通过保持嵌入物之间的原始拓关系来准确预测药物蛋白相互作用
Yanfei Li1, Xiran Chen2, Shuqin Wang3
1College of Computer Science, Nankai University, 300071, Tianjin, China.
BMC biology
|August 6, 2025
概括
这项研究介绍了GLDPI,这是一种用于预测药物蛋白相互作用 (DPI) 的新型模型,它在不平衡的数据上表现出色. GLDPI显著提高了对新型相互作用的预测准确性和概括性,优于现有方法.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 机器学习在药物发现中的作用
背景情况:
- 基于学习的方法显示出预测药物蛋白相互作用 (DPI) 的前景.
- 现有的方法与不平衡的数据集作斗争,限制了准确性,概括性和可扩展性.
- 这些局限性阻碍了DPI预测的实际应用.
研究的目的:
- 开发一个高度通用的模型,GLDPI,用于对不平衡数据集进行准确的DPI预测.
- 在嵌入空间内的分子表示中保存拓关系.
- 为了提高预测准确性和模型在现实世界的场景中的适用性.
主要方法:
- GLDPI使用专用编码器来获取药物和蛋白质序列信息,生成嵌入表示.
- 代数相似性用于有效计算DPI的可能性.
- 基于因关联罪名的先前损失函数,确保嵌入空间拓与药物-蛋白质网络结构保持一致.
主要成果:
- 在不平衡的基准数据集上,GLDPI表现出卓越的表现,在AUPR中实现了100%以上的改进.
- 该模型在冷启动实验中显示出异常的概括性,用于预测新型药物与蛋白质相互作用.
- GLDPI表现出了显著的可扩展性,在不到10小时内推断出大约1.2 × 10 ^ 10的药物蛋白对.
结论:
- 通过捕捉网络关系和分子相互作用特征,GLDPI显著提高了预测性能.
- 该模型为准确和可扩展的DPI预测提供了强大的解决方案,特别是在具有挑战性的不平衡场景中.
- GLDPI在泛化和可扩展性方面的能力为药物发现中的更广泛应用铺平了道路.
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