Epik

Rapid and robust pKa predictions

The Advantages of Empirical pKa Prediction

Proper treatment of ligand protonation states is essential to lead discovery. The pKa's of a drug's various functional groups play a critical role in determining its bioavailability and pharmacokinetic profile, while virtual screening software relies on correctly protonated structures in order to perceive the discrete interactions that drive ligand binding. However, many readily available libraries provide ligand structures in familiar tautomeric forms with all functional groups neutralized. These forms may not be highly populated under biological conditions, and are therefore inappropriate for property prediction or virtual screening experiments.

Epik provides a time-tested solution to these problems, designed specifically to work within the context of contemporary drug discovery workflows. Using Hammett and Taft methods in conjunction with ionization and tautomerization tools, Epik is able to rapidly and reliably predict pKa values and return all chemically sensible structures.

Support for multiple output structures:
While other software may only be capable of returning a single structure or pKa value, Epik can return pKa values and 3D structure files for multiple tautomers and ionization states that are likely to exist under the specified conditions.

Tautomeric optimization:
Epik is distinguished by its ability to treat tautomeric states without user intervention, an invaluable feature when processing millions of ligands.

Multiple solvent choices:
Epik is parameterized to return accurate pKa values in both water and DMSO.

Multiple prediction modes:
For maximum speed, Epik can predict pKa's for a structure file in its supplied state. For maximum accuracy, Epik can vary the tautomeric and ionization states if necessary.

LigPrep integration:
With a single click, Epik can automatically be employed by LigPrep to enumerate tautomers and protonation states.

Citations and Acknowledgements

Schrödinger Release 2021-3: Epik, Schrödinger, LLC, New York, NY, 2021.

ö Greenwood, J. R.; Calkins, D.; Sullivan, A. P.; Shelley, J. C., "Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution," J. Comput. Aided Mol. Des., 2010, 24, 591-604

ö Shelley, J.C.; Cholleti, A.; Frye, L; Greenwood, J.R.; Timlin, M.R.; Uchimaya, M., "Epik: a software program for pKa prediction and protonation state generation for drug-like molecules," J. Comp. Aided Mol. Des., 2007, 21, 681-691

"Adverse Drug Reactions Triggered by the Common HLA-B*57:01 Variant: A Molecular Docking Study"

Van Den Driessche, G.; Fourches, D., J. Cheminform., 2017, 9 (13), 1-17

"Discovery of Thienoquinolone Derivatives as Selective and ATP Non-Competitive CDK5/p25 Inhibitors by Structure-Based Virtual Screening"

Chatterjee, A.; Cutler, S.J.; Doerksen, R.J.; Khan, I.A.; Williamson, J.S., Bioorg. Med. Chem., 2014, 22, 6409-6421

ö "Boosting virtual screening enrichments with data fusion: Coalescing hits from two-dimensional fingerprints, shape, and docking"

Sastry, G.M.; Inakollu, V.S.; Sherman, W, J. Chem. Inf. Model., 2013, 53, 1531-1542

"Predicting and improving the membrane permeability of peptidic small molecules"

Rafi, S.B.; Hearn, B.R.; Vedantham, P.; Jacobson, M.P.; Renslo, A.R., J. Med. Chem., 2012, 55(7), 3163-3169

ö "Testing physical models of passive membrane permeation"

Leung, S.S.F.; Mijalkovic, J.; Borrelli, K.; Jacobson, M.P., J. Chem. Inf. Model., 2012, 52(6), 1621-1636

"Structure-based design of potent aromatase inhibitors by high-throughput docking"

Caporuscio, F.; Rastelli, G.; Imbriano, C.; Del Rio, A., J. Med. Chem., 2011, 54, 4006–4017

"Estimating binding affinities by docking/scoring methods using variable protonation states"

Park, M.; Gao, C.; Stern, H.A., Proteins, 2011, 79, 304-314

ö "Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution"

Greenwood, J. R.; Calkins, D.; Sullivan, A. P.; Shelley, J. C., J. Comput. Aided Mol. Des., 2010, 24, 591-604

ö "Epik: a software program for pKa prediction and protonation state generation for drug-like molecules"

Shelley, J.C.; Cholleti, A.; Frye, L; Greenwood, J.R.; Timlin, M.R.; Uchimaya, M., J. Comp. Aided Mol. Des., 2007, 21, 681-691
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