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

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 biochemicalconditions. Psychedelic drugs such as psilocybin and lysergic aciddiethylamide (LSD) have been renewed as fascinating treatment options andhave gradually demonstrated potential therapeutic effects in mental disorders.However, the multifaceted conditions of psychiatric disorders resulting fromindividuality, complex genetic interplay, and intricate neural circuits impactthe systemic pharmacology of psychedelics, which disturbs the integration ofmechanisms that may result in dissimilar medicinal efficiency. The preciseprescription of psychedelic drugs remains unclear, and advanced approachesare needed to optimize drug development. Here, recent studiesdemonstrating the diverse pharmacological effects of psychedelics in mentaldisorders are reviewed, and emerging perspectives on structural function, themicrobiota-gut-brain axis, and the transcriptome are discussed. Moreover, theapplicability of deep learning is highlighted for the development of drugs onthe basis of big data. These approaches may provide insight intopharmacological mechanisms and interindividual factors to enhance drugdiscovery and development for advanced precision medicine