KLSD:一个精心策划的激酶-连接基因数据库,绘制选择性景观和多药理学
Cheng Chen1, Yuqian Yuan2, Hongyan Li1,3
1School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China.
ACS omega
|March 16, 2026
概括
我们开发了KLSD,一个小分子激酶抑制剂及其活动的数据库,以及一个预测药物发现功效和选择性的双任务模型.
科学领域:
- 药用化学 医学化学
- 计算生物学 计算生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 激酶抑制剂在药物发现中至关重要,但理解它们的选择性和多药理学是复杂的.
- 现有的资源往往缺乏全面的定量活动数据和选择性概况.
- 开发用于酶抑制剂活性和选择性的预测模型是必不可少的.
研究的目的:
- 创建KLSD,一个大规模的小分子激酶抑制剂和它们的定量活性记录的精心策划的数据库.
- 开发一种双任务组合模型,同时预测激酶抑制剂功效 (pAct) 和选择性.
- 为推进激酶抑制剂研究和药物发现提供宝贵的资源.
主要方法:
- 策划了787,213种小分子激酶抑制剂的数据库 (KLSD),在428个人类激酶中记录了180万个定量活动记录.
- 开发了一种使用多分支残留多层感知子 (MLP) 增强各种机器学习和图形网络方法 (SVM,RF,XGBoost,CNN,GCN,GAT,RGCN,VAE) 的双任务组合模型.
- 使用连续强度标签,而不是分类类别,以提高预测分辨率.
主要成果:
- KLSD数据库提供了关于激酶抑制剂选择性和多药学的广泛数据.
- 双任务组合模型在JAK家族 (JAK1/2/3,TYK2) 上进行基准测试时,实现了高分类准确度 (≥0.84每种激酶,整体0.98).
- 该模型在不同的激酶中表现出强烈的概括性.
结论:
- KLSD是激酶抑制剂研究的综合资源,促进了对选择性和多药理学的研究.
- 开发的整体模型准确地预测了激酶抑制剂的强度和选择性,为药物发现提供了强大的工具.
- 无论是KLSD数据库还是预测模型,都可供研究界免费使用.
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