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PI3K-Seeker:一款基于机器学习的网络工具,用于发现PI3K抑制剂

Francisca Joseli Freitas de Sousa¹, Dinler Amaral Antunes², Geancarlo Zanatta^1,3

¹Postgraduate Programme in Biochemistry, Department of Biochemistry at Federal University of Ceará, Fortaleza 60440-554, CE, Brazil.

|December 8, 2025

概括

我们开发了PI3K-Seeker,这是一个有效地识别潜在的酸氨基3-激酶 (PI3K) 抑制剂的网络服务器. 这种工具有助于发现代谢障碍和癌症的新治疗剂.

科学领域:

生物化学生化学
计算生物学计算生物学
药物发现药物发现药物发现

背景情况:

酸丁醇3酶 (PI3Ks) 在人类新陈代谢中起着至关重要的作用.
PI3Ks的失调与代谢障碍和癌症有关.
发现新的PI3K抑制剂是具有挑战性和昂贵的.

研究的目的:

开发PI3K-Seeker,一个用于识别新型PI3K抑制剂的Web服务器.
为增强抑制剂查创建一个两阶段预测模型.
为大规模化合物分析提供一个用户友好的平台.

主要方法:

利用两阶段的预测过程来识别抑制剂.
使用XGBoost算法进行模型训练.
从不同的数据集中提取了PubChem指纹,用于特征工程.

主要成果:

第一个阶段实现了高性能 (MCC: 0.917,AUC-ROC: 0.993,ACC: 0.917).
第二阶段的研究结果非常出色 (MCC:0.939,AUC-ROC:0.956,ACC:0.994).
PI3K-Seeker有效地完善了强效抑制剂的化合物选择.

结论:

PI3K-Seeker是加速发现PI3K抑制剂的宝贵工具.
网络服务器提供了一个用户友好的界面,用于选多种化合物.
这种方法可以减少与药物发现相关的成本和时间.

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