基于MolrProtTrans模型的药物标结合亲缘关系的分类预测
Yicun Lin1, Yuanfeng Li1, Wei Sun1
1College of Biological and Agricultural Engineering, Jilin University, Changchun, 130022, China.
这项研究引入了一种改进的深度学习模型,通过整合分子和蛋白质数据来预测药物向相互作用. 改进的模型在虚拟药物查中表现出卓越的性能,特别是对于G蛋白结合受体 (GPCRs).
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 准确预测药物向相互作用对于高效的虚拟药物查至关重要.
- 现有的模型往往忽略了蛋白质特征,导致偏差的预测和低于最佳的性能,特别是对于复杂的目标,如G蛋白结合受体 (GPCRs).
- 基于变压器的模型,如变压器CPI,在特定任务中显示出局限性,例如GPCR数据集上的标签反转.
研究的目的:
- 开发一种增强的基于变压器的深度学习模型,用于预测药物向相互作用.
- 通过整合分子和蛋白质特征来提高预测准确性.
- 解决现有模型在处理GPCR数据集方面的局限性,并增强整体虚拟药物查能力.
主要方法:
- 开发了一个增强的基于变压器的模型,包含Molr和ProtTrans网络用于特征提取.
- 整合了转移注意力机制,以更好地捕捉特征关系.
- 采用三重损失自主监督学习方法来提高模型的稳定性和准确性.
- 对GPCR标签逆转和人类目标数据集的评估模型性能.
主要成果:
- 拟议的模型在GPCR标签反转数据集上实现了0.81的曲线下面面积 (AUC).
- 该模型在人类目标数据集上获得了0.92的AUC.
- 在实验评估中,性能指标在数值上高于TransformerCPI和其他基线方法.
结论:
- 改进的变压器模型在预测药物向相互作用方面表现出更高的准确性.
- 分子和蛋白质信息的整合,以及先进的深度学习技术,大大提高了预测性能.
- 该模型显示了推进虚拟药物查和加速药物发现管道的有希望的潜力.
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