MOLECULAR DOCKING AND MACHINE LEARNING APPROACH TO ELUCIDATE Mentha aquatica PHYTOCHEMICALS ROLE IN BREAST CANCER TREATMENT

Abstract

With rising cases of breast cancer and developing resistance to available drugs for treatment, the need for novel drug inhibitors that produce an effective response becomes crucial. Phytochemicals are currently being studied for their therapeutic effects on a wide range of diseases. The present study focuses on targeting and inhibiting CDK6, HER2 and ER proteins that are upregulated in breast cancer by phytochemicals present in the plant Mentha aquatica commonly referred to as the water mint. It has an abundance of phytochemicals that have wonderful healing properties and are used to alleviate stress, reduce gastrointestinal discomfort, reduce joint and muscle aches, etc. The present study examines the phytochemical profile of Mentha aquatica and its possible role in treatment for breast cancer via in-silico studies. Molecular docking of 84 compounds was done to analyse their binding affinities towards the target proteins using AutoDock Vina. The results are validated using the PLIP Protein ligand interaction profiler, and validation and classification by machine learning algorithms on the basis of IC50 values finally gave us 11 active compounds with binding affinities similar to or slightly better than the standard inhibitors selected for the study. Furthermore, ADMET analyses were done to predict the drug likeness via Lipinski’s rule of 5, bioavailability, toxicity, and absorption of these compounds for predicting their efficacy against breast cancer as medicative agents.