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结合分子图像和蛋白质结构表示的深度学习框架识别了治疗疼痛的候选药物

Yuxin Yang¹, Yunguang Qiu², Jianying Hu³

¹Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Computer Science, Kent State University, Kent, OH 44242, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.

Cell reports methods

|September 28, 2024

概括

我们开发了一个深度学习框架,LISA-CPI,用于预测药物向相互作用. 这种人工智能工具通过分析分子图像和蛋白质结构来增强治疗疼痛和其他疾病的药物发现.

科学领域:

计算化学是一种计算化学.
药理学药理学是指药理学的学科.
人工智能在药物发现中的作用

背景情况:

人工智能 (AI) 和深度学习显示出识别人类疾病的药物的潜力,包括疼痛.
预测化合物-蛋白相互作用 (CPI) 对于药物发现至关重要,但仍然具有挑战性.

研究的目的:

介绍一个可解释的深度学习框架,LISA-CPI,它集成了连接体分子图像表示和受体3D结构,用于准确的CPI预测.
利用人工智能来识别用于疼痛管理的新型治疗剂.

主要方法:

开发了LISA-CPI,该框架结合了无监督深度学习 (ImageMol) 用于配体表示和AlphaFold2 (Evoformer) 用于蛋白质结构分析.
在一个数据集上训练和评估LISA-CPI,该数据集包括104,969个配体和33个G蛋白结合受体 (GPCR).

主要成果:

在预测实验性CPI方面,LISA-CPI与最先进的模型相比显著改善 (平均绝对误差减少约20%).
确定了潜在的可重复使用的药物,如甲基尔戈美特林和候选代谢物,如治疗疼痛的西提科林,向人类GPCRs.

结论:

在深度学习框架内整合分子图像和3D蛋白质结构数据,为药物发现提供了强大的计算工具.
LISA-CPI为开发疼痛和其他复杂疾病的治疗方法提供了一个有前途的方法.

关键词:

在AlphaFold2中,我们将使用AlphaFold2.CP:系统生物学系统生物学在Evoformer中,我们可以使用Evoformer.G蛋白结合受体的受体是什么在GPCR中,GPCR是指GPCR.在 ImageMol 找图片Mol 人工智能的人工智能是人工智能.化合物-蛋白质相互作用深度学习是一种深度学习.药物重用是为了改变药物的用途.肠道代谢物的代谢物.疼痛疼痛疼痛疼痛

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