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

Structures of P-glycoprotein in different conformations (A) Domain

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

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

Molecules, 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

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression, BMC Chemistry

Interfacial Tension–Temperature–Pressure–Salinity Relationship for the Hydrogen–Brine System under Reservoir Conditions: Integration of Molecular Dynamics and Machine Learning

Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble

Novel Energy Transduction in P-glycoprotein