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

Area of Science:

• Neuroscience and Pharmacology
• Psychiatry
• Computational Biology

Background:

• Mental disorders, including anxiety, major depressive disorder (MDD), and autism spectrum disorder (ASD), are complex brain disorders with diverse etiologies.
• Psychedelic compounds like psilocybin and lysergic acid diethylamide (LSD) are emerging as potential therapeutic agents for these conditions.

Purpose of the Study:

• To review the diverse pharmacological effects of psychedelics in mental disorders.
• To discuss emerging perspectives on structural function, the microbiota-gut-brain axis, and the transcriptome in relation to psychedelic therapy.
• To highlight the application of deep learning in optimizing psychedelic drug development.

Main Methods:

• Review of recent scientific literature on psychedelic pharmacology and mental disorders.
• Discussion of novel biological perspectives including the microbiota-gut-brain axis and transcriptomics.
• Exploration of deep learning applications for big data analysis in drug development.

Main Results:

• Psychedelics demonstrate potential therapeutic effects but their efficacy varies due to individual patient factors and complex disease mechanisms.
• Understanding structural function, microbiota-gut-brain axis, and transcriptome offers new insights into psychedelic action.
• Deep learning can analyze complex datasets to improve drug discovery and development.

Conclusions:

• Optimizing psychedelic drug development requires advanced approaches to address interindividual variability and complex pharmacological mechanisms.
• Integrating insights from structural function, the microbiota-gut-brain axis, and transcriptomics, alongside deep learning, can enhance precision medicine for mental disorders.