Accurate pKa prediction and automatic structure modification is critical for many computational chemistry methods which are strongly dependent on the tautomerization and protonation state of the structures, including docking, binding affinity estimation, QSAR and ADME modelling, and metabolism prediction. MoKa implements a novel approach [1] for in-silico computation of pKa values; trained using a very diverse set of more than 25000 pKa values, it provides accurate and fast calculations using an algorithm based on descriptors derived from GRID molecular interaction fields.
MoKa
STORCHI, LORIANO;
2009-01-01
Abstract
Accurate pKa prediction and automatic structure modification is critical for many computational chemistry methods which are strongly dependent on the tautomerization and protonation state of the structures, including docking, binding affinity estimation, QSAR and ADME modelling, and metabolism prediction. MoKa implements a novel approach [1] for in-silico computation of pKa values; trained using a very diverse set of more than 25000 pKa values, it provides accurate and fast calculations using an algorithm based on descriptors derived from GRID molecular interaction fields.File in questo prodotto:
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