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

Pharmaceuticals, Free Full-Text

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

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

Machine learning for small molecule drug discovery in academia and industry - ScienceDirect

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

Molecular modeling of human P-gp structure. (a) 3D Structure of human

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

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

Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning

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

BioSimLab - Research

Novel Energy Transduction in P-glycoprotein

ADMET Evaluation in Drug Discovery. 13. Development of in Silico Prediction Models for P-Glycoprotein Substrates

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

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