Rani Pooja, Dutta Kamlesh, Kumar Vijay
Computer Science and Engineering Department, National Institute of Technology, Hamirpur, HP, 177005, India.
Information Technology Department, Dr. B R Ambedkar National Institute of Technology Jalandhar, Punjab, 144027, India.
Comput Biol Chem. 2024 Dec;113:108273. doi: 10.1016/j.compbiolchem.2024.108273. Epub 2024 Nov 6.
Drug combination emerges as a viable option for the treatment of malignant diseases. Drug combination outperforms monotherapy by improving therapeutic efficacy, reducing toxicity, and overcoming drug resistance. To find viable drug combinations it is difficult to traverse empirically because of enormous combinational space. Machine learning and deep learning approaches are used to uncover novel synergistic drug combinations in enormous combinational space. Here, AESyn, a novel autoencoder-based drug synergy framework for malignant diseases using a bag of words encoding is proposed. The bag of word encoding technique is used to extract drug-targeted genes. The framework utilized screening data from NCI-ALMANAC, and O'Neil datasets. Autoencoders take drug embeddings with drug-targeted genes as input for processing. The autoencoder in the proposed framework is used to extract drug features. The proposed framework is evaluated on classification and regression metrics. The performance of the proposed framework is compared with existing methods of drug synergy. According to the findings, the proposed framework achieved high performance with an accuracy of 95%, AUROC of 94.2%, and MAPE of 7.2. The autoencoder-based framework for malignant diseases using an encoding technique provides a stable, order-independent drug synergy prediction.
药物联合疗法已成为治疗恶性疾病的一种可行选择。药物联合疗法在提高治疗效果、降低毒性和克服耐药性方面优于单一疗法。由于组合空间巨大，凭经验遍历寻找可行的药物组合很困难。机器学习和深度学习方法被用于在巨大的组合空间中发现新的协同药物组合。在此，提出了一种名为AESyn的新型基于自动编码器的恶性疾病药物协同框架，该框架使用词袋编码。词袋编码技术用于提取药物靶向基因。该框架利用了来自NCI-ALMANAC和奥尼尔数据集的筛选数据。自动编码器将带有药物靶向基因的药物嵌入作为输入进行处理。所提出框架中的自动编码器用于提取药物特征。该框架在分类和回归指标上进行了评估。将所提出框架的性能与现有的药物协同方法进行了比较。根据研究结果，所提出的框架取得了高性能，准确率为95%，曲线下面积为94.2%，平均绝对百分比误差为7.2%。使用编码技术的基于自动编码器的恶性疾病框架提供了稳定的、与顺序无关的药物协同预测。
