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 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.
Key Features:
accurate: prediction error of 0.4 log units (0.7 for novel
structures)
fast: more than one million predictions per hour
automatic: output top N most abundant species at a
designated pH
self-trainable: incorporate additional experimental data to
improve accuracy

http://www.moldiscovery.com

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