通过分子对接和自我注意力推进生物活性预测
IEEE journal of biomedical and health informatics
|August 23, 2024
概括
这项研究引入了一种新的深度学习模型,即药物向相互作用图神经网络 (DTIGN),通过整合药物向相互作用来改善生物活性预测. DTIGN显著提高了候选药物的预测准确性.
科学领域:
- 计算化学是一种计算化学.
- 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:
- 生物活性预测对于药物发现至关重要,传统上依赖量化结构-活性关系 (QSAR) 模型.
- 现有的深度学习方法主要集中在连接体结构上,忽视了其他关键因素,如药物向相互作用.
- 生物活性受到连接体结构,药物标结合,信号通路和生物环境的复杂相互作用的影响.
研究的目的:
- 开发一种先进的深度学习模型,集成药物向相互作用,以便更准确地预测生物活性.
- 通过改善潜在活性分子的识别,增强早期候选药物查.
- 建立一个新的基准数据集,用于评估蛋白质-连接体复合体的背景下生物活性预测模型.
主要方法:
- 设计了一种药物向相互作用图神经网络 (DTIGN) 模型,将原子间力量纳入分子间图中.
- 在DTIGN中利用多头自我注意,从分子对接数据中识别最佳的结合口袋和姿势.
- 使用来自晶体数据库的有限本地结构的半监督学习来改进生物活性预测.
主要成果:
- 与现有方法相比,DTIGN模型在生物活性预测方面表现优越.
- 在9个领先的基于深度学习的生物活性预测技术中,实现了平均27.03%的性能改善.
- 建立了一个新的基准数据集,以促进对蛋白质-连接体复合体的生物活性预测模型的评估.
结论:
- 整合药物向相互作用可以显著提高生物活性预测模型的准确性.
- DTIGN模型为有效和准确的候选药物查提供了一种强大的新方法.
- 开发的基准数据集将推动计算药物发现和个性化医学的研究.
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