Prediction of Membrane Permeation of Drug Molecules by Combining an Implicit Membrane Model with Machine Learning.

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 predi

SUBMITTER: Brocke SA 

PROVIDER: S-EPMC6433486 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature