Machine learning (ML) is widely applied in medicinal chemistry and pharmaceutical industry. Chemoinformatics and molecular ML have been used for decades for safer, faster drug design. However, the important area of agrochemistry has been relatively neglected. New regulations, with strong focus on ecotoxicology, necessitate creation of novel, safer pesticides.

In this talk, I will describe how and why we can apply ML in predictive ecotoxicology, and how those models can be applied in agrochemistry. In particular, I will present ApisTox, a novel dataset about pesticide bee toxicity, how we can construct such datasets from publicly available data sources, and what are the challenges.

Then, we will cover predictive ML applications in ecotoxicology, and how to apply data science tools for agrochemical data. Examples include molecular fingerprints, graph kernels, and graph neural networks. We will also discuss quantitative measures for describing differences between medicinal chemistry and agrochemistry, and how it impacts practical results.