BiGraph-DTA:预测肝脏保护剂与图形卷积网络的药物向相互作用
Arief Sartono1,2, Bambang Riyanto Trilaksono1, Sophi Damayanti3
1School of Electrical Engineering and Informatics Institut Teknologi Bandung (ITB) Bandung Indonesia.
Quantitative biology (Beijing, China)
|February 12, 2026
概括
预测药物标亲和力对于开发肝病治疗方法至关重要. 一个新的模型,BiGraph-DTA,结合了图形和序列网络,以准确识别肝保护性化合物,加速药物发现.
科学领域:
- 药理学和化学信息学
- 计算生物学和药物发现
背景情况:
- 准确预测药物向亲和力 (DTA) 对于识别有效的肝护剂来对抗肝脏疾病至关重要.
- 现有的计算方法往往难以捕捉药物分子和蛋白质标之间的复杂相互作用.
研究的目的:
- 开发和验证BiGraph-DTA,这是一个新的预测模型,用于对肝保护性化合物的DTA评分.
- 加强对肝病治疗的潜在候选药物的鉴定.
主要方法:
- 使用混合深度学习架构,结合图形卷积网络 (GCN) 和双向长短期内存 (BiLSTM) 网络.
- 处理分子结构作为图形和蛋白质序列作为序列数据.
- 在从ChEMBL获得的21,421个肝脏保护性相互作用的精选数据集上训练并评估了该模型.
主要成果:
- BiGraph-DTA显著超过了传统的机器学习 (随机森林,XGBoost) 和现有的深度学习模型 (DeepDTA,GraphDTA).
- 实现了0.7885的平均平方误差,0.7208的R平方值和0.8508.8的一致性指数.
- 在捕捉复杂的依赖关系和相互作用以进行DTA预测方面表现出卓越的能力.
结论:
- BiGraph-DTA模型提供了一个强大的,数据驱动的框架,以加快新型肝保护性化合物的发现.
- 这种方法具有显著的潜力,可以加速肝脏疾病的新疗法开发.
- 强调了将图形和顺序深度学习集成到复杂的药物发现挑战中的力量.
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