Prediction of Aqueous Solubility of a Diverse Set of Compounds Using Quantitative Structure−Property Relationships

Abstract

"Fail early and fail fast" is the current paradigm that the pharmaceutical industry has adopted widely. Removing non-drug-like compounds from the drug discovery lifecycle in the early stages can lead to tremendous savings of resources. Thus, fast screening methods are needed to profile the large collection of synthesized and virtual libraries involved in the early stage. Solubility is one of the filters that are applied extensively to ensure that the compounds are reasonably soluble so that synthesis of the compounds and assay studies of pharmacokinetics and toxicity are feasible. To address this need, we have developed a fast quantitative structure-property relationship (QSPR) model for the prediction of aqueous solubility (at 298 K, unbuffered solution) from the molecular structures. Multiple linear regressions and genetic algorithms were used to develop the models. The model was based on a set of diverse compounds including small organic molecules and drug and drug-like species. The predicted solubility for the training and test sets agrees well with the experimental values. The coefficient of determination is R(2) = 0.84 for the training set of 775 compounds and the RMS error = 0.87. This model was validated on four sets of compounds. The RMS error for the 1665 compounds from the four validation data sets (including compounds from the Physician's Desk References and Comprehensive Medicinal Chemistry databases) is 1 log unit and the unsigned error is 0.77. This model does not require 3-D structure generation which is rather time-consuming. Using 2-D structure as input, this model is able to compute solubility for 90 000-700 000 compounds/h on a SGI Origin 2000 workstation. This kind of fast calculation allows the model to be used in data mining and screening of large synthesized or virtual libraries.

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

“…To our knowledge the current study with more than ϳ193,000 compounds is probably the largest validated metabolic stability model to date. Although there have been a vast number of models generated for ADME/Tox and physicochemical properties such as solubility (Cheng and Merz, 2003;Lind and Maltseva, 2003;Yamashita et al, 2006), even these models have not used such large numbers of compounds. Passive permeability has also been the focus for extensive modeling for many years initially based on Caco-2 or Madin-Darby canine kidney cell data (Segarra et al, 1999;Ekins et al, 2000bEkins et al, , 2001bRen and Lien, 2000;Stenberg et al, 2000).…”

Computational Prediction of Solvation Free Energies of Amino Acids with Genetic Algorithm

“…The accuracy of the present method in predicting molecular solvation free energy is better than that of the QSPR model trained with 775 compounds in which some drug-like properties of organic compounds computed from their 2-D structures were used as molecular descriptors. 28 The quality of the present solvation model is also superior to that of the artificial neural network (ANN) model reported by Liu and So which was trained with 1033 compounds using 19 adjustable variables. 29 The comparisons thus indicate that our GA-based parameterization method would be more efficient in estimating molecular solvation free energies.…”

Molecular Docking Studies of Myricetin and Its Analogues against Human PDK-1 Kinase as Candidate Drugs for Cancer

“…In silico ADME studies solely depend on the chemical structure of molecules. In silico ADMET properties such as ADMET BBB level [21], absorption, aqueous solubility [22] hepatotoxicity [23], CYP2D6 [24], AlogP98 [25] and PSA [26] are studied for the standard compounds from standard data set and further evaluation has been done on test set compounds. A standard ADMET model is generated which predict the human intestinal absorption (HIA) after oral administration of the inhibitors tested.…”