Quantitative structure-activity relationships are often based on standard multidimensional statistical analyses and sophisticated local and global molecular descriptors. Here, the aim is to develop a tool helpful to define a molecule or a class of molecules which fulfills pre-described properties, i.e., an Inverse QSAR approach. If highly sophisticated descriptors are used in QSAR, the structure and then the synthesis recipe may be hard to derive. Thus, descriptors, from which the synthesis recipe can be easily derived, seem appropriate to be included within this study. However, if descriptors simple enough to be useful for defining syntheses recipes of chemicals were used, the accuracy of a numeric expression may fail. This paper suggests a method, based on very simple elements of the theory of partially ordered sets, to find a qualitative basis for the relationship between such fairly simple descriptors on the one side and a series of ecotoxicological properties, on the other side. The partial order ranking method assumes neither linearity nor certain statistical distribution properties. Therefore the method may be more general compared to many standard statistical techniques. A series of chlorinated aliphatic compounds has been used as an illustrative example and a comparison with more sophisticated descriptors derived from quantum chemistry and graph theory is given. Among the results, it was disclosed that only for algae lethal concentration, as one of the four ecotoxicological properties, the synthesis specific predictors seem to be good estimators. For all other ecotoxicological properties quantum chemical descriptors appear as the more suitable estimators.

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In this investigation, a new and simple way to analyze, interpret, and generalize monitoring data of occurrence of pesticide active ingredients in surface waters was developed. The occurrence is quantified using the variables frequency of detection and the concentration level. These two parameters are associated with basically different ecotoxicological effects; for example, a high frequency of detection may be related to bioaccumulation problems, while the level of concentration also controls the acute toxicological effects. The active ingredients were ranked on the basis of the monitoring data in relation to both the frequency of finding and concentration level using the concept of partial ordered sets. The resulting rankings was correlated with other rankings based on descriptors such as sprayed area, applied dose, adsorption to soil organic carbon, vapor pressure, and soil dissipation half-life. A similarity index was applied in order to compare the ranking of the monitoring data with the ranking of the descriptors. It is shown how partial order theory can be used to evaluate the relevance of every single descriptor. The dosage is found to be the most important descriptor, followed by the sprayed area and the adsorption to organic carbon ending up a very close similarity between, respectively, the rankings using monitoring data and rankings using these three descriptors.

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