p K a Prediction Using Group Philicity
ABSTRACT Acid-base dissociation constants (pK(a) values) are important in understanding the chemical, environmental and toxicological properties of molecules. Though various methods have been developed to predict pK(a) by experimental and theoretical models, prediction of pK(a) is still complicated. Hence, a new approach for predicting pK(a) using the group philicity concept has been attempted. Presence of known functional groups in a molecule is utilized as the most important indicator to predict pK(a). The power of this descriptor in describing pK(a) for the series of carboxylic acids, various substituted phenols, anilines, phosphoric acids, and alcohols is probed. Results reveal that the group electrophilicity is suitable for effectively predicting the pK(a) values.
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ABSTRACT: Conceptual density functional theory provides a method for analyzing the chemical reactivity in terms of different global reactivity descriptors including electrophilicity and their local counterparts like philicity. These concepts’ definitions and various applications that demonstrate their interpretive power are discussed. Global and local electrophilicities are able to account for a wide variety of chemical phenomena.Annual Reports Section C"" (Physical Chemistry)""" 04/2009; 105. DOI:10.1039/b802832j
Article: Electrophilicity Index[Show abstract] [Hide abstract]
ABSTRACT: The electrophiliciy index proposed by Parr, Szentpaly, and Liu provides insight into almost every arena of chemistry and encompases information about the structure, properties, stability, reactivity, interactions, bonding, toxicity, and dynamics of many electron systems in ground and excited electronic states. The whole gamut of the conceptual density functional theory lends support toward the electrophilicity index and helps it realize its full potential. As is the case for most of the conceptual DFT-based reactivity/selectivity descriptors, the global and local electrophilicities possess strong interpretive power.Chemical Reviews 07/2006; 106(6):2065-91. DOI:10.1021/cr040109f · 46.57 Impact Factor
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ABSTRACT: Most pharmacologically active molecules contain one or more ionizing groups, and it is well-known that knowledge of the ionization state of a drug, indicated by the pKa value, is critical for understanding many properties important to the drug discovery and development process. The ionization state of a compound directly influences such important pharmaceutical characteristics as aqueous solubility, permeability, crystal structure, etc. Tremendous advances have been made in the field of experimental determination of pKa, in terms of both quantity/speed and quality/accuracy. However, there still remains a need for accurate in silico predictions of pKa both to estimate this parameter for virtual compounds and to focus screening efforts of real compounds. The computer program SPARC (SPARC Performs Automated Reasoning in Chemistry) was used to predict the ionization state of a drug. This program has been developed based on the solid physical chemistry of reactivity models and applied to successfully predict numerous physical properties as well as chemical reactivity parameters. SPARC predicts both macroscopic and microscopic pKa values strictly from molecular structure. In this paper, we describe the details of the SPARC reactivity computational methods and its performance on predicting the pKa values of known drugs as well as Pfizer internal discovery/development compounds. A high correlation (r2=0.92) between experimental and the SPARC calculated pKa values was obtained with root-mean-square error (RMSE) of 0.78 log unit for a set of 123 compounds including many known drugs. For a set of 537 compounds from the Pfizer internal dataset, correlation coefficient r2=0.80 and RMSE=1.05 were obtained.Molecular Pharmaceutics 08/2007; 4(4):498-512. DOI:10.1021/mp070019+ · 4.38 Impact Factor