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

Development of an In Silico Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration

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

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

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

Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information

Frontiers Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase

Accelerating drug target inhibitor discovery with a deep generative foundation model

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

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

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