Computational drug repositioning aims to discover new uses of drugs that have been marketed. However, the existing models suffer from the following limitations. Firstly, in the real world, only a minority of diseases have definite treatment drugs. This leads to an imbalance in the proportion of validated drug-disease associations (positive samples) and unvalidated drug-disease associations (negative samples), which disrupts the optimization gradient of the model. Secondly, the existing drug representation does not take into account the behavioral information of the drug, resulting in its inability to comprehensively model the latent feature of the drug. In this work, we propose a balanced matrix factorization with embedded behavior information (BMF) for computational drug repositioning to address the above-mentioned shortcomings. Specifically, in the BMF model, we propose a novel balanced contrastive loss (BCL) to optimize the category imbalance problem in computational drug repositioning. The BCL optimizes the parameters in the model by maximizing the similarity between the target drug and positive disease, and minimizing the similarity between the target drug and negative disease below the margin. In addition, we designed a method to enhance drug representation using its behavioral information. The comprehensive experiments on three computational drug repositioning datasets validate the effectiveness of the above improvement points. And the superiority of BMF model is demonstrated by experimental comparison with seven benchmark models.
翻译:对药物进行重新定位,目的是发现药物的新用途,然而,现有模式受到以下限制:首先,在现实世界中,只有少数疾病有明确的治疗药物,这导致经证实的药物疾病协会(阳性样品)和未经证实的药物疾病协会(阴性样品)的比例不平衡,这扰乱了药物模型的优化梯度;其次,现有药物代表性没有考虑到药物的行为信息,导致其无法全面模拟药物的潜在特征。在这项工作中,我们建议采用均衡矩阵化矩阵化,同时提供内在的行为信息,用于计算药物重新定位,以解决上述缺陷。具体地说,在生物放大系数模型中,我们提出了一种新的平衡的对比性损失(BCL),以优化计算药物重新定位中的分类不平衡问题。BLC通过尽可能扩大目标药物与阳性疾病之间的相似性,并将目标药物与负性疾病之间的相似性最小化。此外,我们设计了一种方法,即利用上述实验性模型来提高药效性模型的升级。