###### Abstract  
Molecular docking in the pharmaceutical industry is a powerful in silico approach for discovering novel therapies for unmet medical needs predicting drug-target interactions. It not only provides binding affinity between drugs and targets at the atomic level, but also elucidates the fundamental pharmacological properties of specific drugs. The purpose of this review was to illustrate newer and emergent uses of docking when combined with in vitro techniques for drug discovery in metastatic breast cancer. We grouped the selected articles into five main categories; namely, systematic repositioning of drugs, natural drugs, new synthesized molecules, combinations of drugs, and drug latentiation. We focused on new promising drugs that have a good affinity with their targets, thus inducing a favorable biological response. This review suggests that the integration of molecular docking and in vitro studies can accelerate cancer drug discovery showing a good consistency of the results between the two approaches. molecular docking; in vitro; metastatic breast cancer; drug discovery +
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molecular docking. Thus, in silico and in vitro screenings may help to quickly identify the toxicity of the tested drugs/molecules, thus avoiding further steps such as in vivo and preclinical studies (in case of unfavorable results from in silico and in vitro methods) [5]. In silico approaches with docking studies require at least two elements: a protein/drug database and a molecular docking algorithm. Protein and drug databases are a collection of the structures of proteins and drugs. The rapidly increasing number of structures has created big data, which offer a wide range of biological and chemical information and are a recent opportunity to develop better knowledge of the relationships between drugs and targets (usually proteins), drugs and diseases, and targets and diseases. However, although the available data are often heterogeneous and incomplete, computational methods can exploit this knowledge to deepen these interactions [8]. Given the cost and time consumption of experimental methods, high-performing computational algorithms for drug discovery processes are needed. The computational technique known as \"docking\" can predict the binding of drug-target complexes, as well as the conformation of the ligand upon binding to a protein target. The binding free energy of target-drug interactions establishes the affinity of an association and the conditions for forming a complex. Ranked binding free energies are not always precise, but they can be used to select new drugs such as small molecules to be experimentally tested in a virtual screening approach [9; 10; 11]. Small molecules are promising new drugs with a low molecular weight, which allows them to penetrate cells easily [12]. In addition, molecular docking can be also used for predicting the effects of a drug; for example, the identification of an undesired interaction between a compound and off-targets. To date, 57,000 abstracts/papers have been published on molecular docking, indicating the importance of this computational method in drug development [13; 14; 15]. Despite encouraging results, the real condition of the cellular environment, such as the pH and temperature, cannot be fully replicated in a docking study. Each docking algorithm has its limitations and advantages. Therefore, it has been reported that a binding free energy that integrates the results from different docking algorithms can lead to a higher performance in a virtual screening process [16]. Moreover, molecular docking, being a structure-based method, is limited to receptors and ligands with a known stable structure. Thus, the integration of in vitro and in vivo studies as a validation step of in silico methods is an indispensable part of the drug discovery process. These techniques can study different aspects of potential drugs, such as absorption, regulation of targets, metabolic stability, and toxicity [17].