PTB-DDI:基于预训练的tokenizer和BiLSTM模型的药物相互作用预测的准确和简单的框架
Jiayue Qiu1, Xiao Yan1, Yanan Tian1
1Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China.
International journal of molecular sciences
|November 9, 2024
概括
本研究介绍了PTB-DDI,这是一个准确的深度学习框架,通过改进分子表示和特征挖掘来预测药物相互作用 (DDI). 新的PTB-DDI框架显著提高了DDI预测的准确性,为安全的组合治疗提供了宝贵的工具.
科学领域:
- 计算化学和化学信息学
- 药理学和药物发现
- 医疗保健中的人工智能
背景情况:
- 同时使用药物可能导致药物相互作用 (DDI),在临床治疗中构成风险.
- 准确的DDI预测对于预防联合治疗期间不良药物事件至关重要.
- 现有的用于DDI预测的深度学习模型面临着信息丢失和不完整的功能挖掘等挑战.
研究的目的:
- 提出一个准确而简单的框架,PTB-DDI,用于增强药物相互作用预测.
- 解决当前DDI预测模型中分子表示和药物特征提取方面的局限性.
- 在现实数据集上使用双模式配置评估PTB-DDI框架的性能.
主要方法:
- 开发了PTB-DDI框架,集成ChemBerta用于分子表示,双向长期短期记忆 (BiLSTM) 用于上下文感知特征,以及多层感知器 (MLP) 用于非线性关系.
- 在PTB-DDI框架内调查了双模式 (参数共享与参数独立) 的影响.
- 在BIOSNAP和DrugBank数据集上进行了全面的实验,以评估框架的性能.
主要成果:
- 在BIOSNAP和DrugBank数据集上,PTB-DDI框架显示了与基线方法相比的显著改进.
- 实现了高性能指标:AUC-ROC (0.997),PR-AUC (0.995) 和F1得分 (0.984) 在BIOSNAP.
- 取得了强大的绩效指标:AUC-ROC (0.896),PR-AUC (0.873) 和F1得分 (0.826) 在DrugBank.
- 2024年新批准药物的案例研究证实了该框架的预测能力和双重模式的互补性.
结论:
- PTB-DDI框架为药物相互作用预测提供了强大而准确的解决方案.
- 与现有模型相比,拟议的框架有效地克服了信息丢失,并增强了药物特征挖掘.
- 已经开发了一个公开可访问的网站,以促进PTB-DDI框架的使用和可访问性.
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