty, physicochemical attributes, potency, and selectivity. In this manner, CoreDesign can generate novel scaffolds and also explore preliminary SAR around each scaffold using FEP+ potency predictions. CoreDesign requires only a single ligand with quantifiable binding affinity and an initial binding hypothesis, making it especially suited for the hit-identification stage where experimental data is often limited. To validate CoreDesign in a real-world drug discovery setting, we applied it to the design of novel, potent Wee1 inhibitors with improved selectivity over PLK1. Starting from a single known ligand, CoreDesign rapidly explored over 23 billion molecules to identify 1,342 novel chemical series with a mean of 4 compounds per scaffold. Importantly, all chemical series met the predefined property space requirements. To rapidly analyze this large amount of data and prioritize chemical scaffolds for synthesis, we utilize t-Distributed Stochastic Neighbor Embedding (t-SNE) plots of in silico properties. The chemical space projections allowed us to rapidly identify a structurally novel 5-5 fused core meeting all the hit-identification requirements. Several compounds were synthesized and assayed from the scaffold, displaying good potency against Wee1 and excellent PLK1 selectivity. Our results suggest that CoreDesign can significantly speed up the hit-identification process and increase the probability of success of drug discovery campaigns by allowing teams to bring forward high-quality chemical scaffolds de-risked by the availability of preliminary SAR.