推进药物向相互作用预测:一个全面的基于图表的方法,集成知识图嵌入和ProtBert预训练.
Warith Eddine Djeddi1,2, Khalil Hermi3, Sadok Ben Yahia4,5
1LR11ES14, Faculty of Sciences of Tunis, University of Tunis El Manar, Campus Universitaire, 2092, Tunis, Tunisia. waritheddine.jeddi@isikef.u-jendouba.tn.
这项研究介绍了DTIOG,这是一种用于预测药物向相互作用 (DTI) 的新型计算方法. DTIOG将知识图嵌入与蛋白质序列上下文相结合,以准确识别潜在的DTI,加速药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 药物向相互作用 (DTI) 的验证是昂贵和耗时的,限制了实验验证.
- 精确的计算方法对于加速药物发现和预测潜在的DTI至关重要.
- 基于图形的机器学习和知识图形嵌入 (KGE) 正在成为DTI预测的强大工具.
研究的目的:
- 开发和评估一种新的计算方法,DTIOG,用于预测药物向相互作用 (DTI).
- 利用知识图嵌入和上下文蛋白序列信息来提高DTI预测的准确性.
主要方法:
- DTIOG采用两步知识图嵌入 (KGE) 过程来计算嵌入向量.
- 从变压器获得蛋白质双向编码器表示 (ProtBERT) 用于从蛋白质序列中提取上下文信息并评估目标与目标的相似性.
- 该方法整合了来自药物SMILES字符串和蛋白质氨基酸序列的局部表示.
主要成果:
- DTIOG在对酶,离子通道和G蛋白结合受体的数据集中预测DTI方面表现出高效.
- 该方法在使用各种相似度和分类器时,在DTI预测准确度方面超过了现有的算法.
- 在所有测试的数据集中,DTIOG的稳定性和准确性都得到了一致的观察.
结论:
- DTIOG是一种可靠且准确的方法,用于预测药物向相互作用.
- 拟议的方法显示了通过识别新型DTI来加速药物发现的巨大潜力.
- DTIOG可以成为制药领域的研究人员的宝贵工具.
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