Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates

Shown are chemical structures of the 6 incorrectly predicted P-gp

PDF] Computational models for predicting substrates or inhibitors of P- glycoprotein.

Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

Pharmaceutics, Free Full-Text

Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates

Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates

Deep learning models for the estimation of free energy of permeation of small molecules across lipid membranes - Digital Discovery (RSC Publishing) DOI:10.1039/D2DD00119E

Cells, Free Full-Text

Development of Simplified in Vitro P-Glycoprotein Substrate Assay and in Silico Prediction Models To Evaluate Transport Potential of P-Glycoprotein

Shown are chemical structures of the correctly predicted non-substrates

Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning