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在深度学习管道的帮助下探索DPP-IV抑制的设计规则,该管道识别了限制酶切割部位

Changge Guan¹, Jiawei Luo², Shucheng Li¹

¹Key Laboratory for Industrial Biocatalysis, Ministry of Education of China, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China.

|October 30, 2023

概括

我们开发了BERT-DPPIV,这是一种深度学习模型,用于识别抗糖尿病二二酶IV (DPP-IV) 抑制 (DPP-IV-IPs). 这个模型简化了发现,并为强大的DPP-IV-IPs建立了设计规则.

科学领域:

计算生物学是一种计算生物学.
药物发现药物发现
医学中的人工智能.

背景情况:

鉴定抗糖尿病二二酶IV (DPP-IV) 抑制性 (DPP-IV-IPs) 由于昂贵的选和缺乏设计规则而具有挑战性.
目前的方法依赖于对酶化物的艰苦选,没有合理的设计原则.

研究的目的:

开发一个先进的深度学习模型,BERT-DPPIV,用于分类DPP-IV-IP并揭示其设计规则.
通过合理的设计方法发现有力的DPP-IV-IP候选人.

主要方法:

使用微调的BERT架构 (双向编码器表示) 进行端到端分类模型.
在BERT-DPPIV模型中使用注意力机制来识别参与DPP-IV抑制的关键特征和残留物.
利用该模型提出并验证DPP-IV抑制三和五的设计规则.

主要成果:

在基准数据集上,BERT-DPPIV实现了最先进的精度 (0.894) 和马修斯相关系数 (MCC) (0.790),超过了传统的序列特征模型.
注意力机制成功地确定了限制酶切割点和关键残留物,这些残留物有助于DPP-IV抑制.
来自BERT-DPPIV的三和五的验证设计规则在选强大的DPP-IV-IP中表现出有效性.

结论:

BERT-DPPIV提供了一种强大,准确和高效的方法来识别和设计DPP-IV抑制.
该模型阐明设计规则的能力显著提高了DPP-IV抑制剂的合理药物设计.
这种方法有望降低与发现新型抗糖尿病疗法相关的成本和劳动力.

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