Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates
Por um escritor misterioso
Last updated 22 dezembro 2024
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
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