Identification of new potential candidates to inhibit EGF via machine learning algorithm

Abstract

One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was a different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, the eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs' effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.

Keywords: A431; Drug repositioning; EGF; In-cell ELISA; MTT assay; Machine learning; Tyrosine kinase phosphorylation.