多模式特征融合用于骨毒性预测和局部平台
Zhijie Pan1, Xin Yang2, Churong Wang1
1School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China.
Journal of chemical information and modeling
|November 20, 2025
概括
我们开发了BTP-MFFGNN,一种新的AI模型,通过整合分子和图形特征来预测药物诱导的骨质毒性. 这种方法提高了识别骨毒性风险的准确性,以便更安全地开发药物.
科学领域:
- 计算毒理学计算毒理学
- 药物安全 药物安全
- 骨的新陈代谢 骨的新陈代谢
背景情况:
- 药物诱导的骨质毒性在临床实践和药物开发中存在重大风险.
- 现有的机器学习模型难以捕捉复杂的结构-毒性关系.
研究的目的:
- 开发一种新的多式联络预测模型,用于准确的骨毒性预测.
- 解决传统方法在捕获分子结构-毒性相互作用方面的局限性.
主要方法:
- 策划了一个专门的骨毒性数据集.
- 拟议的BTP-MFFGNN是一个多式模式,集成分子指纹和基于图形的特征.
- 采用了一个定制的图形神经网络,具有注意力机制和适应性门融合.
主要成果:
- 在骨毒性预测方面,BTP-MFFGNN取得了显著的改进.
- 实现了0.85的精度 (ACC) 和0.92.9的曲线下面积 (AUC).
- 与之前的最佳模型相比,ACC和AUC分别增加了13%和8%.
结论:
- BTP-MFFGNN为骨毒性评估提供了一个有效的计算框架.
- 该模型为更安全的药物发现和机制驱动毒理学提供了宝贵的支持.
- 为了实际应用,开发了一个可访问的平台OsteoToxPred.
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