A Comparative Study of Naive Bayes and k-NN Algorithm for Multi-class Drug Molecule Classification

Drug is a very much essential substance in health care system. Producing a new drug for a disease in the market using traditional method is very time consuming and expensive. Recently, drug design process is sped-up by using computer resources known as Computer-Aided Drug Design (CADD). In drug design, bio-molecules are responsible to produce a new drug. Therefore, molecules identification is an essential part of CADD. In this paper, two machine learning algorithms such as Naive Bayesian (NB) classifier and k-Nearest Neighbors (k-NN) are evaluated to classify multi-class drug molecules. The result reveals that k-NN method shows higher Accuracy and higher Precision compare to NB. Furthermore, Recall and F1-score of k-NN are higher than that of NB.