基于局部亚结构特征及其补充物的药物相互作用预测
Qing Zhou1, Yang Zhang1, Siyuan Wang1
1College of Computer Science, Chongqing University, Chongqing 400044, China.
Journal of molecular graphics & modelling
|June 30, 2023
概括
预测药物相互作用 (DDI) 对患者安全至关重要. 一种新方法,LSFC,通过分析局部药物亚结构并提供可解释的结果,有效地识别潜在的DDI,即使是在冷启动场景中.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 药物特性可以随着联合使用而改变,导致不可预测的药物相互作用 (DDI).
- 准确的DDI预测对于制药研究和患者安全至关重要.
- 现有的预测方法与冷启动场景作斗争,缺乏解释性.
研究的目的:
- 开发一种用于预测药物相互作用 (DDI) 的新方法,解决现有方法的局限性.
- 提高DDI预测的准确性和可解释性,特别是在冷启动场景中.
主要方法:
- 提出了一种名为LSFC的多通道功能融合方法.
- LSFC利用了药物及其补充剂的本地基结构特征.
- 特性被提取,在药物对之间相互作用,并与DDI预测的全球特性集成.
主要成果:
- 在两个真实世界数据集上,LSFC展示了改善的DDI预测性能.
- 该方法在虫启动和冷启动场景中都显示出一致的改进.
- 视觉检查证实了LSFC识别关键药物亚结构的能力,提高了可解释性.
结论:
- LSFC为药物相互作用预测提供了强大的和可解释的方法.
- 该方法有效地克服了与冷启动场景相关的挑战.
- LSFC在计算药理学和药物安全研究领域取得了进展.
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