Computational Approaches for the Prediction of pKa Values

Computational Approaches for the Prediction of pKa Values
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Open-source QSAR models for pKa prediction using multiple machine learning approaches

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Summary. The pKa of a compound describes its acidity or basicity and, therefore, is one of its most important properties. Its value determines what form of the. Computational Approaches for the Prediction of pKa Values describes the insights that have been gained on the intrinsic and extrinsic features.

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His research uses computational chemistry to investigate atmospheric and biological chemistry. Paul Seybold, Ph. His research interests center on chemical and biochemical applications of quantum chemistry, molecular structure-activity relationships, luminescence spectroscopy, and cellular automata models of complex systems. Computational Approaches for the Prediction of pKa Values. George C. Shields , Paul G. Truhlar density functional theory deprotonation descriptors determined dielectric dimethyl sulfoxide dissociation constants DMSO electron energy of solvation equation estimate the pKas examined experimental values G.

Seybold partial atomic charges Perrin phenols Phys pKa calculations pKa estimation pKa units pKa values properties proton pyridines QSAR quantum chemical reaction relationship relative pKa semiempirical silanols solvent model species standard structure temperature theoretical thermodynamic cycle thiols tion UAHF water molecules Zhang.