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kinCSM-RTK:在药物发现中对受体氨酸激酶抑制剂的基于机器学习的查

Adam Serghini^1,2, Yunzhuo Zhou^1,2, Yoochan Myung^1,2

¹The Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane 4072, Australia.

Journal of chemical information and modeling

|December 22, 2025

概括

我们开发了kinCSM-RTK,这是一种机器学习工具,用于预测小分子对受体氨酸激酶 (RTK) 的抑制作用. 该工具通过更有效地识别有前途的候选药物来帮助药物发现.

科学领域:

生物化学生物化学
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:

受体氨酸激酶 (RTKs) 调节关键的细胞功能,如分化,迁移和增殖.
失调的RTK活性与癌症和神经系统疾病等疾病有关.
小分子抑制剂是管理过度活跃RTKs的条件的重要疗法.

研究的目的:

解决RTK抑制剂临床前药物开发中高成本和时间的挑战.
开发一种计算工具,用于预测小分子对RTKs的抑制作用.
为了促进RTK向治疗的潜在药物候选者的有效入围.

主要方法:

开发了kinCSM-RTK,集成了两个机器学习模型.
模型预测pKi和pIC50值,量化小分子对RTKs的抑制功效.
通过十倍交叉验证和独立的盲测验验证模型性能.

主要成果:

获得了pKi的高预测性能 (Pearson的r=0.773为10倍CV,0.762为盲测).
证明了强大的pIC50预测 (Pearson的r=0.773对于10倍CV,0.768对于盲测).
后期分析显示,芳香相互作用与更强的RTK抑制相关.

结论:

kinCSM-RTK为预测RTK抑制剂功效提供了一个强大的和可通用的方法.
这些发现为RTK药物设计的结构-活性关系提供了洞察力.
这些模型可以通过Web服务器公开访问,用于更广泛的研究用途.

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