Protein kinase inhibitors’ classification using K-Nearest neighbor algorithm

• Published in: Computational biology and chemistry Vol. 86; p. 107269
• Main Authors: Arian, Roya, Hariri, Amirali, Mehridehnavi, Alireza, Fassihi, Afshin, Ghasemi, Fahimeh
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.06.2020
• Subjects: Algorithms; Antineoplastic Agents - chemistry; Antineoplastic Agents - classification; Bayes Theorem; Classification model; Genetic algorithm; K-nearest neighbor; Protein kinase; Protein Kinase Inhibitors - chemistry; Protein Kinase Inhibitors - classification; QSAR; Quantitative Structure-Activity Relationship; Classification model; Protein kinase; QSAR; Genetic algorithm; K-nearest neighbor
• ISSN: 1476-9271; 1476-928X
• DOI: 10.1016/j.compbiolchem.2020.107269

[Display omitted] •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. 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.