Implementation of Simulated Annealing-Support Vector Machine on QSAR Study of Fusidic Acid Derivatives as Anti-Malarial Agent

Abstract

Malaria is a disease caused by the Plasmodium falciparum parasite and leads to many cases of deaths. Recently, the combination of several drugs has been used to treat this disease. However, the parasite is known to be resistant to the anti-malarial agent. Hence, a new candidate for an anti-malarial drug is required to solve the resistance problem. One compound that is promising as an anti-malarial agent is fusidic acid derivatives. Fusidic acid is an antibiotic that is work by preventing parasite growth. Besides, fusidic acid is known to have antiplasmodial activity although the IC50 is still poor. However, the activity can be improved by optimizing the structure through its derivatives. In this study, we developed a QSAR model to predict the activity of fusidic acid derivatives as anti-malarial agent. The model was developed by using Simulated Annealing (SA) for feature selection and Support Vector Machine (SVM) for model development. The results show that SA produces a satisfying combination of features that are indicated by the trend of MSE value during the selection process. Regarding the performance, SVM with RBF kernel produces the best result of the validation parameter. This indicates that the model is valid to be used to predict a compound with unknown activity values for anti-malarial agents.