Protein kinase inhibitors' classification using K-Nearest neighbor algorithm. (June 2020)

• Record Type: Journal Article
• Authors: Arian, Roya; Hariri, Amirali; Mehridehnavi, Alireza; Fassihi, Afshin; Ghasemi, Fahimeh
• Is Part Of: Computational biology and chemistry. Volume 86(2020)
• Publication Date: 2020-06
• Subjects: Protein kinase -- QSAR -- Classification model -- K-nearest neighbor -- Genetic algorithm; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107269
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Protein kinases are enzymes acting as a source of phosphate through ATP to regulate protein biological activities. Inhibiting protein kinases with an active small molecule plays a significant role in cancer treatment. To achieve this aim, QSAR model, is one of the best economical approaches to reduce time and save in costs. Genetic algorithm and K-Nearest Neighbor method were suggested as QSAR model. The outputs of the proposed model demonstrated significant superiority to the SVM and Naïve Bayesian algorithms. Abstract: Protein kinases are enzymes acting as a source of phosphate through ATP to regulate protein biological activities by phosphorylating groups of specific amino acids. For that reason, inhibiting protein kinases with an active small molecule plays a significant role in cancer treatment. To achieve this aim, computational drug design, especially QSAR model, is one of the best economical approaches to reduce time and save in costs. In this respect, active inhibitors are attempted to be distinguished from inactive ones using hybrid QSAR model. Therefore, genetic algorithm and K-Nearest Neighbor method were suggested as a dimensional reduction and classification model, respectively. Finally, to evaluate the proposed model's performance, support vector machine and Naïve Bayesian algorithm were examined. The outputs of the proposed model demonstrated significant superiority to other QSAR models.