School of Information and Electronics, 47833 Beijing Institute of Technology , Beijing, China.
Department of Critical Care Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
J Integr Bioinform. 2024 Sep 2;21(3). doi: 10.1515/jib-2024-0026. eCollection 2024 Sep 1.
Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often results in divergent therapeutic responses to analogous anti-cancer drug treatments within the same cohort of cancer patients. Hence, predicting the drug response by analysing the genomic profile characteristics of individual patients holds significant research importance. With the notable progress in machine learning and deep learning, many effective methods have emerged for predicting drug responses utilizing features from both drugs and cell lines. However, these methods are inadequate in capturing a sufficient number of features inherent to drugs. Consequently, we propose a representational approach for drugs that incorporates three distinct types of features: the molecular graph, the SMILE strings, and the molecular fingerprints. In this study, a novel deep learning model, named MCMVDRP, is introduced for the prediction of cancer drug responses. In our proposed model, an amalgamation of these extracted features is performed, followed by the utilization of fully connected layers to predict the drug response based on the IC50 values. Experimental results demonstrate that the presented model outperforms current state-of-the-art models in performance.
药物治疗仍然是治疗肿瘤的主要方法。由于癌症患者之间存在个体差异，包括基因组特征的差异，同一批癌症患者接受类似的抗癌药物治疗时，往往会产生不同的治疗反应。因此，通过分析个体患者的基因组特征来预测药物反应具有重要的研究意义。随着机器学习和深度学习的显著进步，已经出现了许多利用药物和细胞系特征来预测药物反应的有效方法。然而，这些方法在捕捉药物固有特征方面还不够充分。因此，我们提出了一种药物的表示方法，该方法融合了三种不同类型的特征：分子图、SMILE 字符串和分子指纹。在这项研究中，我们引入了一种名为 MCMVDRP 的新型深度学习模型，用于预测癌症药物反应。在我们提出的模型中，这些提取的特征被融合在一起，然后使用全连接层根据 IC50 值预测药物反应。实验结果表明，所提出的模型在性能上优于当前最先进的模型。
