Computational insight into the chemical space of plant growth regulators.

An enormous technological progress has resulted in an explosive growth in the amount of biological and chemical data that is typically multivariate and tangled in structure. Therefore, several computational approaches have mainly focused on dimensionality reduction and convenient representation of high-dimensional datasets to elucidate the relationships between the observed activity (or effect) and calculated parameters commonly expressed in terms of molecular descriptors. We have collected the experimental data available in patent and scientific publications as well as specific databases for various agrochemicals.