LLMDTA:改善药物目标亲和度的冷启动预测与生物LLM
IEEE transactions on computational biology and bioinformatics
|August 14, 2025
概括
药物标亲和关系的大型语言模型 (LLMDTA) 提高了预测准确性,特别是在新药和蛋白质方面. 这种方法通过克服冷启动场景的传统深度学习模型的局限性来增强药物发现.
科学领域:
- 计算化学和化学信息学
- 生物信息学和计算生物学
- 药物的发现和开发.
背景情况:
- 药物向亲和力 (DTA) 预测对于加速药物开发至关重要.
- 目前的深度学习模型在冷启动场景 (新药/蛋白质) 中扎,原因是从小型数据集进行有限的概括.
- 现有的方法在遇到看不见的分子实体时,往往无法准确预测结合亲和力.
研究的目的:
- 开发一种新的方法,DTA的大型语言模型 (LLMDTA),以解决DTA预测中的冷启动问题.
- 为了利用预先训练的生物语言模型,在DTA预测中改进特征提取.
- 提高DTA预测模型的准确性和概括能力.
主要方法:
- 使用Mol2Vec (分子预训练) 和ESM2 (蛋白质语言模型) 作为特征提取器.
- 使用1D-CNN编码器提取独立的分子特征.
- 设计了一种双线性注意模块,以捕捉交互药物-蛋白质特征,然后进行特征融合以预测亲和力.
主要成果:
- 在热启动和冷启动设置中,LLMDTA在三个基准数据集中表现出高于最先进的基线的性能.
- 在新型蛋白质和新型对预测场景中观察到显著的改善.
- 一个案例研究证实了LLMDTA能够识别新的结合性亲和力,用于未见的药物对抗表皮生长因子受体,通过分子对接验证.
结论:
- 通过集成强大的预训练语言模型,LLMDTA有效地克服了DTA预测中的冷启动挑战.
- 该模型提供了增强的概括性和预测准确性,使其成为现实世界药物发现的实用工具.
- LLMDTA代表了用于预测药物向相互作用的计算方法的重大进步.
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