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Updated: Jun 12, 2025

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KinomePro-DL的开发和应用:一个基于深度学习的在线小分子基因组选择性分析预测平台

Wei Ma¹, Jiaqi Hu¹, Zhuangzhi Chen¹

¹Drug Research Business Unit, PharmaBlock Sciences (Nanjing), Inc., 81 Huasheng Road, Jiangbei New Area, Nanjing, Jiangsu 210032, China.

Journal of chemical information and modeling

|September 25, 2024

概括

一种新的深度学习模型快速预测酶抑制剂的选择性,有助于药物发现和重新利用. 该工具有助于识别潜在的不良影响,并发现具有更好的安全性概况的新型激酶抑制剂.

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.
生物信息学是一种生物信息学.

背景情况:

鉴别激酶抑制剂的选择性对于药物发现,识别非目标效应,并使药物重新定位至关重要.
实验性基因组选择性分析是资源密集和耗时的.

研究的目的:

开发一种深度学习模型,用于预测小分子的基因组选择性概况.
创建一个用户友好的Web服务器,用于预测激酶抑制剂多药学.

主要方法:

一个多任务深度神经网络被训练在对抗191个激酶的抑制剂的精选数据集上.
该模型是通过整合和清理来自六个公共数据集的数据来构建的.
使用auROC,prc-AUC,精度和二进制交叉度指标来评估性能.

主要成果:

该模型实现了高预测性能 (auROC=0.95,prc-AUC=0.92,精度=0.90).
它在对各种蛋白质点的先验测试中表现出强的表现.
使用模型在虚拟查工作流程中识别出具有强烈活性和选择性的新型CDK2激酶抑制剂.

结论:

开发的深度学习模型准确地预测了基因组选择性概况和多药学效应.
KinomePro-DL 网络服务器为研究人员提供了一个有价值的工具,用于预测抑制剂配置文件和微调模型.
这种方法加速了更安全,更有效的激酶抑制剂的发现.

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