Partial order ranking-based QSAR's: estimation of solubilities and octanol-water partitioning

Partial order ranking appears as an attractive alternative to conventional Quantitative Structure Activity Relationships (QSAR) methods, the latter ty pically relying on the application of statistical methods. The method seems attractive as a priori knowledge of specific functional relationships is n ot required. In the present study, it is demonstrated that QSAR models base d on a partial order ranking approach can be used satisfactorily to predict solubilities and octanol-water partitioning for a selection of organic com pounds exhibiting different structural and electronic characteristics. The uncertainty is validated using well-established LSER descriptors. Two requi rements to the model with regard to precision prevail, i.e., the model must be able to rank the single compounds in the basis set correctly compared t o the experimental data, and the model should be based on a basis set of co mpounds large enough to secure a satisfactorily fine-meshed net, taking the number of descriptors into account. In the present study, the model was ab le to rank 318 out of 319 comparisons correctly in the case of solubilities . The corresponding figures for the octanol-water partitioning were 407 out of 408. The precision and the uncertainties of the method which, were foun d closely related to the mutual interplay between the number of compounds a nd the number of descriptors is discussed in terms of the number of descrip tors and compounds involved. The limitations of the method are discussed. ( C) 2001 Elsevier Science Ltd. All rights reserved.