药物Tar通过整合大型语言模型和基因本体学来改善药物可用性预测
Niloofar Borhani1,2, Iman Izadi1, Ali Motahharynia2,3
1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
Bioinformatics (Oxford, England)
|June 29, 2025
概括
我们开发了DrugTar,这是一个深度学习工具,使用蛋白质序列嵌入更有效地预测药物点. 这种方法简化了复杂疾病的目标发现,优于现有的方法.
科学领域:
- 计算生物学是一种计算生物学.
- 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:
- 目标发现对于开发复杂慢性疾病治疗方法至关重要.
- 计算可药性预测方法有希望,但往往依赖于有限的基于序列的特征.
- 现有的机器学习方法面临特征工程,可重现性和可访问性方面的挑战.
研究的目的:
- 调查原蛋白序列和结构对于药物可用性预测的有用性.
- 开发一种高性能计算方法,用于预测药物点.
- 提高药物开发中目标发现的效率.
主要方法:
- 利用深度学习技术整合了蛋白质序列嵌入 (ESM-2) 和基因本体学.
- 开发了DrugTar,一种用于预测目标药物适应性的新算法.
- 利用蛋白质序列和结构数据进行模型训练和验证.
主要成果:
- 蛋白质序列嵌入被证明比蛋白质结构更有信息,用于药物可用性预测.
- 药物Tar实现了高性能,其AUC和PRC值为0.94.
- 药物Tar在预测药物可用性方面超过了现有的最先进的方法.
结论:
- 使用先进的深度学习,DrugTar有效地预测药物向药物可用性.
- 开发的算法简化了目标发现,解决了治疗开发中的一个关键瓶.
- 药物Tar提供了一个强大的,可访问的工具,以加速药物发现研究.
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