Sosnina Ekaterina A, Sosnin Sergey, Nikitina Anastasia A, Nazarov Ivan, Osolodkin Dmitry I, Fedorov Maxim V
Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia.
Institute of Physiologically Active Compounds, RAS, Severniy pr. 1, Chernogolovka 142432, Russia.
ACS Omega. 2020 Jun 21;5(25):15039-15051. doi: 10.1021/acsomega.0c00857. eCollection 2020 Jun 30.
Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes ("interactions") for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery.
推荐系统（RSs）经历了快速发展并对电子商务产生了巨大影响，它有潜力成为药物发现的有用工具。在本文中，我们应用推荐系统方法来预测从ChEMBL中提取的化合物的抗病毒活性类别（活性/非活性）。应用了两种主要的推荐系统方法：协同过滤（Surprise实现）和基于内容的过滤（稀疏组归纳矩阵完成（SGIMC）方法）。研究了推荐系统方法在预测化合物和病毒的抗病毒活性类别（“相互作用”）方面的有效性，对于这些化合物和病毒，它们与其他病毒或化合物的一些相互作用是已知的，同时也研究了在预测新化合物的相互作用概况方面的有效性。通过交叉验证和外部验证接收器操作特征（ROC）得分>0.9量化，这两种方法在个体相互作用和化合物概况的二元分类方面都取得了相对较好的预测质量。因此，即使是简单的推荐系统也可以作为抗病毒药物发现的有效工具。
