key: cord-0790969-jdgu9tkk
authors: Shah, Vraj; Bhaliya, Jaydip; Patel, Gautam M.
title: In silico docking and ADME study of deketene curcumin derivatives (DKC) as an aromatase inhibitor or antagonist to the estrogen-alpha positive receptor (Erα(+)): potent application of breast cancer
date: 2022-01-28
journal: Struct Chem
DOI: 10.1007/s11224-021-01871-2
sha: 7f60655b2a914a6c34cb2914829f95bc70fde360
doc_id: 790969
cord_uid: jdgu9tkk

Regardless of many extensive studies, hormonal-based breast cancer is the most common cause of cancer-related mortality of females worldwide. Indeed, estrogen receptor-positive (ER +) is the communal subtype in breast cancer. To treat this, three types of medications are typically used: selective estrogen receptor modulators (SERMs), selective estrogen receptor down modulators (SERDMs), and aromatase inhibitors (AIs), all of which directly interact with the activation of the estrogen signaling pathway and its formation. Despite their effectiveness, the development of new treatments is required since clinical efficacy is restricted owing to resistance. As a result, in silico studies for drug discovery are booming over the decades because of their affordability and less time-consuming features. Here, 25 deketene curcumin derivatives have been selected for docking studies through MVD software over the positive type of breast cancer through both the treatment hosts Erα + receptor and aromatase. DKC compounds are used because they have several pharmacological uses, including anti-cancer, anti-diabetic, anti-viral, anti-fungal, and anti-bacterial properties. Moreover, an ADME study was carried out for DKC derivatives that reveal the optimum drug-likeness profile. From 25 derivatives, the results showed a better MolDock score, hydrogen bonding, and steric interaction between compounds DKC-10, DKC-20, and DKC-21 with Erα + and aromatase. Although the study was done on both the treatable path hosts, better results were obtained with Erα + as an antagonist. Therefore, it is proposed that three selected DKC derivatives would be better therapeutic agents against breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11224-021-01871-2.

Breast cancer is one of the most frequent malignancies in women, with approximately globally 1.7 million new cases being diagnosed each year, with more than 0.5 million deaths [1] . In 2012, 1.67 million instances have been diagnosed, and approximately 522,000 deaths had been reported [2, 3] . In the year 2018, breast cancer led to the deaths of 627,000 women, accounting for about 15% of all cancer deaths within females. Whereas rates of breast cancer are greater among females in more advanced nations, they are rising in nearly every region around the world. Impacting 2.1 million deaths among women, this information was obtained from the World Health Organization (WHO) [4] . After this, WHO updated the new data on breast cancer cases in the year 2020, according to that 685,000 women deaths due to breast cancer and 2.3 million cases were diagnosed [4a] . Apart from this, breast cancer is on the top list as compared to other cancer types represented in Fig. 1 [4b] .

Nowadays, patients with estrogen-dependent breast cancer (EDBC) or hormonal-dependent breast cancer (HDBC) [5] are treated primarily based on a constellation of medical and histopathological criteria, as well as the assessment of three hormonal receptors (HER2 (human epidermal growth factor receptor 2), estrogen receptor (ER), and progesterone receptor (PR)). Patients with breast cancer who have ER-positive tumors typically have a better prognosis than those who have ER-negative tumors [6] . Additionally, upon reviewing the literature, we discovered that approximately 70 to 75% of breast cancers express positive estrogen receptors (ERa), and the responsible receptors are ERα-positive (ERα + ) [7] [8] [9] [10] , whereas only 24.9% of breast cancer express the negative estrogen receptor and are considered ER-negative (ER −); due to this reason, we have selected ERα-positive as a protein for molecular docking study [8] .

Additionally, EDBC-based breast cancer is dependent on estrogen. The aromatization of the A ring results in the production of estrogens, which is catalyzed by the cytochrome p450 enzyme or aromatase; this aromatase is encoded by the CYP19A1 gene [11] . Aromatase converts testosterone (T) and androstenedione (ASD) and then synthesizes estrogens such as estrone (E1) and estradiole (E2) [11] [12] [13] . The synthesized estrogens then react with Era, which is encoded by the ESR1 gene that is responsible for breast epithelial cell proliferation and tumor development in cancer patients [14] . Additionally, two pathways exist for suppressing the progression of breast cancer: the first is AIs, which inhibit estrogen production and eventually treat the cancer [15] , and the second is antagonist modulators, such as SERDs (selective estrogen down-regulators) and SERMs (selective estrogen receptor modulators), which directly inhibit genomic estrogen signaling [16] . Perhaps AIs are only effective against postmenopausal breast cancer, whereas antagonist modulators are effective against both types of breast cancer [17, 18] .

Several medications are available in the drug market for both ways, AIs and antagonists. First is exemestane (AIs) (Fig. 2a) . This medicine is used to treat a positive type of breast cancer, and this was approved by US FDA in the year 2005; this drug is the type of steroidal aromatase inhibitor [19, 20] . Moreover, drug's docking study was done by Setti et al., and that study reveals the MolDock score of −143.607 kcal/mol [21] . Apart from this, another two drugs are utilized to treat the same, which are tamoxifen (Fig. 2b ) (antagonist-SERM) under the brand name (like Nolvadex and Soltamox) [22] and letrozole (non-steroidal). Tamoxifen is a widely sold drug but there are certain detrimental drawbacks such as it produces endometric cancer, blood clot in the lung and legs, stroke, and cataracts. Similarly, letrozole (AIs) (Fig. 2c) consists of hazardous side effects of polycystic ovary syndrome (PCOS) [23, 24] . The docking analysis of these two medicines was carried out by TilakVijay et al. and Verma et al., on breast cancer, which depicted binding affinity of −149.856 and −136.784 kcal/mol, respectively [24, 25] . Another medication on the list is the anastrazole (AIs) (Fig. 2d) ; this is also a non-steroidal drug that comprises adverse effects such as hot flashes, weakness, bone, joint, and muscle pain or stiffness, sore throat or cough, and high blood pressure [26] . This drug docking investigation was done by Setti et al. and reveals the energy score of −149.521 kcal/mol. Docking data for these medications on breast cancer were obtained using MVD software [21] . On the other hand, some natural products or phytochemicals have been docked for the same disease. For [27, 28] . Additionally, these studies' minimum binding affinity values are relatively low in comparison to ours. Table 1 . The purpose of this paper is to identify DKC derivatives with anti-aromatase and anti-Era + receptor activity for use in breast cancer treatment. Indeed, curcumin, a potential anti-cancer agent, has been demonstrated to be more effective in the prevention and treatment of several cancers. Curcumin is a type of polyphenol compound derived from the South Asian plant Curcuma domestica. Curcuminoids found in C. longa include curcumin, bisdemethoxycurcumin, and demethoxycurcumin. Curcumin is widely used in Ayurveda drugs for a variety of medicinal purposes due to its antioxidant, antiseptic, analgesic, antimalarial, and anti-inflammatory properties.

Curcumin effectively suppresses human carcinomas such as malignant melanoma, cancers of the neck and head, breast, colon, pancreas, prostate, and gonads. Additionally, curcumin derivatives exhibit biological activity against the deadly virus COVID-19 [29] . The inhibitory effects of curcumin compounds on human malignancies are mediated primarily through the control of biochemical cascades, numerous transcription factors, growth factors, pro-inflammatory cytokines, super molecular kinases, and various enzymes [30] .

Curcumin's bioavailability, on the other hand, is low due to insufficient stomach absorption, restricted tissue distribution, rapid metabolism, and consequent removal from the body [31] . To address this issue, we chose derivatives of deketene curcumin for a variety of reasons, including the possibility of increasing metabolic stability by omitting the b-diketone moiety (Fig. 3) . Despite this, some researchers argue that the presence of the b-diketone moiety is necessary for curcumin's therapeutic properties. Recent research from a variety of agencies confirmed that certain curcumin analogues with a 5-carbon enone spacer but no b-diketone retained or enhanced growth-suppressive activity against various cancer cells. Certain mono-carbonyl analogues of curcumin that lack the b-diketone moiety have been shown greater anti-bacterial and anti-inflammatory activity than curcumin [32] . Compounds with the chemical formula 1, 5-diaryl-1, 4-pentadien-3-ones are the derivatives of 

Structural modification in deketene curcumin, in comparison with curcumin and active sites of the compound responsible for anti-cancer activity [31] deketene curcumin or mono-carbonyl analogues of curcumin. These are structural analogues of curcumin (1, 7-bis-(4-hydroxy-3-methoxyphenyl)-1, 6-heptadiene-3, 5-Dione), a prominent pigment found in the Indian spice turmeric Curcuma longa, Zingiberaceae. The structures of curcumin and deketene curcumin (DKC) are more or less same; however, DKC has greater biological activity than pure curcumin [33] . Certain deketene curcumin derivatives are synthesized and evaluated for their efficacy as anti-cancer agents. In comparison, the technique used to determine whether these compounds can inhibit cancer cell progression is time-consuming and expensive, such as in vivo and in vitro studies. As a result, numerous studies have demonstrated that computational approaches, such as structural bioinformatics and pharmacophore modeling, are the optimal choice due to their high accuracy and reduced time requirements [34] . Docking multiple ligands to the active protein and scoring them to determine binding affinity and interaction intensity has become a widely used technique for virtual screening of large databases as well as lead optimization [35] . On the other hand, the characteristics of a medication's absorption, distribution, metabolism, and elimination (ADME) are critical for a treatment candidate's eventual clinical success. They play a critical role in lowering the failure rate of drug candidates in early-stage clinical trials [36] . Additionally, the synthesis of curcumin derivatives is both environmentally and economically sustainable, as a variety of green routes are available. Numerous benefits can be demonstrated, ranging from high yields to secure, low-cost, and straightforward workup procedures [37] .

The present work describes the screening of various deketene curcumin derivatives for their ability to bind directly or indirectly to the ERα-positive receptor and the aromatase enzyme, both of which were obtained from the Protein Data Bank, using the Molegro Virtual Docker software. Apart from this, we conducted ADME studies on the ligands and docking score comparisons with existing drugs used to treat positive type breast cancer. While deketene curcumin derivatives demonstrate potential for both target proteins, they are more effective in terms of binding affinity on the Era + receptor. As a result, DKCs are considered to be effective agents for the treatment of breast cancer.

The crystal structures of Erα + receptor and aromatase which consist of the PDB ID: 3ERT and 3S79 respectively were directly downloaded from the workspace of software MVD v.7.0.0, where the key of the PDB ID (Protein Data Bank) needs to enter which might be accessed at the URL (http:// www. rscb. org/ pdb). These IDs have resolutions of 1.90 Å and 2.75 Å correspondingly. Additionally, the downloaded protein contains some rumpled amino acids that are repaired and rebuilt using the mutated and optimization using MVD [38, 39] . The two-dimensional (2D) structures of deketene curcumin derivatives as ligands were obtained using the Chem Bio Draw 12.0.02 computer program. Then, 2D to three-dimensional (3D) representations were converted by the use of Chem Bio 3D 12.0.02 software, and afterward, these were energetically minimized by using a method implemented in the same software and saved as SDF format (*.sdf). Table 2 displays the 2D and 3D structures of all ligands with their specific pharmacological activities. To generate accurate predictions, the imported structures must be properly prepared, which means they must have the correct atom connectivity and bond ordering. This is because when the PDB file is downloaded in its original state, it contains co-factors and water molecules that may cause an error. To address this issue, water molecules and co-factors were manually removed from the MVD after the PDBs were introduced. The absent charges, protonation states, and polar hydrogen allocation were also carried out using a special Molegro algorithm [40] .

Cavity detection is a critical operation that takes place during the docking process. Through the use of a preparation window in the MVD software, efficient binding sites for the selected aromatase and Erα + were identified during this process. This was accomplished through the use of a program called the grid-based anticipation algorithm. Moreover, for the accomplishment of this computational algorithm, the steps had been selected as the greatest numbers of cavities were 5 within 30 × 30 × 30 Å 3 cube and the volume was selected between 5 and 10,000 Å. Here, from the five selected cavities, the one with having an optimum value of the cavity has been taken for further consideration in the docking process, such as for aromatase and Erα + the volume (96.256, 364.032 Å 3 ), and surface area (204.8, 844.8 Å 2 ) respectively. Table 3 and Fig. 4a , b illustrate all detected cavities and their associated values such as volume, surface area, and coordinates.

During the molecular docking process, the energyoptimized conformers of the generated compounds 

growth inhibitory activities on both prostate and breast cancer lines

Cell growth inhibition against HCT116 [44] DKC-9 (1E,4E)-1,5-bis(4hydroxy-3methoxyphenyl)penta-1,4-dien-3-one inhibit the HIV-1 IN in enzyme assays, Antioxidant [42, 45] 

Cell growth inhibition against HCT116

[44] DKC-21 1,5-Bis-(4-hydroxy-3methoxy-5-morpholin-4-ylmethylphenyl)penta-1,4-dien-3-one against HL-60 neoplasms and HSC-2, HSC-3 and HSC-4 carcinoma cells [47] 

against HL-60 neoplasms and HSC-2, HSC-3 and HSC-4 carcinoma cells [47] DKC-25 1,5-Bis-(4-hydroxy-3methoxy-5-piperidin-1ylmethylphenyl)-penta-1,4-dien-3-one against HL-60 neoplasms and HSC-2, HSC-3 and HSC-4 carcinoma cells [47] were loaded into MVD's pre-saved workspace, with the optimized aromatase and Erα having an anticipated binding cavity. For docking with 25 deketene curcumin derivatives and currently available medications, the most plausible aromatase (cavity 1) and Erα receptor (cavity 1) binding sites were chosen. The 30 Å grid resolution was set during the docking process, and then 10 runs and the population size of 50 were chosen for operating molecular docking simulation; this can be done by the usage of MolDock Simplex Evolution search algorithm [48] . Here, the term number of runs means the number of docking simulations that were run for each ligand that was docked, with each iteration returning to a single final solution, for instance, pose. This process in the software was done by a special algorithm in which a 12-6 conceivable and sp2-sp2 torsion by Lennard Jones term was used [49] . Based on pilot docking findings, re-rank scores were determined for rating the inhibitor poses in the MolDock, and the poses chosen as the best for all of the deketene curcumin derivatives and current medicines were evaluated here. Further, default parameters were selected for maximum iteration (1500), threshold (100), binding radius (20 Å), SE maximum steps (300), and SE neighboring distance factor (1.00).

For the study of binding interactions like hydrogen bonding and steric interaction, the best conformer or pose of the ligand was selected, which has the lowest MolDock score [50] . The binding affinity or MolDock score can be determined by the utilization of differential evolution algorithm. Here, Eq. 1 describes the total biding affinity or MolDock score (E score ), and the terms used in this equation are E inter which shows the ligand and receptor energy interaction and E intra which depicts the ligand internal energy. Furthermore, to calculate the E inter and E intra , Eqs. 2 and 3 are utilized correspondingly. Despite this, piecewise linear potential (E PLP ) was used to check the steric interaction among the atoms which are charged [48, 51, 52] .

The first term in Eq. 3 describes the energy of the ligand's pair of atoms; however, this is only valid for a single bond. The torsional energy has been depicted by the second term of the equation, in that the torsional angle of the bond is denoted by Θ. The average of the torsional energy can be taken if the number of torsions would determine. The third term which is E clash is used when the distance among 2 dense atoms is lesser than 2.0 Å and allotting penalty of 1000 kcal/mol [48] . Apart from this, Molegro Virtual Docker (MVD) is suggested by various scientists because, if compared to specific accessible docking programs, MVD has great accuracy (MD: 87%, Glide: 82%, Surflex: 75%, FlexX: 58%) and has been tested to be worthwhile in several recent studies; additionally, this program is less costly and produces docking results in less time [48] . The binding of ligands by molecular docking (deketene curcumin derivatives) and protein was visualized using Molegro Molecular Viewer 7.0.0 (MMV) (AIs and Erα). It is software for studying and simulating molecular protein-ligand interactions, sequences, and structures.

The antagonistic response of an inhibitor to an enzyme or a protein receptor does not guarantee its suitability as a potential drug [53] . As a result, ADME (absorption, distribution, metabolism, and excretion) analysis and drug-likeness analysis have been critical in drug discovery because they aid in making the correct decision about whether or not to evaluate inhibitors against a biological system [53, 54] . Additionally, the majority of failed medications in clinical trials were produced by inhibitors with insufficient ADME properties, causing severe damage to biological systems as a result of the excessive toxicity. Additionally, the ADME study was conducted via the Swiss ADME web server (Swiss Institute of Bioinformatics, Switzerland). The selected 25 deketene curcumin derivatives are then imported into the program via SMILES (Simplified Molecular Input Line Entry System) in order to generate a pharmacokinetic profile or drug-likeness specification [55].

Apart from that, a specific rule has been established to determine whether or not an inhibitor with specific medical and pharmacological characteristics is a safe and orally active medication in the human body. Christopher A. Lipinski developed a thumb rule for determining drug-likeness in 1997. Additionally, the rule of five (5) may be referred to as Pfizer's rule of five (5) or Lipinski's rule of five (5) [54] . As per this rule, if two (2) or more of these thresholds are met, a drug could be orally absorbed into the human body. These cut-offs are as follows: the MW (molecular weight) of the molecule should be less than 500 g/mol, iLOGP (octanol/water partition coefficient) should be ≤ 5, then the nHBD (H-bond donor) and nHBA (H-bond acceptor) should be in the range of ≤ 10 and ≤ 5, respectively, and further, the TPSA (topology polar surface area) should be 40 Å 2 [56, 57] . Table 4 illustrates the drug-like data of deketene curcumin and its derivatives. If these cut-off values correspond to the respective drug, there will be no violation of the Lipinski rule.

In present work, 25 DKC compounds were successfully docked to the human placental aromatase cytochrome p450 and Erα + receptor in this study (PDB ID: 3S79, 3ERT, respectively). Additionally, a comparative docking study was conducted using currently used drugs (tamoxifen, anastrazole, exemestane, and letrozole) to treat breast cancer. As previously stated and as determined through the literature, tamoxifen is an antagonist type of drug (study conducted on Erα +), whereas the other three are AI type of drugs (study conducted on aromatase); thus, different PDBs were used for the comparative study. Moreover, the best poses were selected to calculate the MolDock and re-rank scores while performing the docking study. Here, Table 5 represents the MolDock score, H-bonding, re-rank score, and steric score between protein of breast cancer and ligand (DKC derivatives and 4 drugs). In addition to this, through literature it was confirmed that ligands (DKC-10, DKC-20, and DKC-21 for Erα +) which are exhibited binding affinity less than −150 kcal/mol would be regarded as a more effective inhibitor [40] [41] [42] [43] [44] [45] [46] [47] [48] . In this study, 3 deketene curcumin derivatives (DKC-10, DKC-20, and DKC-21) out of 25 Tables 8 and 9 and Figs. 9 and 10. Additionally, these tables and figures include interactions for the reference drugs.

To begin with, a depth description of each of these ligand's hydrogen and steric interaction with Erα + and aromatase has been discussed. (Fig. 8b) .

On the other hand, the ligand DKC-20 forms a hydrogen bond with Erα + via interactions between residues Leu 346 and Glu 353, which interact with the hydroxyl group's atom number O (21) , and then Thr 347 interacts with the carbonyl group's atom number O(6) (Fig. 7c) After the comprehensive discussion of the interactions of DKC derivatives, it was observed that DKC-10 ligand exhibits the highest MolDock score (− 204.461 kcal/mol with Erα + and − 201.613 kcal/mol with aromatase) against the breast cancer. This is due to the strong H-bond and steric interactions, as well as the shorter interacting distance. Additionally, other supporting data for hydrogen bonding and steric interaction, such as interacting distance, interaction energy, strength, and the H bonding donor atom between the ligand and protein, are well represented in Tables 6, 7, 8, and 9. In this study, we also did a comparison analysis (Tables 10 and 11 ), in which we evaluated the drugs that are presently used to treat breast cancer. First, the interactions of tamoxifen (antagonist type drug) has been evaluated on Erα+ (Fig. 7e) . The results received through these interactions are very poor in terms of MolDock score, H-bonding, and steric interaction in comparison with DKC derivatives. On the other hand, exemestane, anastrazole, and, letrozole have been taken for study against p450 enzyme ( Fig. 8e-g) , since these medications block aromatase, but, here also inadequate MolDock score, hydrogen bonding, and steric interactions received. Therefore, it is possible to argue that DKC derivatives, might be superior therapeutic agents for treating breast cancer. Furthermore, in the ADME/pharmacokinetics predictions profile, the foremost step of oral bioavailability of the drug can be determined by the drug aqueous solubility as well as intestinal permeability [56] . The results revealed that DKC-1 and its 3 derivatives have high gastrointestinal absorption, and then DKC-10 and DKC-21 possess P-glycoprotein, whereas DKC-1 and DKC-21 possess barrier to P-glycoprotein. Further, DKC-10, DKC-20, and DKC-21 exhibit the barrier towards the blood-brain barrier (BBB) except DKC-1 (Table 11) . Despite this, one Table 11 Parameters of ADME and drug-likeness of DKC-1 and its 3 derivatives common violation has been observed in DKC-10, DKC-20, and DKC-21 due to molecular weight > 500 g/mol as per the Lipinski rule. Moreover, through SWISS ADME web server, boiled egg graph was also generated which illustrates absorption of the ligands in the gastrointestinal tract and brain. This type of graph is also known as the brain or intestinal estimated permeation predictive model or Egan egg graph [58] . The boiled egg graph of the four DKC ligands has been shown in Fig. 11 . Apart from this, Fig. 12 shows the bioavailability radar of the DKC-1 and its 3 derivatives based on six physicochemical properties such as lipophilicity, saturation, size, polarity, solubility, and flexibility [58] . Through radar images, it can be observed that DKC-1 and its 3 derivatives are expected to be orally bioavailable (less toxic and good absorption), less polarity, and low flexibility. 

Overall, it can be concluded that breast cancer in women is a serious global concern at the moment, as it ranks first on the list of cancers when compared to other malignancies. As a result, extensive research is being conducted to determine the most effective inhibitors for the treatment of breast cancer, particularly positive breast cancer. In this work, we discovered DKC derivatives' potential against breast cancer in both inhibitory pathways, such as an antagonist to Erα+ and an inhibitor of the p450 cytochrome enzyme. The molecular docking study of 25 deketene curcumin derivatives have been conducted in this work against two proteins Erα + (PDB: 3ERT) and aromatase (PDB: 3S79), also carried out the comparative study with drugs that are already used to treat the breast cancer. From this study, three DKC derivatives DKC-10, DKC-20, and DKC-21 are discovered with the lowest binding affinity by interaction with Erα + and aromatase (−204.461 kcal/mol, −177.279 kcal/ mol, −161.958 kcal/mol, and −201.613, −131.397, −12 3.724, respectively). However, other parameters of these derivatives such as H-bonding and steric interactions are more favorable for the inhibition of Erα + (positive type of breast cancer host) in comparison with aromatase. In addition to this, docking study of existing drugs with breast cancer reveals poor outcomes in terms of MolDock score, H-bonding, and steric interactions in contrast to DKC derivatives. Besides this, satisfactory results were received in the ADME/pharmacokinetics study of DKC derivatives. Therefore, it can be said that DKC-10, DKC-20, and DKC-21 are the leading candidate to treat a positive type of breast cancer. Apart from this, in the future, these candidates should be undergone the process of pre-clinical trials such as in vivo and in vitro studies against breast cancer. Acknowledgements The authors would like to thank the Swiss Institute of Bioinformatics for the SWISS ADME web server.

Author contribution All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Vraj Shah, Jaydip Bhaliya, and Gautam M. Patel. The first draft of the manuscript was written by Vraj Shah, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. 

is a tumor suppressor that inhibits estrogen receptor expression in ER-positive breast cancer

The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review

Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN

World Health Organisation (WHO), Breast Cancer

Molecular docking and 3D-QSAR-based virtual screening of flavonoids as potential aromatase inhibitors against estrogen-dependent breast cancer

Lysine-specific demethylase 1 (LSD1) is highly expressed in ER-negative breast cancers and a biomarker predicting aggressive biology

Targeting the PI3K/AKT/mTOR pathway in estrogen receptor-positive breast cancer

Estrogen receptor breast cancer phenotypes in the Surveillance, Epidemiology, and End Results database

A 2009 update on the treatment of patients with hormone receptor-positive breast cancer

New therapeutic approaches in breast cancer

Functions and physiological roles of two types of estrogen receptors, ERα and ERβ, identified by estrogen receptor knockout mouse

Molecular characterization of aromatase

Structural and functional characterization of aromatase, estrogen receptor, and their genes in endocrine-responsive and-resistant breast cancer cells

Discovery of a multi-target compound for estrogen receptor-positive (ER+) breast cancer: involvement of aromatase and ERs

Aromatase inhibitors: mechanism of action and role in the treatment of breast cancer

Current landscape of targeted therapies for hormone-receptor positive, HER2 negative metastatic breast cancer

American Cancer Society perspectives on environmental factors and cancer

The development, application and limitations of breast cancer cell lines to study tamoxifen and aromatase inhibitor resistance

Aromatase inhibitors: past, present and future in breast cancer therapy

Anastrozole ('Arimidex') versus tamoxifen as first-line therapy in postmenopausal women with advanced breast cancer: results of the doubleblind cross-over SAKK trial 21/95-a sub-study of the TARGET (Tamoxifen or 'Arimidex'Randomized Group Efficacy and Tolerability) trial

Impact of Aromatase protein variants and drug interactions in breast cancer: a molecular docking approach

Side effect of letrozole (2021) https:// breas tcanc ernow. org/ infor mation-suppo rt/ facing-breast-cancer/ goingthrou gh-breast-cancer-treat ment/ side-effec ts-letro zole-femara

De novo designing, assessment of target affinity and binding interactions against aromatase: discovery of novel leads as anti-breast cancer agents

Virtual screening of novel compounds as potential ER-alpha inhibitors

Side effect of anastrazole (2021) https:// breas tcanc ernow. org/ infor mation-suppo rt/ facing-breast-cancer/ going-throu gh-breast-cancer-treat ment/ side-effec ts-anast rozolearimi dex

Design and pharmacophoric identification of flavonoid scaffold-based aromatase inhibitors

In silico computational prediction of anti-breast cancer effect of abruquinones from

Identification of potent COVID-19 main protease (Mpro) inhibitors from curcumin analogues by molecular docking analysis

Identification of curcumin derivatives as human LMTK3 inhibitors for breast cancer: a docking, dynamics, and MM/PBSA approach

New structural analogues of curcumin exhibit potent growth suppressive activity in human colorectal carcinoma cells

Exploration and synthesis of curcumin analogues with improved structural stability both in vitro and in vivo as cytotoxic agents

New antitumoral agents I: in vitro anticancer activity and in vivo acute toxicity of synthetic 1, 5-bis (4-hydroxy-3-methoxyphenyl)-1, 4-pentadien-3-one and derivatives

Structural bioinformatics and its impact to biomedical science

Virtual screening and fast automated docking methods

QSAR modeling, docking, ADME and reactivity of indazole derivatives as antagonizes of estrogen receptor alpha (ER-α) positive in breast cancer

Green synthesis and biological activity of silver-curcumin nanoconjugates

The structural basis of estrogen receptor/ coactivator recognition and the antagonism of this interaction by tamoxifen

Novel aromatase inhibitors by structure-guided design

In silico approach: docking study of oxindole derivatives against the main protease of COVID-19 and its comparison with existing therapeutic agents

Cooking enhances curcumin anti-cancerogenic activity through pyrolytic formation of deketene curcumin

5-Diphenyl-1, 4-pentadiene-3-ones and cyclic analogues as antioxidative agents. Synthesis and structureactivity relationship

Structure-activity relationship studies of curcumin analogues

Structure-activity relationships of the antitumor C5-curcuminoid GO-Y030

Geometrically and conformationally restrained cinnamoyl compounds as inhibitors of HIV-1 integrase: synthesis, biological evaluation, and molecular modeling

Method for the preparation of 1, 5-bis (4-hydroxy-3-metoxy-phenyl)-penta-1, 4-dien-3-one and derivatives with antitumoral properties

Synthesis and biological evaluation of 1, 5-bis (4-hydroxy-3-methoxyphenyl) penta-1, 4-dien-3-one and its aminomethyl derivatives

MolDock: a new technique for high-accuracy molecular docking

Docking studies of chlorogenic acid against aldose redutcase by using molgro virtual docker software

Structure based comprehensive modelling, spatial fingerprints mapping and ADME screening of curcumin analogues as novel ALR2 inhibitors

Fully automated and rapid flexible docking of inhibitors covalently bound to serine proteases

GEMDOCK: a generic evolutionary method for molecular docking

Design of potential anti-melanoma agents against SK-MEL-5 cell line using QSAR modeling and molecular docking methods

A molecular docking approach to evaluate the pharmacological properties of natural and synthetic treatment candidates for use against hypertension

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

In-silico molecular docking and ADME/pharmacokinetic prediction studies of some novel carboxamide derivatives as anti-tubercular agents

A boiled-egg to predict gastrointestinal absorption and brain penetration of small molecules

Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations

The authors declare no competing interests.