Literature DB >> 14741036

ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach.

T J Hou1, K Xia, W Zhang, X J Xu.   

Abstract

A novel method for the estimation of aqueous solubility was solely based on simple atom contribution. Each atom in a molecule has its own contribution to aqueous solubility and was developed. Altogether 76 atom types were used to classify atoms with different chemical environments. Moreover, two correction factors, including hydrophobic carbon and square of molecular weight, were used to account for the inter-/intramolecular hydrophobic interactions and bulkiness effect. The contribution coefficients of different atom types and correction factors were generated based on a multiple linear regression using a learning set consisting of 1290 organic compounds. The obtained linear regression model possesses good statistical significance with an overall correlation coefficient (r) of 0.96, a standard deviation (s) of 0.61, and an unsigned mean error (UME) of 0.48. The actual prediction potential of the model was validated through an external test set with 21 pharmaceutically and environmentally interesting compounds. For the test set, a predictive r=0.94, s=0.84, and UME=0.52 were achieved. Comparisons among eight procedures of solubility calculation for those 21 molecules demonstrate that our model bears very good accuracy and is comparable to or even better than most reported techniques based on molecular descriptors. Moreover, we compared the performance of our model to a test set of 120 molecules with a popular group contribution method developed by Klopman et al. For this test set, our model gives a very effective prediction (r=0.96, s=0.79, UME=0.57), which is obviously superior to the predicted results (r=0.96, s=0.84, UME=0.70) given by the Klopman's group contribution approach. Because of the adoption of atoms as the basic units, our addition model does not contain a "missing fragment" problem and thus may be more simple and universal than the group contribution models and can give predictions for any organic molecules. A program, drug-LOGS, had been developed to identify the occurrence of atom types and estimate the aqueous solubility of a molecule.

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Year:  2004        PMID: 14741036     DOI: 10.1021/ci034184n

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  37 in total

1.  Mono-Alkylated Ligands Based on Pyrazole and Triazole Derivatives Tested Against Fusarium oxysporum f. sp. albedinis: Synthesis, Characterization, DFT, and Phytase Binding Site Identification Using Blind Docking/Virtual Screening for Potent Fophy Inhibitors.

Authors:  Yassine Kaddouri; Farid Abrigach; Sabir Ouahhoud; Redouane Benabbes; Mohamed El Kodadi; Ali Alsalme; Nabil Al-Zaqri; Ismail Warad; Rachid Touzani
Journal:  Front Chem       Date:  2020-12-11       Impact factor: 5.221

2.  Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures.

Authors:  A Varnek; D Fourches; F Hoonakker; V P Solov'ev
Journal:  J Comput Aided Mol Des       Date:  2005-11-16       Impact factor: 3.686

Review 3.  Recent progress in the computational prediction of aqueous solubility and absorption.

Authors:  Stephen R Johnson; Weifan Zheng
Journal:  AAPS J       Date:  2006-02-03       Impact factor: 4.009

4.  Managing, profiling and analyzing a library of 2.6 million compounds gathered from 32 chemical providers.

Authors:  Aurélien Monge; Alban Arrault; Christophe Marot; Luc Morin-Allory
Journal:  Mol Divers       Date:  2006-09-21       Impact factor: 2.943

5.  Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review.

Authors:  Laure Mamy; Dominique Patureau; Enrique Barriuso; Carole Bedos; Fabienne Bessac; Xavier Louchart; Fabrice Martin-Laurent; Cecile Miege; Pierre Benoit
Journal:  Crit Rev Environ Sci Technol       Date:  2015-06-18       Impact factor: 12.561

6.  Inflation of correlation in the pursuit of drug-likeness.

Authors:  Peter W Kenny; Carlos A Montanari
Journal:  J Comput Aided Mol Des       Date:  2013-01-10       Impact factor: 3.686

7.  ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage.

Authors:  Sichao Wang; Youyong Li; Junmei Wang; Lei Chen; Liling Zhang; Huidong Yu; Tingjun Hou
Journal:  Mol Pharm       Date:  2012-03-16       Impact factor: 4.939

8.  A systematic investigation of quaternary ammonium ions as asymmetric phase-transfer catalysts. Application of quantitative structure activity/selectivity relationships.

Authors:  Scott E Denmark; Nathan D Gould; Larry M Wolf
Journal:  J Org Chem       Date:  2011-05-06       Impact factor: 4.354

9.  Interpretable correlation descriptors for quantitative structure-activity relationships.

Authors:  Benson M Spowage; Craig L Bruce; Jonathan D Hirst
Journal:  J Cheminform       Date:  2009-12-24       Impact factor: 5.514

Review 10.  QSPR studies on aqueous solubilities of drug-like compounds.

Authors:  Pablo R Duchowicz; Eduardo A Castro
Journal:  Int J Mol Sci       Date:  2009-06-03       Impact factor: 6.208

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