Article ; Online: Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning.
Journal of chemical information and modeling
2018 Volume 59, Issue 3, Page(s) 1147–1162
Abstract: Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the ... ...
| Abstract | Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the predictions was done against experimental permeability coefficients from Parallel Artificial Membrane Permeability Assays (PAMPA), and effects of partial charge sets, CGenFF, AM1-BCC, and OPLS, on the performance of the predictions were discussed. (D)HDGB-based models improved the predictions over the two-state implicit membrane models, and partial charge sets seemed to have a strong impact on the predictions. Machine learning increased the accuracy of the predictions, although it could not outperform the physics-based approach in terms of correlations. |
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| MeSH term(s) | Cell Membrane Permeability/drug effects ; Drug Evaluation, Preclinical ; Machine Learning ; Models, Biological ; Thermodynamics |
| Language | English |
| Publishing date | 2018-12-27 |
| Publishing country | United States |
| Document type | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S. |
| ZDB-ID | 190019-5 |
| ISSN | 1549-960X ; 0095-2338 |
| ISSN (online) | 1549-960X |
| ISSN | 0095-2338 |
| DOI | 10.1021/acs.jcim.8b00648 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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