A machine learning approach in predicting mosquito repellency of plant – derived compounds

Abstract

The increasing prevalence of mosquito – borne diseases has prompted intensified efforts in the prevention of being bitten by the vector. Among the various strategies of vector control, the application of repellents provides instant and effective protection from mosquitoes. However, emerging concerns regarding the safety of the widely used repellent, DEET, has led to initiatives to explore natural alternatives. In order to fully realize the potential of natural repellents, focusing on the discovery of natural compounds eliciting repellency is of paramount importance. In this paper, machine learning was utilized to establish association between the mosquito repellent activity of 33 natural compounds using 20 chemical descriptors. Individually, the descriptors had insignificant monotonic relationship with the response variable. But when optimized, the formulated model through boosted trees regression exhibited reliable predictive ability (r²_train = 0.93, r²_test = 0.66, r²_overall = 0.87). The findings presented have also introduced new descriptors that exhibited association with repellency through ensemble learning such as heat capacity, Log P, entropy, enthalpy, Gibb’s free energy, energy, and zero-point energy.