顶级DTI:集成拓深度学习和大型语言模型用于药物向相互作用预测
Muhammed Talo1,2,3, Serdar Bozdag1,2,3,4
1Department of Computer Science and Engineering, University of North Texas, Denton, TX 76207, USA.
bioRxiv : the preprint server for biology
|February 20, 2025
概括
顶级DTI集成了拓数据分析与大型语言模型,以预测药物向相互作用,提高药物发现效率. 这种新的框架提高了预测准确性和稳定性,优于现有方法.
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:
- 准确的药物向相互作用 (DTI) 预测对于有效的药物发现至关重要.
- 传统药物开发是漫长而昂贵的.
- 计算方法加快了候选人的识别.
研究的目的:
- 提出Top-DTI,这是一个用于预测DTI的新型框架.
- 将拓数据分析 (TDA) 与大型语言模型 (LLM) 整合起来.
- 为了提高DTI预测的准确性和稳定性.
主要方法:
- 使用持久同质学从蛋白质接触图和药物分子图像中提取拓特征.
- 使用蛋白质和药物LLM从序列和SMILES字符串中生成嵌入.
- 结合拓和LLM衍生特征用于DTI预测.
主要成果:
- 顶级DTI在BioSNAP和人类DTI数据集上的性能优于最先进的方法.
- 在冷分裂场景中实现了卓越的性能,证明了强度.
- 整合拓特征显著改善了预测性能.
结论:
- 顶级DTI为DTI预测提供了一种强大而准确的方法.
- 拓和LLM特征的整合是非常有效的.
- 这一框架加速了对潜在的候选药物的识别.
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