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

Accurate global machine learning force fields for molecules with hundreds of atoms

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

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Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins

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

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

P-glycoprotein Substrate Models Using Support Vector Machines Based on a Comprehensive Data set

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

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