AI-Driven Personalized Antidepressant Discovery for Adolescents: Molecular Property Prediction and Drug Design

The rising incidence of adolescent depression necessitates targeted and effective treatments. Traditional drug discovery methods are costly and time-consuming. Leveraging extensive data resources, artificial intelligence (AI) technologies are revolutionizing this process. This study explores AI-driven approaches for designing personalized antidepressants for adolescents, focusing on molecular property prediction and generation. I highlight key data resources, AI algorithms, and their applications in predicting drug properties and designing new drugs. Our research method strategically utilizes critical data, reduces dependency on extensive datasets, and deeply explores the intrinsic relationships betI en different data types. Our findings indicate that AI can significantly enhance drug discovery for adolescent depression, providing more effective and tailored treatments with feI r side effects.