Epigenetic dysregulation in cancers by isocitrate dehydrogenase 2 (IDH2).

Recent advances in genome-wide studies have revealed numerous epigenetic regulations brought about by genes involved in cellular metabolism. Isocitrate dehydrogenase (IDH), an essential enzyme, that converts isocitrate into -ketoglutarate (KG) predominantly in the tricarboxylic acid (TCA) cycle, has gained particular importance due to its cardinal role in the metabolic pathway in cells. IDH1, IDH2, and IDH3 are the three isomeric IDH enzymes that have been shown to regulate cellular metabolism. Of particular importance, IDH2 genes are associated with several cancers, including gliomas, oligodendroglioma, and astrocytomas. These mutations lead to the production of oncometabolite D-2-hydroxyglutarate (D-2-HG), which accumulates in cells promoting tumor growth. The enhanced levels of D-2-HG competitively inhibit α-KG dependent enzymes, inhibiting cell TCA cycle, upregulating the cell growth and survival relevant HIF-1α pathway, promoting DNA hypermethylation related epigenetic activity, all of which synergistically contribute to carcinogenesis. The present review discusses epigenetic mechanisms inIDH2 regulation in cells and further its clinical implications.

Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer.

Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.

Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

BACKGROUND: The current study recognizes the significance of estrogen receptor alpha (ERα) as a member of the nuclear receptor protein family, which holds a central role in the pathophysiology of breast cancer. ERα serves as a valuable prognostic marker, with its established relevance in predicting disease outcomes and treatment responses. METHOD: In this study, computational methods are utilized to search for suitable drug-like compounds that demonstrate analogous ligand binding kinetics to ERα. RESULTS: Docking-based simulation screened out the top 5 compounds - ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569 against the targeted protein. Further, their dynamics studies reveal that the compounds ZINC13377936 and NCI35753 exhibit the highest binding stability and affinity. CONCLUSION: Anticipating the competitive inhibition of ERα protein expression in breast cancer, we envision that both ZINC13377936 and NCI35753 compounds hold substantial promise as potential therapeutic agents. These candidates warrant thorough consideration for rigorous In vitro and In vivo evaluations within the context of clinical trials. The findings from this current investigation carry significant implications for the advancement of future diagnostic and therapeutic approaches for breast cancer.

Virtual Screening of Different Subclasses of Lignans with Anticancer Potential and Based on Genetic Profile.

Cancer is a multifactorial disease that continues to increase. Lignans are known to be important anticancer agents. However, due to the structural diversity of lignans, it is difficult to associate anticancer activity with a particular subclass. Therefore, the present study sought to evaluate the association of lignan subclasses with antitumor activity, considering the genetic profile of the variants of the selected targets. To do so, predictive models were built against the targets tyrosine-protein kinase ABL (ABL), epidermal growth factor receptor erbB1 (EGFR), histone deacetylase (HDAC), serine/threonine-protein kinase mTOR (mTOR) and poly [ADP-ribose] polymerase-1 (PARP1). Then, single nucleotide polymorphisms were mapped, target mutations were designed, and molecular docking was performed with the lignans with the best predicted biological activity. The results showed more anticancer activity in the dibenzocyclooctadiene, furofuran and aryltetralin subclasses. The lignans with the best predictive values of biological activity showed varying binding energy results in the presence of certain genetic variants.

Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFR for the Clinical Treatment of Glioblastoma.

Glioblastoma (GBM) is a WHO Grade IV tumor with poor visibility, a high risk of comorbidity, and exhibit limited treatment options. Resurfacing from second-rate glioma was originally classified as either mandatory or optional. Recent interest in personalized medicine has motivated research toward biomarker stratification-based individualized illness therapy. GBM biomarkers have been investigated for their potential utility in prognostic stratification, driving the development of targeted therapy and customizing therapeutic treatment. Due to the availability of a specific EGFRvIII mutational variation with a clear function in glioma-genesis, recent research suggests that EGFR has the potential to be a prognostic factor in GBM, while others have shown no clinical link between EGFR and survival. The pre-existing pharmaceutical lapatinib (PubChem ID: 208,908) with a higher affinity score is used for virtual screening. As a result, the current study revealed a newly screened chemical (PubChem CID: 59,671,768) with a higher affinity than the previously known molecule. When the two compounds are compared, the former has the lowest re-rank score. The time-resolved features of a virtually screened chemical and an established compound were investigated using molecular dynamics simulation. Both compounds are equivalent, according to the ADMET study. This report implies that the virtual screened chemical could be a promising Glioblastoma therapy.

Structure-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation of VEGF inhibitors for the clinical treatment of Ovarian Cancer.

Vascular endothelial growth factor (VEGF) and its receptor play an important role both in physiologic and pathologic angiogenesis, which is identified in ovarian cancer progression and metastasis development. The aim of the present investigation is to identify a potential vascular endothelial growth factor inhibitor which is playing a crucial role in stimulating the immunosuppressive microenvironment in tumor cells of the ovary and to examine the effectiveness of the identified inhibitor for the treatment of ovarian cancer using various in silico approaches. Twelve established VEGF inhibitors were collected from various literatures. The compound AEE788 displays great affinity towards the target protein as a result of docking study. AEE788 was further used for structure-based virtual screening in order to obtain a more structurally similar compound with high affinity. Among the 80 virtual screened compounds, CID 88265020 explicates much better affinity than the established compound AEE788. Based on molecular dynamics simulation, pharmacophore and comparative toxicity analysis of both the best established compound and the best virtual screened compound displayed a trivial variation in associated properties. The virtual screened compound CID 88265020 has a high affinity with the lowest re-rank score and holds a huge potential to inhibit the VGFR and can be implemented for prospective future investigations in ovarian cancer.

A Multi-target Drug Designing for BTK, MMP9, Proteasome and TAK1 for the Clinical Treatment of Mantle Cell Lymphoma.

BACKGROUND: Mantle cell lymphoma (MCL) is a type of non-Hodgkin lymphoma characterized by the mutation and overexpression of the cyclin D1 protein by the reciprocal chromosomal translocation t(11;14)(q13:q32). AIM: The present study aims to identify potential inhibition of MMP9, Proteasome, BTK, and TAK1 and determine the most suitable and effective protein target for the MCL. METHODOLOGY: Nine known inhibitors for MMP9, 24 for proteasome, 15 for BTK and 14 for TAK1 were screened. SB-3CT (PubChem ID: 9883002), oprozomib (PubChem ID: 25067547), zanubrutinib (PubChem ID: 135565884) and TAK1 inhibitor (PubChem ID: 66760355) were recognized as drugs with high binding capacity with their respective protein receptors. 41, 72, 102 and 3 virtual screened compounds were obtained after the similarity search with compound (PubChem ID:102173753), PubChem compound SCHEMBL15569297 (PubChem ID:72374403), PubChem compound SCHEMBL17075298 (PubChem ID:136970120) and compound CID: 71814473 with best virtual screened compounds. RESULT: MMP9 inhibitors show commendable affinity and good interaction profile of compound holding PubChem ID:102173753 over the most effective established inhibitor SB-3CT. The pharmacophore study of the best virtual screened compound reveals its high efficacy based on various interactions. The virtual screened compound's better affinity with the target MMP9 protein was deduced using toxicity and integration profile studies. CONCLUSION: Based on the ADMET profile, the compound (PubChem ID: 102173753) could be a potent drug for MCL treatment. Similar to the established SB-3CT, the compound was non-toxic with LD50 values for both the compounds lying in the same range.

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery.

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.

Structural Insights into the Molecular Design of ROS1 Inhibitor for the Treatment of Non-Small Cell Lung Cancer (NSCLC).

BACKGROUND: Non-Small Cell Lung Cancer (NSCLC) alone is the leading cause of deaths worldwide. ROS1 is a receptor tyrosine kinase (RTK), eminently recognized as the stereotyped oncogenic driver. These RTKs trigger an array of physiological regulations via cellular signal transduction pathways, which are crucial for cancer development. This attributed ROS1 as an appealing and potential target towards the targeted cancer therapy. The present research aims to propound out an effective contemporary inhibitor for targeting ROS1 with a high affinity. METHODS: Molegro Virtual Docker (MVD) provided a flexible docking platform to find out the bestestablished drug as an inhibitor for targeting ROS1. A similarity search was accomplished against the PubChem database to acquire the corresponding inhibitor compounds regarding the Entrectinib (Pub- Chem ID: 25141092). These compounds were docked to procure the high-affinity inhibitor for the target protein via virtual screening. A comparative study between the control molecule (PubChem ID: 25141092)and the virtual screened compound(PubChem ID-25175866) was performed for the relative analysis of their salient features, which involved pharmacophore mapping, ADMET profiling, and BOILED-Egg plot. RESULTS: The virtual screened compound (PubChem ID-25175866) possesses the lowest rerank score (-126.623), and the comparative ADMET analysis also shows that it is a potential and effective inhibitor for ROS1 among the selected inhibitors. CONCLUSION: The present study provided a scope for the ROS1 inhibitor as significant prevention for nonsmall cell lung cancer (NSCLC). It can be upheld for future studies as a promising support via in vivo studies.

In silico Insights on IL-6: A Potential Target for Multicentric Castleman Disease.

BACKGROUND: Multicentric Castleman Disease (MCD) is a confrontational lymphoproliferative disorder described by symptoms such as lymph node proliferation, unwarranted secretion of inflammatory cytokines, hyperactive immune system, and in severe cases, multiple organ dysfunction. Interleukin-6 (IL-6) is a pleiotropic cytokine which is involved in a large range of physiological processes in our body such as pro-inflammation, anti-inflammation, differentiation of T-cells and is reported to be a key pathological factor in MCD. In the case of MCD, it was observed that IL-6 is overproduced from T-cells and macrophages which disturb Hepcidin, a vital regulator of iron trafficking in macrophage. The present study endeavour to expound the inhibitor which binds to IL-6 protein receptor with high affinity. METHODS: MolegroVirtual Docker software was employed to find the best-established drug from the list of selected inhibitors of IL-6. This compound was subjected to virtual screening against PubChem database to get inhibitors with a very similar structure. These inhibitors were docked to obtain a compound binding with high affinity to the target protein. The established compound and the virtual screened compound were subjected to relative analysis of interactivity energy variables and ADMET profile studies. RESULTS: Among all the selected inhibitors, the virtual screened compound PubChem CID: 101119084 is seen to possess the highest affinity with the target protein. Comparative studies and ADMET analysis further implicate this compound as a better inhibitor of the IL-6 protein. CONCLUSION: Hence, this compound recognized in the study possesses high potential as an IL-6 inhibitor which might assist in the treatment of Multicentric Castleman Disease and should be examined for its efficiency by in vivo studies.

In Silico Insights on GD2 : A Potential Target for Pediatric Neuroblastoma.

BACKGROUND: Originating from the abnormal growth of neuroblasts, pediatric neuroblastoma affects the age group below 15 years. It is an aggressive heterogenous cancer with a high morbidity rate. Biological marker GD2 synthesised by the GD2 gene acts as a powerful predictor of neuroblastoma cells. GD2 gangliosides are sialic acid-containing glycosphingolipids. Differential expression during brain development governs the function of the GD2. The present study explains the interaction of the GD2 with its established inhibitors and discovers the compound having a high binding affinity against the target protein. Technically, during the development of new compounds through docking studies, the best drug among all pre-exist inhibitors was filtered. Hence in reference to the best docked compound, the study proceeded further. METHODOLOGY: The In silico approach provides a platform to determine and establish potential inhibitor against GD2 in Pediatric neuroblastoma. The 3D structure of GD2 protein was modelled by homology base fold methods using Smith-Watermans' Local alignment. A total of 18 established potent compounds were subjected to molecular docking and Etoposide (CID: 36462) manifested the highest affinity. The similarity search presented 336 compounds similar to Etoposide. RESULTS: Through virtual screening, the compound having PubChem ID 10254934 showed a better affinity towards GD2 than the established inhibitor. The comparative profiling of the two compounds based on various interactions such as H-bond interaction, aromatic interactions, electrostatic interactions and ADMET profiling and toxicity studies were performed using various computational tools. CONCLUSION: The docking separated the virtual screened drug (PubChemID: 10254934) from the established inhibitor with a better re-rank score of -136.33. The toxicity profile of the virtual screened drug was also lesser (less lethal) than the established drug. The virtual screened drug was observed to be bioavailable as it does not cross the blood-brain barrier. Conclusively, the virtual screened compound obtained in the present investigation is better than the established inhibitor and can be further augmented by In vitro analysis, pharmacodynamics and pharmacokinetic studies.

Identification of Potent VEGF Inhibitors for the Clinical Treatment of Glioblastoma, A Virtual Screening Approach.

Vascular endothelial growth factor (VEGF) expression could be found in all glioblastomas. VEGF takes part in numerous changes including the endothelial cell proliferation, the vasculature of solid tumor: its survival invasion, and migration, chemotaxis of bone marrow-derived progenitor cells, vasodilation and vascular permeability. VEGF inhibition can be a smart therapeutic strategy because it is extremely specific and less toxic than cytotoxic therapy. To establish better inhibition of VEGF than the current inhibitors, present study approach is by molecular docking, virtual screening to illustrate the inhibitor with superior affinity against VEGF to have a cautious pharma profile. To retrieve the best established and high-affinity high affinity molecule, Molegro Virtual Docker software was executed. The high-affinity scoring compounds were subjected to further similarity search to retrieve the drugs with similar properties from pubchem database. The completion of virtual screening reveals that PubChem compound SCHEMBL1250485 (PubChem CID: 66965667) has the highest affinity. The study of the drug-likeness was verified using OSIRIS Property Explorer software which supported the virtual screened result. Further ADMET study and drug comparative study strongly prove the superiority of the new established inhibitor with lesser rerank score and toxicity. Overall, the new inhibitor has higher potential to stop the expression of VEGF in glioblastoma and positively can be further analysed through In vitro studies.

FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia.

Acute Myeloid Leukaemia (AML) is a blood cancer, which affects the red blood cells in the bone marrow. Of the possible proteins that are affected in AML, fms-like tyrosine kinase 3 (FLT3) has long been recognized as a potential therapeutic target as it affects the other signaling pathways and leads to a cascade of events. First-generation inhibitors sorafenib and midostaurin, as well as secondgeneration agents such as quizartinib and crenolanib are known. It is of interest to identify new compounds against FLT3 with improved activity using molecular docking and virtual screening. Molecular docking of existing inhibitors selected a top scoring bestestablished candidate Quizartinib having PubChem CID: 24889392. Similarity searching resulted in compound XGIQBUNWFCCMASUHFFFAOYSA-NPubChemCID: 44598530 which shows higher affinity scores. A comparative study of both the compounds using a drug-drug comparison, ADMET studies, boiled egg plot and pharmacophore parameters and properties confirmed the result and predicted the ligand to be an efficient inhibitor of FLT3.

Virtual Screening of IL-6 Inhibitors for Idiopathic Arthritis.

Juvenile idiopathic arthritis (JIA) is a heterogeneous disease characterized by the arthritis of unknown origin and IL6 is a known target for JIA. 20 known inhibitors towards IL-6 were screened and Methotrexate (MTX) having PubChem ID: 126941 showed high binding capacity with the receptor IL-6. The similarity searching with this compound gave 269 virtual screened compounds. The said screening presented 269 possible drugs having structural similarity to Methotrexate. The docking studies of the screened drugs separated the compound having PubChem CID: 122677576 (re-rank value of -140.262). Toxicity and interaction profile validated this compound for having a better affinity with the target protein. Conclusively, this study shows that according to ADMET profile and BOILED-Egg plot, the compound (PubChem CID: 122677576) obtained from Virtual Screen could be the best drug in future during the prevention of juvenile idiopathic arthritis. In the current study, the drug CID: 122677576 is a potent candidate for treating JIA. The pharmacophore study revealed that the drug CID: 122677576 is a non-inhibitor of CYP450 microsomal enzymes and was found to be non-toxic, similar to the established drug Methotrexate (CID: 126941). It has a lower LD50 value of 2.6698mol/kg as compared to the established compound having LD50 value as 23.4955mol/kg. Moreover, the compound was found to be non-carcinogenic.

Design of PD-L1 inhibitors for lung cancer.

The progression of lung cancer is associated with inactivation of programmed cell death protein 1, abbreviated as PD- 1 which regulates the suppression of the body's immune system by suppressing T- cell inflammatory activity and is responsible for preventing cancer cell growth. It is of interest to identify inhibitors for PD-L1 dimeric structure through molecular docking and virtual screening. The virtual screened compound XGIQBUNWFCCMAS-UHFFFAOYSA-N (PubChem CID: 127263272) displays a high affinity with the target protein. ADMET analysis and cytotoxicity studies further add weight to this compound as a potential inhibitor of PD-L1. The established compound BMS-202 still shows the high re-rank score, but the virtual screened drug possesses a better ADMET profile with a higher intestinal absorption value and lower toxicity.

Identification of High-Affinity Small Molecule Targeting IDH2 for the Clinical Treatment of Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is symbolized by an increase in the number of myeloid cells in the bone marrow and an arrest in their maturation, frequently resulting in hematopoietic insufficiency (granulocytopenia, thrombocytopenia, or anemia) with or without leukocytosis either by a predominance of immature forms or a loss of normal hematopoiesis. IDH2 gene encodes for isocitrate dehydrogenase enzyme which is involved in the TCA cycle domino effect and converts isocitrate to alpha-ketoglutarate. In the U.S, the annual incidence of AML progressively increases with age to a peak of 12.6 per 100,000 adults of 65 years or older. Mutations in isocitrate dehydrogenase 2 (arginine 132) have been demonstrated to be recurrent gene alterations in acute myeloid leukemia (AML) by forming 2-Hydroxy alpha ketoglutarate which, instead of participating in TCA cycle, accumulates to form AML. The current study approaches by molecular docking and virtual screening to elucidate inhibitor with superior affinity against IDH2 and achieve a pharmacological profile. To obtain the best established drug Molegro Virtual Docker algorithm was executed. The compound AG-221 (Pub CID 71299339) having the high affinity score was subjected to similarity search to retrieve the drugs with similar properties. The virtual screened compound SCHEMBL16391748 (PubChem CID-117816179) shows high affinity for the protein. Comparative study and ADMET study for both the above compounds resulted in equivalent chemical properties. Virtual screened compound SCHEMBL16391748 (PubChem CID-117816179) shows the lowest re-rank score. These drugs are identified as high potential IDH2 inhibitors and can halt AML when validated through further In vitro screening.

A Computer - Aided Drug Designing for Pharmacological Inhibition of Mutant ALK for the Treatment of Non-small Cell Lung Cancer.

BACKGROUND: Lung cancer is the most common among all the types of cancer worldwide with 1.8 million people diagnosed every year, leading to 1.6 million deaths every year according to the American cancer society. The involvement of mutated Anaplasic Lymphoma Kinase (ALK) positive fusion protein in the progression of NSCLC has made a propitious target to inhibit and treat NSCLC. In the present study, the main motif is to screen the most effective inhibitor against ALK protein with the potential pharmacological profile. The ligands selected were docked with Molegro Virtual Docker (MVD) and CEP-37440 (PubChem CID- 71721648) was the best docked pre-established compound with a permissible pharmacological profile. METHODS: The selected ligands were docked with Molegro Virtual Docker (MVD). With reference to the obtained compound with the lowest re-rank score, PubChem database was virtually screened to retrieve a large set of similar compounds which were docked to find the compound with higher affinity. Further comparative studies and in silico prediction included pharmacophore studies, proximity energy parameters, ADMET and BOILED-egg plot analysis. RESULTS: CEP-37440 (PubChem CID- 71721648) was the best docked pre-established compound with preferable pharmacological profile and PubChem compound CID-123449015 came out as the most efficient virtually screened inhibitor. Interestingly, the contours of the virtual screened compound PubChem CID- 123449015 fall within our desired high volume cavity of protein having admirable property to control the ALK regulation to prevent carcinogenesis in NSCLC. BOILED-Egg plot analysis depicts that both the compounds have analogous characteristics in the divergent aspects. Moreover, in the evaluations of Blood Brain Barrier, Human Intestinal Absorption, AMES toxicity, and LD50, the virtually screened compound (PubChem CID-123449015) was found within high optimization. CONCLUSION: These investigations denote that the virtually screened compound (PubChem CID- 123449015) is more efficient to be a better prospective candidate for NSCLC treatment having good pharmacological profile than the pre-established compound CEP-37440 (PubChem CID- 71721648) with low re-rank score. The identified virtually screened compound has high potential to act as an ALK inhibitor and can show promising results in the research of non-small cell lung cancer (NSCLC).

An In silico Approach to Identify High Affinity Small Molecule Targeting m-TOR Inhibitors for the Clinical Treatment of Breast Cancer

Breast cancer is the most frequent malignancy among women. It is a heterogeneous disease with different subtypes defined by its hormone receptor. A hormone receptor is mainly concerned with the progression of the PI3K/AKT/ mTOR pathway which is often dysregulated in breast cancer. This is a major signaling pathway that controls the activities such as cell growth, cell division, and cell proliferation. The present study aims to suppress mTOR protein by its various inhibitors and to select one with the highest binding affinity to the receptor protein. Out of 40 inhibitors of mTOR against breast cancer, SF1126 was identified to have the best docking score of -8.705, using Schrodinger Suite which was further subjected for high throughput screening to obtain best similar compound using Lipinski's filters. The compound obtained after virtual screening, ID: ZINC85569445 is seen to have the highest affinity with the target protein mTOR. The same result based on the binding free energy analysis using MM-GBSA showed that the compound ZINC85569445 to have the the highest binding free energy. The next study of interaction between the ligand and receptor protein with the pharmacophore mapping showed the best conjugates, and the ZINC85569445 can be further studied for future benefits of treatment of breast cancer.