混合DTA:通过扩展混合与因关联罪恶感的混合来改善药物向亲和度的预测
Youngoh Kim1, Dongmin Bang2,3, Bonil Koo2,3
1Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
Bioinformatics (Oxford, England)
|July 15, 2025
概括
混合DTA通过使用预训练模型和一种新的数据增强策略来提高药物向亲和力 (DTA) 的预测. 这种方法提高了准确性,特别是对于稀疏的数据和新的药物标对来说,加速了药物发现.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 药物向亲和力 (DTA) 预测对于药物发现至关重要,它比二元相互作用预测提供了更多的洞察力.
- 目前的DTA预测方法面临着由于数据稀缺,稀缺以及新药或蛋白质的"冷启动"问题所带来的挑战.
研究的目的:
- 引入MixingDTA,这是一个旨在克服数据稀缺性和DTA预测数据稀缺性的框架.
- 提高DTA预测的准确性和通用性,特别是对于未见的药物标对.
主要方法:
- 利用MEETA (基于MolFormer和ESM的效率聚合变压器) 模型中的分子和蛋白质的域特定预训练语言模型.
- 实施了GBA-Mixup,这是一个新的数据增强策略,基于GBA原则进行实体嵌入.
主要成果:
- 与最先进的基线相比,仅MEETA骨干的平均平方误差就提高了19%.
- 添加GBA-Mixup进一步提高了8.4%的性能,并且不依赖模型,在不同的骨干中产生高达16.9%的收益.
- 案例研究表明,MixingDTA能够对未见的药物标对进行概括,并识别关键残留物.
结论:
- 混合DTA有效地解决了DTA预测中的数据稀缺性和稀疏性.
- 该框架提供了准确,可扩展和生物知情的预测,加速了药物发现管道.
- GBA-Mixup为DTA预测提供了显著的,与模型无关的性能改进.
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