Development of Reliable Aqueous Solubility Models and Their Application in Druglike Analysis

Abstract

In this work, two reliable aqueous solubility models, ASMS (aqueous solubility based on molecular surface) and ASMS-LOGP (aqueous solubility based on molecular surface using ClogP as a descriptor), were constructed by using atom type classified solvent accessible surface areas and several molecular descriptors for a diverse data set of 1708 molecules. For ASMS (without using ClogP as a descriptor), the leave-oneout q 2 and root-mean-square error (RMSE) were 0.872 and 0.748 log unit, respectively. ASMS-LOGP was slightly better than ASMS (q 2 ) 0.886, RMSE ) 0.705). Both models were extensively validated by three cross-validation tests and encouraging predictability was achieved. High throughput aqueous solubility prediction was conducted for a number of data sets extracted from several widely used databases. We found that real drugs are about 20-fold more soluble than the so-called druglike molecules in the ZINC database, which have no violation of Lipinski's "Rule of 5" at all. Specifically, oral drugs are about 16-fold more soluble, while injection drugs are 50-60-fold more soluble. If the criterion of a molecule to be soluble is set to -5 log unit, about 85% of real drugs are predicted as soluble; in contrast only 50% of druglike molecules in ZINC are soluble. We concluded that the two models could be served as a rule in druglike analysis and an efficient filter in prioritizing compound libraries prior to high throughput screenings (HTS).

Open Source Drug Discovery with Bioclipse

“…Of primary aim in ADME modeling is to promote candidates presenting ADME-properties within certain intervals, such as the well known (but debated) Lipinski rule of having an octanol-water partition coefficient (logP) of less than 5 [51]. Several ADME models for Bioclipse Decision Support are available, including water solubility [52], Blood-Brain-Barrier Penetration [53], and P-Glycoprotein (PgP) Inhibition [54].…”

Analysis of the solubility of betaine: calculation of descriptors and physicochemical properties

“…If such a method was realised, then it would be an important step to the estimation of properties of analogs of betaine. Wang et al [7,8] have determined the solubility of betaine in a number of solvents and we have converted their mol fraction solubilities into solubilities on the molarity scale in mol dm À3 at 298 K. The solubility in water has been reported by Huuskonen [9] and listed by Wang et al [10] as log Cw/1 mol dm À3 ) = 0.72. The interest in these solubilities is that we have shown from a series of solubilities in a number of solvents that it is possible to extract the Abraham descriptors of a molecule [11,12].…”

Application of Random Forest and Multiple Linear Regression Techniques to QSPR Prediction of an Aqueous Solubility for Military Compounds

“…This coefficient was estimated in only a limited number of the studies. [18,20,21] Moreover, it is still necessary to test the ability of each method to predict the aqueous solubility of external validation sets of compounds. But some authors have ignored this fact and erroneously estimated the predictive ability of their method by R 2 or Q 2 values.…”

“…However, these models were mainly devoted to drug or drug-like compounds and thus cannot be used for solubility prediction of military compounds, which are structurally dissimilar. Finally, for computational methods that are based on a direct quantum-mechanical evaluation of solubility, [19][20][21][22][23] it is still necessary to use physicochemical characteristics as descriptors and to employ potentially time-consuming software for solubility modeling. Sometimes such approaches cannot process large and diverse sets of compounds.…”