Target Discovery Using Deep Learning-Based Molecular Docking and Predicted Protein Structures With AlphaFold for Novel Antipsychotics

• 영문명: Target Discovery Using Deep Learning-Based Molecular Docking and Predicted Protein Structures With AlphaFold for Novel Antipsychotics
• 발행기관: 대한신경정신의학회
• 저자명: Yangsik Kim Seyong Kim
• 간행물 정보: 『Psychiatry Investigation』제20권 제6호, 504~514쪽, 전체 11쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2023.06.30

영문 초록

Objective New drugs are needed to treat antipsychotic-resistant schizophrenia, especially those with clozapine-resistant schizophrenia. Atypical antipsychotics have predominantly 5-HT2A and dopaminergic antagonism, but also require investigation of other receptors. Methods In this study, the binding affinities between clozapine, olanzapine, and quetiapine with neuropharmacological, immunological, and metabolic receptors were measured using GNINA (Deep Learning Based Molecular Docking) and AlphaFold (Predicted Protein Structures). Results Through this study, it was determined that these antipsychotics showed high binding affinity to a variety of receptors, such as CB2, 5-HT1BR, NPYR4, and CCR5. Cyclosporin A and everolimus which show high affinities with those receptors could be used for the development of new antipsychotic drugs based on these drugs. Conclusion In the future, the method used in this study will be applied to the development of new antipsychotic drugs, including drug repositioning, and to the discovery of the pathophysiology of schizophrenia.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

REFERENCES

키워드

Schizophrenia Machine learning Drug discovery Clozapine Antipsychotic