EGFR <sup>AP</sup> : a predictive machine learning model for assessing small molecule activity against the epidermal growth factor receptor
Ashish Gupta, Amarinder Singh Thind, Rituraj Purohit
Abstract
values was 0.82. The model was probed for overfitting using 10-fold cross-validation, and a series of structure-based drug design experiments were performed to validate the tool's predictions. The findings backed up the model's performance. This tool will be of significant importance to medicinal chemists in identifying promising EGFR inhibitors.
Topics & Concepts
Epidermal growth factor receptorSmall moleculeEpidermal growth factorFactor (programming language)Drug discoveryCancer researchEGFR inhibitorsChemistryDrugPharmacologyReceptorComputer scienceComputational biologyBiologyBiochemistryProgramming languageComputational Drug Discovery MethodsHER2/EGFR in Cancer ResearchLung Cancer Treatments and Mutations