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

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

Frontiers In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein–Protein Interactions

Differences in ligand-induced protein dynamics extracted from an unsupervised deep learning approach correlate with protein–ligand binding affinities

The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors

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

BioSimLab - Research

Multisite model for P-glycoprotein drug binding. MOLCAD representation

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

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

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

Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2 - ScienceDirect

Effect of the Force Field on Molecular Dynamics Simulations of the Multidrug Efflux Protein P-Glycoprotein

Prediction and characterization of P-glycoprotein substrates potentially bound to different sites by emerging chemical pattern and hierarchical cluster analysis - ScienceDirect