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KMID : 1118520230200060504
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Psychiatry Investigation
2023 Volume.20 No. 6 p.504 ~ p.514
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Target Discovery Using Deep Learning-Based Molecular Docking and Predicted Protein Structures With AlphaFold for Novel Antipsychotics
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Kim Yang-Sik
Kim Se-Yong
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Abstract
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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.
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KEYWORD
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Schizophrenia, Machine learning, Drug discovery, Clozapine, Antipsychotic
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