Cancer significantly impacts life expectancy and ranks among the foremost causes of death globally [1]. It is a disease characterised by the uncontrolled growth of certain cells within the body, which may then spread to other regions or become initiated in any of the trillions of cells that constitute the human body [2], 3]. The cancerous cells at the initial stage may be eradicated by the immune system, and those that survive may evolve into clinically defined tumours that cause harm [3], 4]. Cancers are of different types, including lung, stomach, oesophagal, and breast cancer. These occur throughout the body without our awareness [5].
Breast cancer has been identified as the most prevalent cancer type globally [6]. Pinto et al. [7] reported that breast cancer has the highest incidence among cancers, with 2.29 million new cases, accounting for 11.5 % of all cancer diagnoses around the world. Breast cancer is the most commonly diagnosed cancer and the second leading cause of death among women [6], [7], [8]. In 2020, breast cancer comprised 30 % of female cancers, with an estimated 276,480 new cases and over 42,000 estimated deaths in 2020 [6], 9]. Ferlay et al. [10] also assert that breast cancer stands out as the most common cancer on a global scale. Breast cancer, as a type of cancer, frequently affects women in their forties and fifties, with a mortality rate of 20 % and a morbidity rate of 30 % [6]. In 2024, 313,500 new cases were reported in the United States, while concerted efforts and resources have been invested to reduce mortality from breast cancer among women under 70 years by 25 % in 2040 [6].
Breast cancer develops as a result of the abnormal growth of cells, which occurs owing to the interplay of several factors, including hormonal, environmental, and genetic factors, amongst others [11]. Breast cancer has been classified into different subtypes based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-related protein (HER2) [11], [12], [13]. Hormone-positive breast cancers account for more than 70 % of breast cancer cases, and estrogen receptor-positive is the most common subtype. The increased production of estrogen and overactivation of estrogen receptors contribute to the extreme risk of breast cancer, thereby making the estrogen receptor alpha a major and promising target for the breast cancer pathogenesis [14]. Therefore, the inhibition of the enzyme activity and targeting estrogen receptor alpha are viable approaches in mitigating breast cancer. Despite the concerted efforts and resources invested in the development of inhibitors for breast cancer, most of the therapeutic agents developed have adverse effects. In contrast, others have shown reduced potency in eliminating cancerous cells [15]. Hence, medicinal plants need to be investigated to identify new drug candidates to prevent and treat the disease effectively.
Medicinal plants are widely known as a rich source of novel pharmaceuticals with excellent therapeutic properties against existing and emerging diseases [16]. Plants belonging to the Bauhinia genus are classed in the family Fabaceae [17]. These plants possess different classes of phytochemicals, including flavonoids. Bauhinia flavonoids are subdivided into flavonol, flavanol, flavononol, flavanone, flavone, isoflavones, biflavonoids, and flavonoid glycosides [18]. Pharmacologically, the Bauhinia flavonoids possess excellent antidiabetic, anticancer, cytotoxic, antioxidant, anti-inflammatory, antibacterial, and anticataract activities [19], [20], [21], [22], [23], [24], [25], [26].
Computer-aided drug design (CADD) encompasses various computational methods to discover, design, and develop new therapeutic agents for treating diseases [27]. CADD plays a vital role in enhancing active ligands, identifying novel drugs, and comprehending biological processes at the molecular level [28]. Molecular docking and molecular dynamics simulation constitute a structure-based computational technique that elucidates the binding modes, interactions, affinities, and stability between ligands and their respective targets [29], 30]. Drug likeness and pharmacokinetic studies help to identify new drug candidates against targeted diseases [31]. While concerted efforts are ongoing in the search for potent drug candidates for breast cancer through estrogen receptor alpha inhibition, no study has investigated the inhibitory potential of Bauhinia flavonoids as therapeutic agents against the target. Thus, our study aimed to identify new cytotoxic agents among Bauhinia flavonoids using advanced computational drug discovery approaches for treating human breast cancer.
The three-dimensional structure of the estrogen receptor alpha with PDB ID: 6CHZ was retrieved from the protein data bank (https://www.rcsb.org/structure/6CHZ). The protein was uploaded into the PyMol software, and the cofactor, ions, native ligand, and water molecules were removed. The bare protein was saved in PDB format and loaded into the MGL Tool 1.5.2 interface, where polar hydrogen was merged and charges were computed. The resulting protein file was saved in PDBQT format for docking processes.
Further, the library of 164 naturally occurring flavonoids identified in plant species in the Bauhinia genus was constructed. The chemical structure of the flavonoids and reference compound (5-fluorouracil) were built with Spartan 14 software (see Supplementary Material, Table S1). The SDF format of the chemical structures was uploaded in the Open Babel interface of PyRx 0.8 software, and their energy was minimized under the Merck Molecular Force Field (MMFF94) at the steepest descent of 0.1. The minimized output of the ligand was saved in PDBQT format for docking processes.
The docking procedure was first validated to perform molecular docking studies of the naturally occurring flavonoids from the Bauhinia genus. In validating the docking procedure, the native ligand was re-docked into the binding pocket of the estrogen receptor alpha enzyme by considering residues with 5 Å (Met343, Leu346, Thr347, Leu349, Ala350, Asp351, Glu353, Trp383, Leu384, Leu387, Met388, Leu391, Arg394, Phe404, Met421, Ile424, Leu428, Gly521, Met522, His524, Leu525, Val533 and Leu539) and the RMSD value was estimated.
After validating the docking procedure, molecular docking studies were performed. To dock the phytochemicals against the target receptor, the PDBQT files of the protein and ligands were loaded into the PyRx 0.8 vina interface. Then, the residues with residence at a bond distance of 5 Å were used to set the active site of the enzyme. Later on, the grid box centre and size of the protein were set at center_x = −29.7601, center_y = −0.6423, center_z = −21.8636 and size_x = 25.5126, size_y = 19.1684, size_z = 27.6765. Then, the ligands were docked on the Autodock tool [32] of the PyRx 0.8 software at an exhaustiveness of 50. On completion of the molecular docking processes, the binding affinity of each inhibitor was collated. Chemical compounds with high binding affinity relative to the reference drug were selected for post-docking analysis, such as hydrogen bonds, hydrophobic and pi-interactions, using Discovery Studio Visualizer software 2020.
Molecular dynamics simulations, each lasting 100 ns, were performed using Schrödinger Maestro 2018-1 to assess the binding stability of the selected docked poses on estrogen receptor alpha. The docked complexes were solvated in TIP3P water molecules using an orthorhombic water box through Desmond’s system builder tool. Counter ions were added to neutralise the systems, which were then minimised using the steepest descent and LBFGS algorithms, with a maximum of 2000 iterations and a convergence threshold of 1 kcal/mol/Å. Following minimisation, molecular dynamics production runs were performed under periodic boundary conditions using the NPT ensemble, maintaining constant parameters for the number of atoms (N), pressure (P), and temperature (T). At 300 K and 1.013 bar atmospheric pressure, the simulations were performed with a cutoff distance of 9 Å for non-bonded interactions and a Particle Mesh Ewald (PME) method for electrostatic interactions. Energy recording intervals were set at 1.2 ps and trajectory sampling intervals at 50 ps. After the 100 ns production runs were finished, the trajectories were examined to create simulation interaction diagrams that shed light on the complexes’ binding stability.
The phytochemicals with high binding affinity were assessed for their drug-likeness property by strict consideration of the PAINS (Pan-Assay Interference Compounds) filter test and Lipinski’s rule of 5 (molecular weight ≤500, hydrogen bond acceptor ≤10, hydrogen bond donor ≤5, logP ≤5, and molar refractivity 40–130) [33]. Also, pharmacokinetic properties like solubility, gastrointestinal absorption, bioavailability, AMES toxicity, and hepatotoxicity attributes of the compound were assessed using the pkCSM (https://biosig.lab.uq.edu.au/pkcsm/) web server.
The quantum chemical descriptors of the potential estrogen receptor alpha were estimated using its optimized structure. The DFT with B3LYP 6–31 + G (d, p) basis set was employed in the Spartan 14 software [34]. The energy of the highest occupied molecular orbital (E
HOMO), energy of the lowest unoccupied molecular orbital (E
LUMO), energy gap, ionization potential, chemical potential, electrophilicity index, and chemical hardness were computed using the equation below.
The molecular docking method has evolved to become the most efficient route to identifying new drug candidates against various terminal ailments. In this study, the naturally occurring flavonoids from the Bauhinia genus were docked against estrogen receptor Alpha. Before docking, the docking protocol was validated by redocking the co-crystallized ligand into the generated grid, and the RMSD value between the native and re-docked ligand was found to be 1.99 Å, which indicates that the developed docking procedure is reproducible. Also, the overlaying image of the redocked and native ligand is presented in Figure 1.
Superimposed image of native and re-docked ligand.
Five Fluorouracil was used as a reference compound in the current study. The binding affinity of 5-Fluorouracil (−5.3 kcal/mol) was set as the cut-off point. The drug-like properties of the identified inhibitors with high binding affinity compared with the 5 5-fluorouracil were selected for further analysis (Table 1).
Interaction analysis of flavonoids with high binding affinity against estrogen receptor alpha.
| Ligand | Binding energy (kcal.mol−1) | Hydrogen bond | Hydrophobic interaction | π-π interaction | |
|---|---|---|---|---|---|
| Amino acid | Distance (Å) | ||||
| BG1 | −10.5 | Phe404 | 2.38 | Met343, Ala350, Leu387, Leu391, Phe404, Met421, Ile424, Leu525 | Met343, Ala350, Leu387, Leu391, Phe404, Met421, Ile424, Leu525 |
| BG2 | −10.2 | – | – | Leu346, Ala350, Trp383, Ile424, His524, Leu525 | Leu346, Ala350, Trp383, Ile424, His524, Leu525 |
| BG3 | −9.8 | – | – | Met343, Thr347, Ala350, Leu384, Met421, Ile424, Leu525, Tyr526, Cys530, Pro535 | Met343, Thr347, Ala350, Leu384, Met421, Ile424, Leu525, Tyr526, Cys530, Pro535 |
| BG4 | −9.1 | – | – | Leu346, Leu384, Phe404, Ile424 | Leu346, Leu384, Phe404, Ile424 |
From molecular docking screening we identified four hit molecules (7-hydroxy-2-((7-methoxy-2-(4-methoxyphenyl)-4-oxo-4H-chromen-3-yl)methyl)-6-methyl-4H-chromen-4-one (BG1), 7-hydroxy-2-[(7-methoxy-4-oxo-4H-chromen-3-yl-2-(4-methoxyphenyl) methyl]-6-methyl-4H-chromen-4-one (BG2), 6-methyl-2-phenyl-4H-chromen-4-one (BG3) and 6,8-C-dimethyl kaempferol-3-methyl ether (BG4)) that possess higher docking score compared with the reference drug. These identified compounds belong to the isoflavone and flavonol groups. The 2d structure of these identified compounds is shown in Figure 2.
Chemical structures of the hit molecules.
BG1 obtained from Bauhinia purpurea emerged as the best binder against Estrogen Receptor Alpha enzyme with a binding affinity of −10.5 kcal/mol. The hydrogen atom on the aromatic ring of the naturally occurring flavonoid formed a hydrogen bond interaction with Phe404 at 2.38 Å. Its stability was strengthened at the binding pocket of estrogen receptor alpha by forming both hydrophobic and pi interactions with Met343, Ala350, Leu387, Leu391, Phe404, Met421, Ile424, and Leu525 (Figure 3a).
Interaction diagram of (a) 6CHZ-BG1, (b) 6CHZ-BG2, (c) 6CHZ-BG3, (d) 6CHZ-BG4.
BG2 isolated from B. purpurea was selected as the second-best estrogen receptor alpha enzyme inhibitor as it elicited a binding affinity of −10.2 kcal/mol. The hydrophobic and pi-interactions established between the isoflavone and Leu346, Ala350, Trp383, Ile424, His524, and Leu525 accounted for the stability and good binding affinity observed at the binding pocket of the enzyme (Figure 3b). Conversely, no hydrogen bond interaction was demonstrated against the amino acid residues of the enzyme.
BG3 purified from Bauhinia variegata was selected as the third-best binder against estrogen receptor alpha, with a binding affinity of −9.8 kcal/mol. The naturally occurring flavonol moiety established good hydrophobic and pi-interactions with Leu346, Leu384, Leu387, Leu391, Phe404, His524, and Leu525 (Figure 3c). Also, the ligand showed no hydrogen bond interaction with the naturally occurring flavonoid.
BG4 isolated from Bauhinia malabarica emerged as the fourth-best inhibitor with a considerably high binding affinity of −9.1 kcal/mol. The flavonol participated in hydrophobic and pi-interactions with Leu346, Leu384, Phe404, and Ile424. However, hydrogen bond interaction was not observed between the ligand and estrogen receptor alpha enzyme (Figure 3d).
Molecular dynamics simulation is an advanced computer-aided drug discovery method whereby the behaviour, stability, and interaction of hit molecules are studied in a simulated living medium. In this study, the stability and interactions of the selected hit flavonoids were established by subjecting each protein-ligand complex to a 100 ns simulation period. The root mean square deviation (RMSD) plot showed that BG1 was stable from 0to 100 ns (Figure 4a). The root mean square fluctuation (RMSF) plot showed a slight fluctuation ranging from 0.5 to 3.0 Å (Figure 4b). Also, the interaction analysis showed that Ala350 formed a hydrogen bond interaction with BG1, and hydrophobic interactions were observed with Met341, Leu349, Trp383, Leu387, Met388, His524, and Leu536. Furthermore, a water bridge was observed between Leu346, Met517, Cys530, Lys531, and the ligand (Figure 4c).
Image for (a) RMSD plot of 6CHZ-BG1, (b) RMSF plot of 6CHZ-BG1, (c) contact plot of 6CHZ-BG1.
The RMSD plot for the 100 ns MD simulation of 6CHZ-BG2 showed that the ligand was primarily stable with slight fluctuation throughout the simulation time period (Figure 5a). The RMSF plot showed slight fluctuation ranging between 0.8 and 6.2 Å (Figure 5b). Furthermore, the contact plot showed that the compound established hydrogen bond interactions with Trp383 and His524, hydrophobic interactions with Leu349, Leu387, Met388, Leu391, and water bridge with Thr347, Cys530, and Asn532 (Figure 5c).
Image for (a) RMSD plot of 6CHZ-BG2, (b) RMSF plot of 6CHZ-BG2, (c) contact plot of 6CHZ-BG2.
6CHZ-BG3 was simulated for 100 ns to understand its stability at the enzyme’s binding pocket. Considering the RMSD plot of the protein-ligand complex, it can be inferred that a fairly stable system was obtained (Figure 6a). The RMSF plot ranged between 0.6 and 2.5 Å (Figure 6b). The flavonoid moiety formed a hydrogen bond interaction with Glu353 and Arg394, while a water bridge was observed between the phytochemical and Thr347, Glu353, and Leu387 (Figure 6c).
Image for (a) RMSD plot of 6CHZ-BG3, (b) RMSF plot of 6CHZ-BG3, (c) contact plot of 6CHZ-BG3.
The RMSD plot of the 6CHZ-BG4 complex subjected to a 100 ns MD simulation showed that the ligand demonstrated a steady increase from 0 to 12 ns, followed by a stable simulation system from 13 to 100 ns (Figure 7a). The protein stability was further examined and established with an RMSF plot ranging between 0.6 and 2.5 Å (Figure 7b). Also, the interaction analysis showed that the ligand participated in hydrogen bond interactions with Thr347, Trp383, Val534, and Lys529, hydrophobic interactions with Leu539, and water bridges with Leu525, Met528, Lys529, Cys530, and Val534 (Figure 7c).
Image for (a) RMSD plot of 6CHZ-BG4, (b) RMSF plot of 6CHZ-BG4, (c) contact plot of 6CHZ-BG4.
The calculated quantum chemical parameters of the hit flavonoids are presented in Table 2.
Calculated global reactivity descriptor values for the hit molecules.
| Flavonoids | E HOMO | E LUMO | I | µ | η | ω |
|---|---|---|---|---|---|---|
| BG1 | −6.03 | −1.62 | 6.03 | −3.83 | 2.21 | 3.32 |
| BG2 | −5.95 | −1.57 | 5.95 | −3.76 | 2.19 | 3.23 |
| BG3 | −5.49 | −1.76 | 5.49 | −3.63 | 1.87 | 3.52 |
| BG4 | −5.52 | −1.60 | 5.52 | −3.56 | 1.96 | 2.23 |
E HOMO, energy of highest occupied molecular orbital; E LUMO, energy of lowest unoccupied molecular orbital; I, ionization energy; µ, chemical potential; η, hardness; ω, electrophilicity index.
The HOMO and LUMO properties of chemical compounds are essential to predicting their electron acceptability and electron donating potential. In this study, BG3 gave the highest HOMO, while BG2 gave the highest LUMO value. The energy gap value of the molecules showed that BG1 was the lowest, while BG3 was the highest (Figure 8). The chemical compounds showed considerably similar values to those of other global reactivity descriptors calculated. However, BG1 gave the highest ionization potential (6.03 eV), BG3 (1.76 eV) gave the highest electron affinity, BG4 (−3.83 eV) gave the highest chemical potential value, BG1 (2.21 eV) demonstrated the highest chemical hardness value, and BG3 (3.52 eV) gave the highest electrophilicity index.
Pictorial representation of HOMO and LUMO as well as energy gap value of BG1, BG2, BG3 and BG4.
Finally, the Drug likeness and pharmacokinetic studies of the selected flavonoids were also performed, and the results obtained are presented in Table 3.
Drug likeness and pharmacokinetic properties of the hit molecules.
| Ligand | Lipinski’s rule violation | PAINS | Solubility | GIT | Bioavailability | AMES toxicity | Hepatotoxicity |
|---|---|---|---|---|---|---|---|
| BG1 | No | Yes | −3.997 | High | 0.55 | No | No |
| BG2 | No | Yes | −5.500 | High | 0.55 | No | No |
| BG3 | No | Yes | −4.371 | High | 0.55 | No | No |
| BG4 | No | Yes | −3.273 | High | 0.55 | No | No |
GI, gastrointestinal tract.
The results showed that all the selected hit molecules obeyed Lipinski’s rule and also passed the PAINS filter test. The pharmacokinetic studies also showed that the selected inhibitors of estrogen receptor alpha are highly soluble and could be readily absorbed in the gastrointestinal tract due to their bioavailability index. The toxicity profile showed that the compounds are non-toxic to the liver and also elicited no AMES toxicity.
Since ancient times, medicinal plants have been identified as a rich source of therapeutic agents with excellent pharmacological properties [35]. These plants are known to produce secondary metabolites, active ingredients responsible for the potency of extracts and fractions against various diseases [22]. Though their pharmacological activities vary depending on the abundance of phytochemicals or region of collection, most medicinal plants contain a specific class of phytochemicals, which are sometimes unique to them and demonstrate unique therapeutic effects when administered to disease subjects [36].
Flavonoids are among the important secondary metabolites that have shown excellent therapeutic values. These abundant phytochemicals are widely identified in the genus and species of various medicinal plants. They are chemical compounds with excellent antioxidant potentials such that they mop up free radicals and also alter the pathway by which free radicals generate diseases thereby blocking the occurrence of most diseases associated with free radicals [37], [38], [39]. The Bauhinia genus is among the family Fabaceae, which are widely known as a rich source of different classes of flavonoids [40]. The flavonoids from the Bauhinia genus are among the phytochemicals that contribute to the unique pharmacological efficacy of its species [40]. Various classes of flavonoids from the genus have been reported to demonstrate excellent pharmacological activities [17]. The present study focused on predicting the anticancer efficacy of naturally occurring flavonoids identified in the Bauhinia genus using molecular docking, molecular dynamics simulation, and ADMET strategies.
Estrogen is an important steroid hormone usually produced in humans by the liver, adrenal glands, pancreas, placenta, breast tissues, and ovaries [41]. Estrogen receptor comprises mainly estrogen receptor alpha and estrogen receptor beta, with the high expression of the former directly implicated in breast cancer [42]. Despite the extensive usage of chemical compounds in breast cancer therapy, it has been discovered that many breast cancer patients develop natural resistance to treatments like hormonal therapy [43], 44]. Also, other effective medications are associated with episodes of side effects, and others are not effective in eliminating the malignant tumour [41], 45].
Several studies have been performed by using computational methods to investigate breast cancer inhibitors from natural sources, with various successes achieved. Effiong et al. [46] identified o-tolylamino-acetic acid (4-nitro-benzylidene)-hydrazide obtained from Pleurotus ostreatus extract as its most potent inhibitor of estrogen receptor alpha with a low binding affinity. Furthermore, some selected flavonoids were screened against estrogen receptor alpha but elicited low binding affinity with good drug likeness properties, while some chlorogenic acid derivatives gave good binding affinities against estrogen receptor alpha [47], 48]. With the promising anticancer features of flavonoids, it is important to investigate more flavonoids for their therapeutic effect against breast cancer.
Computer-aided drug design is among the most effective drug discovery and design approaches that reduce cost, stress, and time associated with drug lead identification [49], 50]. Molecular docking is a computational method used to rank the inhibitory effect of phytochemicals through their binding affinity against the selected target receptor [50]. In this study, the naturally occurring flavonoids from the Bauhinia genus were screened for their potential estrogen receptor alpha inhibitory potential using a virtual screening approach. As a result of screening after comparing with standard four hit molecules were selected for further analysis. BG1 elicited the highest binding affinity, followed by BG1, BG2, BG3 and BG4, followed by BG1, BG2, BG3 and BG4, with binding affinities of −10.5, −10.2, −9.8, and −9.1 kcal/mol, respectively. All the hit flavonoids demonstrated better inhibitory potential when compared to α-pinene (−5.9 kcal/mol), D-limonene (−6.0 kcal/mol) and isocyclomorusin (−8.4 kcal/mol) [41], 51]. Also, these potential breast cancer therapeutic agents demonstrated considerably higher binding affinity against estrogen receptor alpha than cannabinoids and curcumin derivatives [52], 53]. Furthermore, important interactions like hydrogen bonds, hydrophobic, and pi are relevant to the increased binding affinity, stability, and effectiveness of phytochemicals at the binding pocket of enzymes implicated in disease pathophysiology. Additionally, fewer hydroxyl and methoxy groups in BG1 and BG3 contributed to their increased binding force at the receptor’s active site, compared to BG2 and BG4.
Molecular dynamics simulation is an efficient method of investigating the stability of ligands in the binding pocket of enzymes [54]. In molecular dynamics simulation, RMSD helps to understand the stability of protein-ligand complexes, while the RMSF gives thorough insight into the stability of proteins [55]. Hydrophobic and hydrogen bond interactions contribute to the ligand’s stability at the enzyme’s binding pocket, while the water bridge helps the ligand bind effectively in the active site of receptors [55]. In this study, the RMSD and RMSF plots of the protein-ligand complexes showed that all the hit flavonoids attained significant stability. Also, the interaction plot obtained from the 100 ns MD simulation period showed that the complexes demonstrated appreciable hydrophobic interactions with enzymes at the active site of the estrogen receptor alpha enzyme.
Drug likeness is a vital parameter in estimating the suitability of chemical compounds as potential drug candidates [56]. In drug-likeness prediction, Lipinski’s rule of 5 can be considered such that the molecular weight of the studied compounds will be less than 500, hydrogen bond donors ≤5, hydrogen bond acceptors ≤10, molar refractivity between 40 and 130, and logP ≤ 5 [33]. Aside from Lipinski’s rule, a good drug candidate must pass the PAINS filter test. Pharmacokinetic parameters are relevant in the evaluation of phytochemicals as drug candidates. Solubility, gastrointestinal (GI) absorption, and bioavailability properties are vital for drug candidates, as they are easily absorbed into the GI of disease subjects. A chemical compound with suitable solubility properties should have a solubility index between −6.5 and 0.5. All the selected naturally occurring flavonoids exhibited good solubility and high GI properties. Furthermore, the toxicity profile of the hit molecules showed no toxicity potential, such that they are non-hepatotoxic and possess no AMES toxicity.
In the quantum chemical study of the potential estrogen receptor alpha inhibitors, the HOMO, LUMO, energy gap, ionization potential, electron affinity, electrophilicity index, chemical potential and hardness values were estimated. The EHOMO and ELUMO value of a drug candidate indicates its potential to donate and lose electrons, respectively [55], 57]. BG3 showed the greatest electron-donating tendency, while BG2 showed the highest electron-accepting tendency. The energy gap of chemical compounds provides valuable information on their chemical reactivity and pharmacological potentials against targeted diseases, while the electrophilicity index of chemical compounds gives concise information about their biological properties [58], 59]. In this study, all the phytochemicals gave comparable energy gap and electrophilicity values, indicating that they can work effectively as estrogen receptor alpha inhibitors. However, BG3 gave the highest energy gap and electrophilicity values. Therefore, the order of increasing energy gap value is BG3 > BG4 > BG2 > BG1, while that of electrophilicity index is in the order of BG3 > BG1 > BG2 > BG4. Chemical hardness and chemical potential are parameters used to estimate the stability and reactivity of drug candidates at the active site of receptors [60]. BG4 and BG1 gave the highest chemical potential and hardness values, indicating that these phytochemicals have more significant potential to become more reactive and stable when inhibiting the breast cancer enzyme. Ionization potential shows the bioavailability property of phytochemicals when administered as therapeutic agents against targeted diseases [57], 59]. The HOMO and LUMO plots of the hit molecules showed that electrons are delocalized over their pi-network and aromatic ring systems, which in turn, influences the efficiency of the flavonoids to effectively bind to the estrogen receptor alpha target and become more stabilized at its binding pocket. Remarkably, all the phytochemicals showed good bioavailability property. However, BG1 elicited the highest bioavailability index, followed by BG2, BG4, and BG3.
The present study evaluated the estrogen inhibitory potential of naturally occurring flavonoids from the Bauhinia genus using molecular docking, molecular dynamics simulation, drug-likeness and pharmacokinetics approaches. Of the 164 flavonoids screened against the estrogen receptor alpha enzyme, four phytochemicals were selected based on their binding affinity and drug likeness compliance. The flavonoids ranked in the order of BG1 > BG2 > BG3 > BG4 with respect to their binding affinity. All the hit molecules established key hydrogen bonds, hydrophobic and pi-interactions, confirming their stability at the enzyme’s binding pocket. The molecular dynamics simulation showed that all the hit flavonoids were stable in the 100 ns simulation. In contrast, the quantum chemical studies showed that they possess good bioavailability, electrophilicity, EHOMO and ELUMO properties. The drug-likeness and pharmacokinetic properties revealed the chemical compounds as potential drug candidates with good bioavailability index. The selected compounds in vitro and in vivo validation is recommended for further studies.