Combining SILCS and Artificial Intelligence for High-Throughput Prediction of Drug Molecule Passive Permeability Data
Poonam Pandey and Alexander D. MacKerell, Jr
Copyright (c) 2023, University of Maryland Baltimore All Rights Reserved
This release includes training and independent test dataset to create a high-throughput predictive model for passive permeability of drug-like molecules.The input feature vector used to train the developed prediction model includes absolute free energies profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach.
Contents:
the lgfe_perm_input_data_2023 subdirectory holds the feature vector and target (logPm) for the training and independent test datasets.
hyperparameter_optimization.py file allows one to search hyper parameters using grid search.
pred_logP.py file allows one to predict logPm values from training model for random forest, GradientBoosting, LightGBM, and XGBoost regression models.