Reliability of logP Predictions Based on Calculated Molecular Descriptors: A Critical Review

Abstract

Correct QSAR analysis requires reliable measured or calculated logP values, being logP the most frequently utilized and most important physico-chemical parameter in such studies. Since the publication of theoretical fundamentals of logP prediction, many commercial software solutions are available. These programs are all based on experimental data of huge databases therefore the predicted logP values are mostly acceptable - especially for known structures and their derivatives. In this study we critically reviewed the published methods and compared the predictive power of commercial softwares (CLOGP, KOWWIN, SciLogP/ULTRA) to each other and to our recently developed automatic QS(P)AR program. We have selected a very diverse set of 625 known drugs (98%) and drug-like molecules with experimentally validated logP values. We have collected 78 reported "outliers" as well, which could not be predicted by the "traditional" methods. We used these data in the model building and validation. Finally, we used an external validation set of compounds missing from public databases. We emphasized the importance of data quality, descriptor calculation and selection, and presented a general, reliable descriptor selection and validation technique for such kind of studies. Our method is based on the strictest mathematical and statistical rules, fully automatic and after the initial settings there is no option for user intervention. Three approaches were applied: multiple linear regression, partial least squares analysis and artificial neural network. LogP predictions with a multiple linear regression model showed acceptable accuracy for new compounds therefore it can be used for "in-silico-screening" and/or planning virtual/combinatorial libraries.

New carbohydrazide derivatives of 1H-pyrazolo[3,4-b]pyridine and trypanocidal activity

“…Lipophilicity, expressed as logP, has been recognized as molecular descriptor considered in studies of absorption, permeability, hydrophobic interactions with proteins and distribution of a molecule in the biological environment (Van der Waterbeemd et al 1996, Eros et al 2002. Replacing the phenyl group (4b) by methyl (4a) or trifluoromethyl group (4c) decreases lipophilicity and volume values of these molecules (Table I).…”

Acid Dissociation Constants and Molecular Descriptors of Some Xylene Linked Bispyridinium Oximes

“…We can consider this point for our future investigation so as to analyze the difference in predicated data by using different computational programs and discuss the similarity or variances observed. However there are some efforts available in the literature to compare the molecular descriptors calculations based on different softwares [36,37]. The most precise results available are generally obtained from ab-initio calculations, meaning that these calculations tend to converge to an exact mathematical solution.…”

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

“…Lipophilicity and water solubility are properties related to the octanol-water partition coefficient, which can be used as rough early ADME screens to reject probable development failures as early as possible. Clark and co-workers used a data set containing 1085 compounds for developing a neural network model for octanolpartition coefficient prediction from the results of semi empirical AM1 calculations [81] and Eros et al developed neural network (fitting and prediction errors were s = 0.48 and s = 0.72 respectively)from database of 625 molecules, 98% of which are registered API showing high structural diversity [82]. Similarly, more recent studies based on QSPR ANN model predicted octanol-water partition coefficients for 209 chlorinated trans-azobenzene derivatives, contaminants in herbicides [83].…”