The epidermal growth factor receptor (EGFR) is a transmembrane glycoprotein that constitutes one of four members of the erbB family of tyrosine kinase receptors. EGFR is over-expressed in a variety of tumor cell lines and has been associated with poor prognosis and decreased survival rates. The most common pharmacology approach to inhibiting EGFR has been to develop small-molecule inhibitors that bind to the intracellular portion of the receptor to prevent tyrosine kinase phosphorylation and subsequent activation of signal transduction pathways. The present study aimed to identify high-affinity compounds targeting EGFR with safer pharmacological profiles through lead-based virtual screening approaches combined with machine learning classification models and molecular docking. A collection of EGFR inhibitors were served as the query reference molecules for identification of structurally similar database compounds by Tanimoto-based similarity searching against the PubChem database. PubChem BioAssay data is used as a training and test set for developing classification models that distinguish active and inactive compounds against EGFR. The algorithms used include Random Forest, Supported Vector Machine, and Multilayer Perceptron. The highest-ranked database compounds were subjected to the classification model for predicting their bioactivities and the compounds passed both filters were successively docked upon EGFR for calculating the binding affinities. The final candidates were also further subjected to Lipinski and Verber's filter to confirm both lead-like and drug-like properties.

Lead-based virtual screening and prediction of EGFR inhibitors using PubChem’s database with data mining and machine learning algorithms

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.