Computational insight into the chemical space of plant growth regulators

• Published in: Phytochemistry (Oxford) 2016-02, Vol.122, p.254-264
• Main Authors: Bushkov, Nikolay A., Veselov, Mark S., Chuprov-Netochin, Roman N., Marusich, Elena I., Majouga, Alexander G., Volynchuk, Polina B., Shumilina, Daria V., Leonov, Sergey V., Ivanenkov, Yan A.
• Format: Article
• Language: English
• Subjects: Agrochemicals; Agrochemicals - chemistry; Arabidopsis - chemistry; Arabidopsis thaliana; Brassicaceae; Databases, Factual; Herbicides - chemistry; In silico modeling; In vivo screening; Kohonen; Molecular Structure; Pesticides; Pesticides - chemistry; Phytoestrogens - chemistry; Plant Growth Regulators; Self-organizing maps
• ISSN: 0031-9422; 1873-3700
• DOI: 10.1016/j.phytochem.2015.12.006

To overview chemical space of plant growth regulators and pesticides, Kohonen self-organizing maps was applied. Advanced modeling was supported by experimental screening using Arabidopsis thaliana. [Display omitted] •Representative set of agrochemicals was collected and processed.•Advanced computational technique was applied to analyze the data.•For validation, screening on Arabidopsis thaliana was performed; 27 hits discovered.•The model is suitable for the selection of compounds for experimental assessment. 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. The resulting dataset was then thoroughly analyzed using Kohonen-based self-organizing technique. The overall aim of the presented study is to investigate whether the developed in silico model can be applied to predict the agrochemical activity of small molecule compounds and, at the same time, to offer further insights into the distinctive features of different agrochemical categories. The preliminary external validation with several plant growth regulators demonstrated a relatively high prediction power (67%) of the constructed model. This study is, actually, the first example of a large-scale modeling in the field of agrochemistry.