Psychedelic Drugs in Mental Disorders: Current Clinical Scope and Deep Learning‐Based Advanced Perspectives

Psychedelic drugs such as psilocybin and LSD show renewed potential for treating mental disorders like anxiety, major depressive disorder, and autism spectrum disorder. However, individual genetic, epigenetic, and neural circuit differences create complex pharmacological responses, making precise prescription challenging. This review examines recent studies on psychedelics' diverse effects, covering structural function, the microbiota-gut-brain axis, and transcriptome perspectives. It also highlights deep learning and big data approaches as promising tools to clarify mechanisms and interindividual factors, aiming to improve drug discovery and advance precision medicine for psychiatric conditions.

Study at a glance

Abstract

Abstract Mental disorders are a representative type of brain disorder, including anxiety, major depressive depression (MDD), and autism spectrum disorder (ASD), that are caused by multiple etiologies, including genetic heterogeneity, epigenetic dysregulation, and aberrant morphological and biochemical conditions. Psychedelic drugs such as psilocybin and lysergic acid diethylamide (LSD) have been renewed as fascinating treatment options and have gradually demonstrated potential therapeutic effects in mental disorders. However, the multifaceted conditions of psychiatric disorders resulting from individuality, complex genetic interplay, and intricate neural circuits impact the systemic pharmacology of psychedelics, which disturbs the integration of mechanisms that may result in dissimilar medicinal efficiency. The precise prescription of psychedelic drugs remains unclear, and advanced approaches are needed to optimize drug development. Here, recent studies demonstrating the diverse pharmacological effects of psychedelics in mental disorders are reviewed, and emerging perspectives on structural function, the microbiota‐gut‐brain axis, and the transcriptome are discussed. Moreover, the applicability of deep learning is highlighted for the development of drugs on the basis of big data. These approaches may provide insight into pharmacological mechanisms and interindividual factors to enhance drug discovery and development for advanced precision medicine.