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. 2014 Sep 12;6(1):42.
doi: 10.1186/s13321-014-0042-6. eCollection 2014 Dec.

Quantitative estimation of pesticide-likeness for agrochemical discovery

Sorin Avram  1 , Simona Funar-Timofei  1 , Ana Borota  1 , Sridhar Rao Chennamaneni  2 , Anil Kumar Manchala  2 , Sorel Muresan  3
Affiliations

Quantitative estimation of pesticide-likeness for agrochemical discovery

Sorin Avram et al. J Cheminform. .

Abstract

Background: The design of chemical libraries, an early step in agrochemical discovery programs, is frequently addressed by means of qualitative physicochemical and/or topological rule-based methods. The aim of this study is to develop quantitative estimates of herbicide- (QEH), insecticide- (QEI), fungicide- (QEF), and, finally, pesticide-likeness (QEP). In the assessment of these definitions, we relied on the concept of desirability functions.

Results: We found a simple function, shared by the three classes of pesticides, parameterized particularly, for six, easy to compute, independent and interpretable, molecular properties: molecular weight, logP, number of hydrogen bond acceptors, number of hydrogen bond donors, number of rotatable bounds and number of aromatic rings. Subsequently, we describe the scoring of each pesticide class by the corresponding quantitative estimate. In a comparative study, we assessed the performance of the scoring functions using extensive datasets of patented pesticides.

Conclusions: The hereby-established quantitative assessment has the ability to rank compounds whether they fail well-established pesticide-likeness rules or not, and offer an efficient way to prioritize (class-specific) pesticides. These findings are valuable for the efficient estimation of pesticide-likeness of vast chemical libraries in the field of agrochemical discovery. Graphical AbstractQuantitative models for pesticide-likeness were derived using the concept of desirability functions parameterized for six, easy to compute, independent and interpretable, molecular properties: molecular weight, logP, number of hydrogen bond acceptors, number of hydrogen bond donors, number of rotatable bounds and number of aromatic rings.

Keywords: Agrochemicals; Fungicide; Herbicide; Insecticide; Pesticide; SAR databases.

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Figures

Figure 1
Figure 1
Frequency counts and desirability function plots of herbicides. Histograms and desirability functions (red curve, see right scale) of six molecular descriptors, i.e., MW (molecular weight), LogP (log of the octanol–water partition coefficient), HBA (number hydrogen bond acceptors), HBD (number hydrogen bond donors), RB (number of rotatable bonds), arR (number of aromatic rings) computed for the herbicides subset.
Figure 2
Figure 2
Frequency counts and desirability function plots of insecticides. Histograms and desirability functions (red curve, see right scale) of six molecular descriptors, i.e., MW (molecular weight), LogP (log of the octanol–water partition coefficient), HBA (number hydrogen bond acceptors), HBD (number hydrogen bond donors), RB (number of rotatable bonds), arR (number of aromatic rings), computed for the insecticides subset.
Figure 3
Figure 3
Frequency counts and desirability function plots of fungicides. Histograms and desirability functions (red curve, see right scale) of six molecular descriptors, i.e., MW (molecular weight), LogP (log of the octanol–water partition coefficient), HBA (number hydrogen bond acceptors), HBD (number hydrogen bond donors), RB (number of rotatable bonds), arR (number of aromatic rings), computed for the fungicides subset.
Figure 4
Figure 4
Comparative representation of desirability functions. Desirability function curves describing the three classes of pesticides: herbicides, insecticides and fungicides, in terms of MW (molecular weight), LogP (log of the octanol–water partition coefficient), HBA (number hydrogen bond acceptors), HBD (number hydrogen bond donors), RB (rotatable bonds), and arR (number of aromatic rings); dark grey –overlapping area described by the three curves; light grey – maximum area described by the three curves.
Figure 5
Figure 5
Basic molecular properties of herbicides, insecticides and fungicides selected from AgroSAR. Comparative distribution plots of AgroSAR selected herbicides (green), insecticides (blue) and fungicides (red), in terms of MW (molecular weight), LogP (log of the octanol–water partition coefficient), HBA (number hydrogen bond acceptors), HBD (number hydrogen bond donors), RB (rotatable bonds), and arR (number of aromatic rings).
Figure 6
Figure 6
Evaluation of AgroSAR pesticides. (a) Cumulative frequencies of AgroSAR pesticide sets (herbicides – green, insecticides – blue, fungicides – red, pesticides – orange) plotted against quantitative estimates scores and performance of Tice's, Hao's and Lipinski's rule-based approaches as describes in Table 1 (rule-type performances are represented independent from the x-axis score values) (b); ROC curves showing the discriminative power of the scoring functions (c); frequency distributions of herbicides (left), insecticides (middle) and pesticides (right) in terms of quantitative estimates scores and frequencies corresponding to compounds passing rule-based models (in red percentages of compounds passing rule-based filters per cutoff). In the panels: QEH, Quantitative estimate of herbicide-likeness; QEI, Quantitative estimate of insecticide-likeness; QEF, Quantitative estimate of fungicide-likeness; QEP, Quantitative estimate of pesticide-likeness; QEPmax and QEPavg, - the maximum and the average of QEH, QEI and QEF values, respectively.
Figure 7
Figure 7
Examples of highly scored AgroSAR pesticides. Chemical representation of AgroSAR herbicides (a), insecticides (b) and fungicides (c) and quantitative estimation scores in parenthesis.
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