通过使用图形注意力神经网络,通过弹头参与药物目标相互作用的水平来预测PROTAC非目标效应
Yutong Hu1,2, Kieran Didi3, Adam P Cribbs4
1Thrust of Bioscience and Biomedical Engineering, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China.
Computational and structural biotechnology journal
|November 17, 2025
概括
一个新的深度学习框架SENTINEL预测了针对蛋白质溶解向仿真体 (PROTACs) 的非目标蛋白质降解风险. 该工具有助于在药物开发的早期优先考虑更安全的PROTAC弹头.
科学领域:
- 生物化学和药理学 生物化学和药理学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 向蛋白质分解的仿真体 (PROTACs) 为向蛋白质降解提供了治疗潜力.
- 在PROTAC开发过程中,非标蛋白质降解是一个重要的安全问题.
研究的目的:
- 开发一个计算框架,SENTINEL,用于预测PROTAC弹头的偏离目标倾向.
- 评估基于图形的深度学习对PROTAC非目标效应建模的实用性.
主要方法:
- 使用基于图形的深度学习方法,将PROTAC弹头编码为分子图形.
- 采用了路径增强图形转换器网络 (PAGTNs) 和基于图形的注意力神经网络 (GAT).
- 利用已建立的数据库和文献来获取药物向相互作用数据.
主要成果:
- 在模拟基于结合计数的非目标效应方面取得了高精度,AUC为0.9600,F1得分为0.6983.
- 超越了传统的机器学习模型,如随机森林 (AUC=0.840,F1分数=0.2778).
- 证明了SENTINEL在数据有限的场景中优先考虑低风险弹头的可扩展性.
结论:
- SENTINEL提供了一种有效的计算策略,用于对PROTAC目标外影响的早期风险评估.
- 该框架支持优先考虑更安全的PROTAC弹头,帮助治疗开发.
- 这些发现强调了深度学习在预测药物诱导的蛋白质降解负担方面的潜力,尽管数据集大小的限制需要谨慎和进一步验证.
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