使用基于深度学习的分子对接和预测蛋白质结构的目标发现与新型抗精神病药物的AlphaFold
Yangsik Kim1,2, Seyong Kim2
1Department of Psychiatry, Inha University Hospital, Incheon, Republic of Korea.
Psychiatry investigation
|May 30, 2023
概括
针对特定受体的新抗精神病药物,如CB2,5-HT1BR,NPYR4和CCR5,是治疗耐药精神分裂症的必要条件. 这项研究使用人工智能来识别精神分裂症治疗的潜在新药候选者.
科学领域:
- 神经药理学神经药理学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 精神分裂症的耐治疗病例需要新的治疗药物.
- 非典型抗精神病药主要向5-HT2A和多巴胺基受体,因此需要探索其他受体向.
研究的目的:
- 调查现有的抗精神病药物的新型受体结合 afinities.
- 使用计算方法识别抗精神病耐药精神分裂症的潜在新药候选者.
主要方法:
- 利用GNINA (基于深度学习的分子对接) 和AlphaFold (预测的蛋白质结构) 来评估结合亲和力.
- 评估了克洛扎宾,奥兰扎宾和奎蒂亚平与各种神经药物学,免疫学和代谢受体之间的结合 afinities.
主要成果:
- 克洛沙平,奥兰沙平和奎平对包括CB2,5-HT1BR,NPYR4和CCR5.5在内的受体表现出高的结合亲缘关系.
- 鉴定了环素A和everolimus作为新抗精神病药物开发的潜在候选药物,因为它们与这些向受体的高度亲缘关系.
结论:
- 使用的计算方法可以指导新型抗精神病药物的开发.
- 未来的应用包括药物重新定位和阐明精神分裂症病理生理学.
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