多模态拓意识图形神经网络,用于强大的化学蛋白相互作用预测
Jianshi Wang1,2
1Department of Systems Innovation, Graduate School of Engineering, Hongo Campus, The University of Tokyo, Tokyo 113-8656, Japan.
International journal of molecular sciences
|September 13, 2025
概括
这项研究介绍了MM-TCoCPIn,这是一个用于预测化学蛋白相互作用 (CPI) 的新型网络. 它集成了网络拓,生物医学语义和3D蛋白质结构,以提高药物发现的准确性和可解释性.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 预测化学蛋白相互作用 (CPI) 对药物发现至关重要,但在有限的数据下具有挑战性.
- 现有的方法经常与稀疏或杂的生物信息作斗争.
研究的目的:
- 开发一个可靠和可解释的模型来预测CPI.
- 整合多样化的数据模式,以提高预测准确度.
主要方法:
- 介绍了MM-TCoCPIn,一个多模式拓意识的化学蛋白相互作用网络.
- 综合网络拓 (CTC编码器),生物医学语义 (SciBERT) 和3D蛋白质结构 (AlphaFold2图表上的GVP-GNN).
主要成果:
- 在STITCH,STRING和PubMed数据集上实现了最先进的性能 (AUC = 0.93,F1 = 0.92).
- 证明拓学,语义学和结构都为独特的生物学见解做出了贡献.
- 拓确保了稳定性,语义提高了回忆力,结构提高了精度.
结论:
- MM-TCoCPIn为CPI建模提供了一个可扩展和可解释的解决方案.
- 该框架有效地将预测准确性与药物发现中的机制理解联系起来.
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