Membrane Permeability
Physics-based, accurate predictions of passive membrane permeability
The Advantage of a Physics-based Model
Unlike QSAR-based modeling, physics-based models offer more robustness across diverse chemistries, due to the models’ ability to address conformation dependent phenomena such as internal hydrogen-bonding, which can have a dramatic effect on permeability but cannot be accurately modeled by QSAR approaches.
Macrocycle support:
Automatic detection and sampling of macrocycles via Prime’s advanced macrocycle sampling algorithm.
Partition energy “dG” Insert prediction:
Purely physics-based model, excellent for congeneric datasets.
RRCK cell-based assay Papp prediction:
Physics-based model tuned to predict RRCK (MDCK-LE) assay logPapp values.
Citations and Acknowledgements
Schrödinger Release 2020-2: Prime, Schrödinger, LLC, New York, NY, 2020.
ö Leung, S. S. F., Sindhikara, D. J., and Jacobson, M. P., "Simple Predictive Models of Passive Membrane Permeability Incorporating Size-Dependent Membrane-Water Partition," J. Chem. Inf. Model., 2016, 56(5), 924–929
ö Leung, S.S.F, Mijalkovic, J., Borrelli, K., and Jacobson, M.P., "Testing Physical Models of Passive Membrane Permeation," J. Chem. Inf. Model., 2012, 52(6), 1621-1636
ö Rezai, T., Yu, B., Millhauser, G.L., Jacobson, M.P., and, and Lokey, R.S., "Testing the Conformational Hypothesis of Passive Membrane Permeability Using Synthetic Cyclic Peptide Diastereomers," J. Am. Chem. Soc. Model., 2006, 128(8), 2510–2511
