SSLDTI:一种基于自我监督学习的药物向相互作用预测的新方法.
Zhixian Liu1, Qingfeng Chen2, Wei Lan2
1School of Electronics and Information Engineering, Beibu Gulf University, Qinzhou, Guangxi, China.
Artificial intelligence in medicine
|March 10, 2024
概括
这项研究引入了一种新的图形自编码器和自我监督学习模型,以改善药物向相互作用预测. 该模型有效地解决了数据限制和类不平衡问题,优于现有方法.
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
- 机器学习是机器学习.
背景情况:
- 确定药物向相互作用 (DTI) 对于加速药物开发至关重要.
- 图形神经网络 (GNN) 是有前途的,但在特征提取方面面临局限性,原因是深层架构中的聚合或过度平滑不足.
- 一个重大挑战是已知的DTI的稀缺性,导致预测建模中的严重类不平衡.
研究的目的:
- 开发一个先进的计算模型来准确预测DTI.
- 克服现有的GNN在捕获全面图形特征方面的局限性.
- 为了解决DTI数据集固有的阶级失衡问题.
主要方法:
- 提出了一个混合模型,将图形自编码器和自我监督学习 (SSL) 结合起来.
- 该模型使用有限的标记数据高效地编码多层图形特征.
- 积极的样本补偿系数被整合到目标函数中,以减轻阶级不平衡.
主要成果:
- 拟议的SSLDTI模型在两个数据集中与四种基线方法相比显示出更高的性能.
- 实验结果证实了该模型在准确预测DTI方面的有效性.
- 通过SSLDTI模型新预测的DTI被使用DrugBank数据库验证.
结论:
- 开发的模型有效地捕捉了用于DTI预测的复杂图形特征.
- 该方法成功地减轻了药物向相互作用数据集中的类不平衡问题.
- 这种方法为增强药物发现管道提供了一个有前途的计算策略.
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