Applying Deep-Learning-Driven De Novo Design to Hit Identification: A Case Study on A2A Adenosine Receptor Antagonists.

Artificial intelligence is increasingly applied in early drug discovery to accelerate hit identification and reduce costs. In this study, we implemented an AI-driven de novo design workflow using REINVENT to generate novel antagonists for the A2A adenosine receptor, a validated target for neurodegenerative diseases. The approach combined ligand-based and structure-based components with pharmacokinetic considerations, including blood-brain barrier permeability, within a multiparameter optimization scoring function. Two generative runs were performed: the first, with balanced scoring weights, yielded inactive 1,2,4-triazole derivatives, while an alternative filtering pipeline identified a micromolar hit. A second run emphasizing structural constraints and key receptor interactions produced three active compounds with nanomolar affinity and predicted CNS permeability. These findings highlight the critical role of scoring function parametrization and filtering strategies in AI-driven drug design and demonstrate the potential of reinforcement learning to explore chemical space for CNS-targeted ligands.