Development of Reliable Aqueous Solubility Models and Their Application in Druglike Analysis | doi.page

0 citations0 references

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

Journal of Chemical Information and Modeling2007Vol. 47(4), pp. 1395–1404

Citations Over TimeTop 10% of 2007 papers

Junmei Wang, George A. Krudy, Tingjun Hou, Wei Zhang, George W. Holland, Xiaojie Xu

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-one-out 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).

Citations Over TimeTop 10% of 2007 papers

Abstract

Related Papers