Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Bioinformatics. 2023 Sep 2;39(9). doi: 10.1093/bioinformatics/btad514.
Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines.
In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model.
The codes of MSDRP are available at https://github.com/xyzhang-10/MSDRP.
癌症异质性极大地影响癌症治疗效果。体外预测药物反应有望帮助制定个性化治疗方案。近年来，提出了几种基于机器学习和深度学习的计算模型来预测体外药物反应。然而，这些方法中的大多数基于单一药物描述（例如药物结构）捕获药物特征，而不考虑药物与生物实体（例如靶标、疾病和副作用）之间的关系。此外，这些方法中的大多数分别为药物和细胞系收集特征，但未能考虑药物和细胞系之间的两两相互作用。
在本文中，我们提出了一种名为 MSDRP 的深度学习框架，用于药物反应预测。MSDRP 使用交互模块来捕获药物和细胞系之间的相互作用，并通过相似网络融合算法整合药物和生物实体之间的多种关联/相互作用，在所有实验的所有性能指标上均优于一些最先进的模型。从头测试和独立测试的实验结果表明，我们的模型对新药具有出色的性能。此外，几个案例研究说明了使用来自多源数据的药物相似性矩阵的特征向量来表示药物的合理性以及我们模型的可解释性。
MSDRP 的代码可在 https://github.com/xyzhang-10/MSDRP 上获得。
