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基于细胞和药物分子结构的融合,预测协同药物组合
Shiyu Yan1, Gang Yu2, Jiaoxing Yang3
1Computer School, University of South China, Hengyang, 421001, China. yanshiyu@usc.edu.cn.
Interdisciplinary sciences, computational life sciences
|March 15, 2025
概括
预测协同作用的药物组合对于有效的癌症治疗至关重要. 一种新的深度学习方法,DconnC,通过分析药物分子结构和细胞特征,准确地识别这些组合,减少实验工作量.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 药物组合疗法提高了疗效,减少了副作用,特别是在癌症治疗中.
- 确定协同作用的药物组合是实验密集且具有挑战性的.
- 深度学习方法显示了预测协同药物组合的潜力,减少了实验负担.
研究的目的:
- 开发一种新的计算方法,用于预测协同作用的药物组合.
- 利用细胞特征和药物分子结构来准确预测协同作用.
- 减少识别有效药物组合所需的广泛实验.
主要方法:
- 拟议的药物分子连接细胞 (DconnC) 方法.
- 利用细胞特征作为节点来连接药物分子结构.
- 使用双向循环神经网络 (Bi-RNN) 和长短期记忆 (LSTM) 进行特征优化.
- 综合药物分子结构信息与细胞特征进行预测.
主要成果:
- 与下一个最佳方法相比,DconnC显示平均平方误差 (MSE) 减少了35%.
- 在各种评估指标上取得了卓越的表现.
- 在预测不同细胞系和Loewe协同得分间隔的协同效应方面显著改进.
结论:
- DconnC通过发现药物的分子结构和细胞特征之间的联系,有效地预测协同作用的药物组合.
- 该方法为药物协同效应预测提供了更准确,更有效的方法.
- 这种计算策略可以加速新型组合疗法的发现.
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