The focus of this project is on sustainable development of chemical reagents for detection of glyphosate, the most widely used pesticide in the world, which affects the abundance of aquatic life as well as the quality of drinking water for human consumption. We will focus on developing high sensitivity, synthetically easily accessible, non-hazardous reagents for the determination of glyphosate.

To achieve this, we will employ machine learningto generate and evaluate chemical reagents in-silico and allow the algorithms to iteratively learn which structures provide highest sensitivity while having high sustainability index.

The machine learning algorithms will allow evaluation of thousands of chemical reagentswithout producing chemical wasteand the most promising reagents will be synthesized as well as their suitability in the practical analysis of glyphosate will be evaluated.