Investigation, design and synthesis of new anticancer agents with anticancer effect potential on MCF-7 Breast Cancer Cells by Machine Learning Method

Abstract

Cancer is one of the diseases with a high mortality rate, which occurs when cells multiply uncontrollably, acquire an invasive character and metastasize. Breast cancer is one of the cancer types with an increasing incidence worldwide. Chemotherapy is a method used in the treatment of cancer diseases, and the chemotherapeutic drugs used inhibit the growth and proliferation of cancer cells due to their cytotoxic properties. Today, machine learning techniques offer significant advantages by helping several steps of the drug discovery process, reducing the time spent in the laboratory, the use of consumables and chemical materials, and the maximum time predicted for the discovery of a drug with traditional methods. In our study, it was aimed to determine the 3 Schiff base derivatives with the most active cytotoxic effect on breast cancer cells from the large data set using machine learning. In our study, 7 Schiff base derivatives were determined from a large data set containing 98 compounds, and the 3 most active compounds with cytotoxic properties on breast cancer cells and their IC50 values were determined by machine learning method. In the future, it is thought that compound 1 can be used as an alternative to pharmacological applications to be used in preclinical studies as a therapeutic agent, supported by in vitro and in vivo applications, in order to be used in cancer treatments.

Keywords

• Machine Learning
• Drug Discovery
• Breast Cancer
• MCF-7

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