Abstract

Herbicides are biocidal chemical compounds used to kill weeds primarily by inhibiting their growth [1]. Weeds can cause serious damage in agriculture, resulting in critical losses of yield, quality, and profit. Herbicide residues have been found on food for human consumption, and some are dangerous to human health. For example, paraquat has been banned or severely restricted in many countries due to concerns over its effect on the environment and on people’s health. The discovery of less toxic compounds that prevent the growth of weeds could greatly impact food sustainability as well as allow for a safer environment for humans and crops alike. The goal of this research is to take known herbicides and, using molecular modeling, modify these molecules in order to identify herbicides with more potent weed killing abilities but with reduced environmental impact. This is done by training an Artificial Neural Network (ANN) software to predict the IC₅₀ values of the newly designed herbicides. The half-maximal inhibitory concentration (IC₅₀) of an herbicide is an informative measure of an herbicide’s effectiveness [2]. A lower IC₅₀ value means that the herbicide is effective at low concentration. In this research we will model approximately 200+ modifications to 16 known herbicides. This should provide several molecules that will have superior weed-killing properties but with much less environmental impact.

Keywords: IC₅₀; Artificial neural network; Artificial intelligence; Gaussian molecular modeling software

Abbreviations: ANN: Artificial Neural Network; AI: Artificial Intelligence; IC₅₀: Inhibition Concentration at 50%; LD₅₀: Lethal Dose at 50%; LUMO: Lowest Unoccupied Molecular Orbital; HOMO: Highest Occupied Molecule Orbital