[SMILESynergy:基于变压器预训练模型的抗癌药物协同作用预测]
Liqiang Zhang1,2, Yufang Qin1,2, Ming Chen1,2
1College of Information, Shanghai Ocean University, Shanghai 201306, P. R. China.
概括
这项研究介绍了SMILESynergy,这是一种深度学习模型,可以预测癌症治疗中的药物协同作用. 它增强了药物组合查,可能克服对单一疗法的耐药性.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 机器学习在药物发现中的作用
背景情况:
- 对单一药物疗法的获得性耐药性在治疗癌症等复杂疾病方面构成了重大挑战.
- 药物组合为克服耐药性和改善治疗结果提供了一个有希望的策略.
- 了解药物分子相互作用对于开发有效的组合疗法至关重要.
研究的目的:
- 开发基于变压器的深度学习模型,SMILESynergy,用于预测药物组合的协同效应.
- 研究不同药物分子之间的相互作用对抗癌药物疗效的影响.
- 为快速选最佳药物组合提供一个工具.
主要方法:
- 使用简化分子输入线输入系统 (SMILES) 字符串来表示药物分子.
- 雇员 SMILES 数字化用于数据增强以生成药物分子异构体.
- 在变压器架构中应用注意力机制来编码和解码药物表示.
- 集成了一个多层感知子 (MLP) 来预测药物协同效应值.
主要成果:
- 在回归任务中,SMILESynergy模型实现了平均平方误差为51.34.
- 在分类任务中表现出0.97的高精度.
- 在预测性能方面超越了DeepSynergy和MulinputSynergy等现有模型.
结论:
- SMILESynergy提供了一个强大的深度学习方法,用于预测药物协同作用.
- 该模型的增强预测性能可以帮助研究人员识别有效的药物组合.
- 有助于更快地选最佳药物组合,以改善癌症治疗结果.
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