AI-driven de-novo design and development of non-toxic DYRK1A inhibitors

(DYRK1A) plays a key role in various diseases, including DYRK1A syndrome, cancer, diabetes, and neurodegenerative disorders such as Alzheimer’s disease (AD), making it a compelling therapeutic target. This work integrates multiple Artificial Intelligence (AI) methods, including predictive models and generative algorithms, to design non-toxic DYRK1A inhibitors. A dual-target drug discovery framework is constructed by combining AI-driven approaches with classical techniques. An ensemble Quantitative Structure-Activity Relationship (QSAR) model predicts compound affinities, while Directed Message Passing Neural Networks (DMPNN) assess toxicity. In the generative phase, a Hierarchical Graph Generation (HGG) model facilitates the design of potential inhibitors, which are further refined through docking studies, synthesized, and experimentally validated. This approach led to the identification of the pyrazolyl-1H-pyrrolo[2,3-b]pyridine 1 as a potent DYRK1A inhibitor, prompting the synthesis of a novel derivative series. Enzymatic assays confirmed nanomolar-level inhibitory activity, while ORAC assays and LPS-induced pro-inflammatory response evaluations in BV2 microglial cells demonstrated antioxidant and anti-inflammatory properties. Overall, these compounds exhibit strong DYRK1A inhibition alongside promising pharmacological effects.