Structure-based virtual screening and molecular docking for the identification of potential multi-targeted inhibitors against breast cancer

基于结构的虚拟筛选和分子对接用于鉴定潜在的乳腺癌多靶点抑制剂

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Abstract

Breast cancer is characterized by an uncontrolled growth of cells in breast tissue. Genes that foster cell growth in breast cells are overexpressed, giving rise to breast tumors. The identification of effective inhibitors represents a rational chemopreventive strategy. The current in silico study provides a pharmacoinformatic approach for the identification of active compounds against a co-chaperone HSP90 and the human epidermal growth factor receptors EGFR and HER2/neu receptor. The elevated levels of expression of these target proteins have been documented in breast cancer. The utilization of drug-likeness filters helped to evaluate the pharmacological activity of potential lead compounds. Those fulfilling this criterion were subjected to energy minimization for 1000 steepest descent steps at a root means square gradient of 0.02 with an Amber ff12SB force field. Based on molecular docking results and binding interaction analysis, this study represents five chemical compounds (S-258282355, S-258012947, S-259417539, S-258002927, and S-259411474) that indicate high binding energies that range between -8.7 to -10.3 kcal/mol. With high cytochrome P inhibitory promiscuity activity, these multi-targeted potential hits portray not only good physiochemical interactions but also an excellent profile of absorption, distribution, metabolism, excretion, and toxicity, which hypothesizes that these compounds can be developed as anticancer drugs in the near future.

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