使用化学结构和蛋白质序列预测药物相互作用的有毒副作用
Liyuan Zhang1, Yongxin Sheng1, Jinxiang Yang1
1School of Public Health, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China.
Scientific reports
|December 29, 2024
概括
这项研究介绍了TSEDDI,一种模型,使用化学结构和蛋白质数据准确预测药物相互作用 (DDI) 的毒性副作用. 通过早期识别可能有害的药物组合,TSEDDI提高了临床试验的安全性.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 药物组合可能会导致严重的有毒副作用,导致临床并发症和退出市场.
- 准确预测药物相互作用 (DDI) 诱导的毒性对于患者安全和药物开发至关重要.
研究的目的:
- 开发和验证一种模型,TSEDDI,用于识别具有诱导有毒副作用高风险的药物对.
- 改善在临床环境中早期检测不良反应.
主要方法:
- 利用卷积神经网络 (CNN) 来从药物化学结构和蛋白质数据 (酶,载体,点) 中提取特征.
- 实现了一个加权的二进制交叉损失函数来解决类不平衡.
- 集成的多头注意力机制与剩余连接,以提高模型性能.
主要成果:
- 在预测DDI副作用方面,TSEDDI模型获得了0.9059 (±0.0010) 的高准确性,超过了基线模型.
- 在各种评估指标上表现出一致的优异表现.
- 一个案例研究验证了该模型对特定药物对预测药物诱导毒性的潜在机制的能力.
结论:
- 通过利用先进的深度学习技术,TSEDDI有效地预测药物相互作用的有毒副作用.
- 该模型的架构结合了注意力机制和剩余连接,提高了对不良DDI效应的检测.
- TSEDDI是临床试验中早期识别不良反应的宝贵工具,有可能提高药物安全性.
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