Computational insight into the chemical space of plant growth regulators Nikolay A. Bushkov a,⇑ , Mark S. Veselov a,b,c , Roman N. Chuprov-Netochin a , Elena I. Marusich a , Alexander G. Majouga b,c , Polina B. Volynchuk a , Daria V. Shumilina a , Sergey V. Leonov a , Yan A. Ivanenkov a,b,c,d a Moscow Institute of Physics and Technology, 9 Institutskiy Lane, Dolgoprudny, Moscow Region 141700, Russian Federation b Lomonosov Moscow State University, GSP-1, Leninskie Gory, Moscow 119991, Russian Federation c National University of Science and Technology MISiS, 2 Leninskiy Prospect, Moscow 119049, Russian Federation d ChemDiv, 6605 Nancy Ridge Drive, San Diego, CA 92121, USA article info Article history: Received 5 September 2015 Received in revised form 2 December 2015 Accepted 11 December 2015 Available online xxxx Keywords: Arabidopsis thaliana Brassicaceae In silico modeling Kohonen Self-organizing maps In vivo screening Plant growth regulators Pesticides Agrochemicals abstract 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 calcu- lated parameters commonly expressed in terms of molecular descriptors. We have collected the experi- mental 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. Ó 2015 Elsevier Ltd. All rights reserved. 1. Introduction PGRs (plant growth regulators) is a class of compounds that includes natural plant hormones (phytohormones) and their syn- thetic analogs (Basra, 2000). They represent organic molecules that regulate the growth of cultivated plants and are active in different concentrations (Teale et al., 2006). A distinct phytohormone can affect a number of crucial processes occurring in plants thereby promoting their growth and progression. Whereas, a particular process can be controlled by different plant hormones. Commonly, the mechanism of action of these molecules is determined by exogenous application (Gray, 2004). To date, eight classes of natu- ral plant hormones have been described (Fig. 1): auxins, cytokinins, jasmonic acid, abscisic acid, ethylene, gibberellins, brassinosteroids (Nambara and Marion-Poll, 2005) and strigolactones (Dun et al., 2009). The other extensive class of compounds used in agriculture is pesticides, which comprise three groups considered below. Insecti- cides and fungicides defend plants from needless insects and fungi, respectively. Herbicides share about 40–60% of all pesticides and, in many cases, are toxic towards weeds improving crop yield. According to Weed Science Society of America, there are 29 classes of herbicides with different mechanisms of action (Fig. 2). Many papers comprehensively discuss the application and properties of various pesticides (Dayan et al., 2012; Gandy et al., 2015; Santner et al., 2009; Ulrich et al., 2012). Such chemicals are valuable tool for agricultural biotechnology to circumvent the need for genetic engineering that results in cost reduction. How- ever, due to the potential negative environmental impact and the decrease in effectiveness after prolonged application (Adesemoye et al., 2009; Khan et al., 2008), an urgent demand is continuously observed on safer and more effective alternatives. http://dx.doi.org/10.1016/j.phytochem.2015.12.006 0031-9422/Ó 2015 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. E-mail address: bushkov@phystech.edu (N.A. Bushkov). Phytochemistry xxx (2015) xxx–xxx Contents lists available at ScienceDirect Phytochemistry journal homepage: www.elsevier.com/locate/phytochem Please cite this article in press as: Bushkov, N.A., et al. Computational insight into the chemical space of plant growth regulators. Phytochemistry (2015), http://dx.doi.org/10.1016/j.phytochem.2015.12.006