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TPpred-CMvL:使用对比多视图学习对多功能治疗的预测

Ke Yan^1,2, Kangrui Xiang¹, Zixu Chen^1,2

¹School of Computer Science and Technology, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Haidian District, Beijing, 100081, China.

|December 30, 2025

概括

一种新的方法,TPpred-CMvL,通过整合蛋白质语言模型和进化信息,准确地预测多功能治疗 (MTP). 这种方法克服了以前方法的局限性,提高了药物发现潜力.

科学领域:

生物化学和生物信息学
药物发现和开发药物发现和开发
计算生物学计算生物学

背景情况:

治疗性可以提供针对性的治疗,并具有最小的副作用,这在现代药物发现中至关重要.
的稳定性和生产成本对制药应用构成重大障碍.
由于传统特征工程的局限性,预测多功能治疗是复杂的.

研究的目的:

开发一种用于准确预测多功能治疗 (MTP) 的新型计算方法.
通过整合多样化的生物序列信息来增强MTP预测.
解决MTP预测方面的挑战,包括数据不平衡.

主要方法:

介绍了TPpred-CMvL,这是一个用于MTP预测的对比多视图学习模型.
集成的TAPE (来自蛋白质大语言模型) 用于语义预训练和PSSM用于进化信息.
使用自适应合成抽样和焦点损失来处理长尾数据分布中的分类不平衡.

主要成果:

与MTP预测的现有方法相比,TPpred-CMvL表现出优异的性能.
对比的多视图学习方法有效地捕获了复杂的序列表示.
该方法在预测多功能治疗方面取得了最先进的准确性.

结论:

一个新的对比的多视图学习模型TPpred-CMvL已成功开发用于MTP预测.
该模型整合了语义和进化信息,实现了最先进的结果.
在http://bliulab.net/TPpred-CMvL上提供TPpred-CMvL的Web服务器,以实现更广泛的访问.

关键词:

对比多视角学习的学习.进化信息是一种进化信息.不平衡的数据不平衡的数据语义信息是语义信息.

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