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Article

Structure-Guided Virtual Screening and Molecular Dynamics Simulation of Selective Small-Molecule Inhibitors Targeting E3 Ubiquitin Ligase MDM2 in Breast Cancer

Tahani Bakhsh1★★ Corresponding author

  1. 1Department of Biological Sciences, College of Science, University of Jeddah, Jeddah, SAUDI ARABIA.

CORRESPONDENCE

Tahani Bakhsh

Department of Biological Sciences, College of Science, University of Jeddah, Jeddah, SAUDI ARABIA. ; tahanibakhsh@hotmail.com

tabakh@uj.edu.sa

Received: 11-03-2026; Revised: 22-05-2026; Accepted: 03-07-2026.

Volume 16, Issue 4 · pp. 1642–1656 · PUBLISHED Oct-Dec 2026 · DOI: 10.5530/ijpi.20260058

View on IJPI original site ↗

ABSTRACT

Background MDM2 is an E3 ubiquitin ligase that suppresses p53, promotes its degradation, inhibits apoptosis, and drives uncontrolled cancer cell growth. Targeting the MDM2-p53 interaction is therefore a promising strategy for solid tumor therapy. Purpose To identify selective small-molecule MDM2 inhibitors with potential therapeutic value in breast cancer. Materials and Methods A structure-based virtual screening workflow was performed on the MCULE platform. Selected hits were assessed using PASS, cell line prediction, safety, pharmacokinetic, and ADMET analyses, followed by molecular dynamics simulations to evaluate complex stability. Results Virtual screening identified promising MDM2-targeting compounds. Subsequent profiling highlighted two lead candidates with predicted cytotoxic activity, favorable drug-like properties, and stable binding within the MDM2 pocket. Conclusion Two potential small-molecule MDM2 inhibitors were identified as promising lead compounds for restoring p53 activity and developing targeted breast cancer therapies.

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INTRODUCTION

Cancers remain one of the leading causes of mortality worldwide, and the dysregulation of cellular signaling pathways plays a critical role in tumor initiation and progression. Among these pathways, the p53 tumor suppressor pathway is essential for maintaining genomic stability by regulating cell cycle arrest, apoptosis, and DNA repair mechanisms. The Murine Double Minute 2 (MDM2) protein is an E3 ubiquitin ligase that negatively regulates p53 by promoting its ubiquitination and subsequent proteasomal degradation (Borrero et al., 2021; Sciot, 2021). MDM2 was initially derived from purified acentric chromosomes found in the cell line that had gone through spontaneous transformation 3T3DM by cloning. The reason behind cloning the genes present in these abnormal chromosomes, also known as double minutes, is that they are often the source of gene amplification, which may lead to cellular proliferation and tumorigenesis. Amplification of a genomic clone of MDM2 in rodent cells resulted in high tumorigenic potential in nude mice. This point of data hinted at the fact that MDM2 might be oncogenic (Wang et al., 2024; Yu et al., 2014). In parallel, researchers in another lab were isolating proteins that interacted with the p53 tumor suppressor protein, based on the assumption that such proteins might be capable of modulating p53 function. The MDM2 binds to p53 via its N-terminal domain leads to the formation of the MDM2, p53 complex. This causes the masking of the p53 transcription activation domain and finally leads to a decrease in the transcriptional activity of p53. As per the literature, MDM protein acts as oncoproteins, which is frequently overexpressed in a wide variety of human cancers, thus facilitating tumorigenesis (Zhu et al., 2025). Additionally, MDM2’s ring finger domain serves as an E3 ligase for p53, resulting in p53 ubiquitination. Ubiquitination is a kind of post-translational modification carried out by a cascade of E1, E2, and E3 enzymes, which mediate the linking of ubiquitin to the amino group of lysine residues of target or client proteins' side chains (Callis, 2014; Checler et al., 2000; Hepowit et al., 2022).

The overexpression of MDM2 disrupts p-53 mediated tumor suppression, allowing cancer cells to evade apoptosis and proliferate. Normally, levels of tumor suppressor p53 remain very low due to MDM promoting p53 ubiquitination and degradation (Russano et al., 2025). The most widely researched link between MDM proteins and cancer is their capability to bind ubiquitinate and degrade p53 through 26s proteasome (Hepowit et al., 2022). When cells face DNA damage, oxidative stress, or lack of oxygen, MDM proteins are suppressed and p53 starts to accumulate. This results in triggering various signaling cascades that in turn cause cell cycle halt, DNA fixing, and cell death (Abuetabh et al., 2022).

MDMs suppress p53 level and function through the following main mechanism: though directly binding to p53 through its N-terminal domains, MDM2 blocks the transcription of p53-responsive genes. Secondly, MDMs regulate p53 in a way that they have the ability to form a dimer and catalyze the degradation of p53 protein via ubiquitin dependent pathway. Furthermore, MDMs have the potential to lead to the p53 shuttling from the nucleus, thereby hindering the binding of p53 to its targeted genes. It also stated that MDM2 lowers p53 activity levels (Brown et al., 2026; Subhasree et al., 2013; Zhao et al., 2015). Later, it was shown that MDM2 overexpression inhibited the p53 mediated activation of a reporter gene containing the responsive element. A possible mechanism of MDM2 oncogenicity was therefore proposed: inactivation of the p53 tumor suppressor gene. Besides that, various research indicates that MDM proteins also work independently of p53 in various cellular functions such as where the cell is at cell cycle control, cell differentiation, and metabolism, which broadens the functions of proto-oncoproteins even more (Kung et al., 2022).

MDM2 itself has direct tumorigenic capacity supporting oncogenic behavior after overexpression. Additionally, different types of MDM2 mRNA splicing variants and post-translational modifications may alter the course of the disease. Investigations have revealed that 5.9% of breast cancer cases had MDM2 gene amplification, which was about 17 times higher than that of normal cells. P21, a downstream cell cycle protein kinase inhibitor of p53, can block the cell cycle progression, exerting an anti-cancer effect in breast cancer (Shamloo et al., 2019; Sun et al., 2023). Estrogen Receptor alpha (ER) is a nuclear receptor and an oncoprotein that is expressed in approximately 70% of breast cancers and strongly correlates with MDM2. ER positively regulates MDM2 expression via p53 independent pathway while MDM2 induces ubiquitination and degradation of ER by forming a protein complex with it. Upon chemotherapy, MDM2 is capable of functioning as an E3 ubiquitin ligase to induce apoptosis in breast cancer cells (Habara et al., 2022; Patel et al., 2022; Tecalco-Cruz et al., 2017). A decrement of KLF6 expression results in an increase in it and decrease in p53 indicating that this dysregulation in MDM2 pathways is additionally correlated with the reduced survival of HCC patients after surgery (Choudhary et al., 2013).

In view of the context, it creates considerable prevalence of malignancies resulting in high morbidity and mortality. Therefore, disrupting MDM2-p53 interaction becomes a vital therapeutic strategy. Various small-molecule inhibitors have been developed, exhibiting some limitations. RG7112 (Nutlin-3a Compound) and similar agents can cause MDM2 inactivation through apoptosis. Nutlin 3a is well documented to block MDM2 p53 binding, thereby enhancing p53-dependent programmed cell death in osteosarcoma (Psatha et al., 2023). In addition to targeting the p53 axis, modulation of associated oncogenic pathways, such as STAT signaling, heat shock proteins are found to be involved in protein folding and stress response pathways that support tumor cell survival. Similarly, Matrix Metalloproteinase-9 (MMP-9) contributes to tumor invasion and metastasis (Lang et al., 2019). Therefore, our study employed a computational in silico approach to uncover the potential small-molecule inhibitors of MDM2 via structure-based drug design. The investigation was conducted using a variety of characteristics, including pharmacokinetics, drug-likeliness aspects, lethality predictions, molecular interaction, and molecular dynamics. The MDM2 protein was chosen for the investigation, with its own inhibitor treated as a control compound. Surpassing these parameters enables the identification of the potent lead candidates.

MATERIALS AND METHODS

Obtaining and Preparing the Target Protein

Protein Data Bank (https://www.rcsb.org/) was utilized for retrieving our aimed protein 3LBL in the pdb format. The protein was prepared by removing unwanted obstacles and elongating the hydrogen bonds through the BIOVIA Discovery Studio Visualizer 2021 (Berman, 2023).

Structure-Based Virtual Screening of Small Molecules

The Mcule platform was utilized for screening of small compounds, which provides comprehensive curated molecules for computer-aided drug discovery. It's a combination of multiple approaches within a single platform, such as hit identification, ligand-based screening, structure-based screening, access to purchasable compounds, and many more, enabling exploration of chemical spaces (Kiss et al., 2012; Malik et al., 2019; Sait et al., 2019).

We have employed the hit-identification workflow available within the structure-based virtual screening module of the Mcule platform for retrieving potential small compound inhibitors. Under the workflow section, different parameters were applied. RO5 violation was set to 0 to 1 (Muteeb et al., 2022). Rest was minimized. In the diversity selection, a sampler was selected, and a default value was selected. This allows direct screening of extensive compound libraries against a predefined target protein. The three-dimensional pre-processed targeted protein was then uploaded to the Mcule platform, and docking studies were performed using the Autodock Vina-enabled Mcule screening platform. The complex dimensions were used for grid box values (Alsamghan et al., 2020; Malik et al., 2019; Trott et al., 2010; Roskoski, 2023).

Inhibitor Docking Assessment

Additionally, the inhibitor compound (Control) docking was also conducted. 1-Click docking under lead optimization was utilized within the same Mcule platform. The inhibitor compound was downloaded from the protein data bank attached to the protein. This compound was then uploaded to 1-click docking, and the prepared target protein was loaded. Similarly, the binding dimensions were pasted and retrieved from the macromolecule complex. The results were generated based on different poses. Among those, the best pose having the highest docking score was selected and downloaded.

Structure-Based Toxicity Filtration

The generated small compounds were further tested for toxicity check based on the compounds' structure. The toxicity was also checked using Toxicity checker within the integrated Mcule platform (Kiss et al., 2012). All the compounds were checked individually by simply clicking on compounds ID and click for toxicity checker. These results will be displayed as PASS or FAIL, indicating compound toxicity and non-toxicity. This lead in sorting the filtering of the compounds.

Biological Activity Spectra Prediction

The biological activity spectrum was conducted to focus on the biologically active substances. For that, the online software PASS (prediction of activity spectra for substances) was utilized, which is widely used to predict more than 300 pharmacological properties based on the structure SMILES (Parasuraman, 2011). The SMILES generated from the Mcule platform were copied and pasted one by one. The results were generated with pa (probability of activity) and pi (probability of inactivity) values. The desired properties related to MDM2 were selected and retrieved. Compounds with higher Pa values compared to Pi values were also focused.

Study of Cancerous Cell Line Activity

The compound’s potential to act against cancer was forecasted by use of the online server CLC-Pred. its capable of determining cytotoxic effects of chemical compounds against different human cancer cell lines on the basis of the structure similarity of anticancer agents already known. For that, the SMILES were again pasted on the server. The results were generated with probabilities of compound activity and inactivity against different cell lines. Additionally, in this IAP values were also generated (Inferred Antitumor Activity Probability), indicating the likelihood that the compound may exhibit. The greater the value, the more promising the potential will be (Bishwakarma et al., 2026; Cheng et al., 2024; Lagunin et al., 2023).

Prediction of Compounds Lethality

The online server GUSAR (http://www.way2drug.com/gusar/ac utoxpredict.htmL) incorporated within the way2drug platform was used to predict acute rat toxicity. It presents the median LD50 values for different routes of administration, including oral, intravenous, intraperitoneal, and subcutaneous. They were also assigned to different classes according to the toxicity classification system (Zakharov et al., 2012).

Side Effects Profiling Assessment

The possible side effects were predicted using the online server ADVER-PRED (http://www.way2drug.com/adverpred/). The server predicts the side effects based on structure similarity, trained on known drug safety data, enabling early identification of potential safety risks (Ivanov et al., 2018).

Pharmacokinetics Profiling

The pharmacokinetic properties of the compounds were analyzed through the pkCSM server. The tool is widely utilized for predicting the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) parameters, including absorption, distribution, metabolism, excretion, and toxicity. Parameters such as intestinal absorption, blood-brain barrier permeability, cytochrome P450 interactions, and total clearances can also be assessed in this to evaluate the drug-like properties and physiological behavior of the compounds. To that end, Deep-Pk, the extended version of pkCSM, was also used to examine additional features, such as biocontamination and environmental factors. SMILES were respectively used to check all these druggable features contributing to understanding the systemic exposure and stability of compounds in biological systems (Bultum et al., 2022; Pires et al., 2015).

Profiling of Simulation Studies

Molecular dynamics simulations are used to capture the movement of the atoms in a system. Therefore, as per the integrated analyses of all the parameters, the best compounds were selected to check for stability. There are different parameters in simulation studies for depicting the investigations such as RMSF (Root Mean Square Fluctuation), RMSD (Root Mean Square Deviation), Solvent surface area, radius of gyration, and hydrogen bonds. The dynamics simulation took place at 300 k by utilizing the GROMACS software with version 5.1.2. Afterward, complexes and topologies were created using the gmx grep of the GROMACS package. The forcefield CGenFF (CHARMm general force field) was then applied. The complexes were solvated using the TIP3 cubic box model. The next step was to neutralize the charges by adding salt after the boundary conditions (Khan et al., 2022; Saxena, 2019). With 25,000,000 steps, the steepest descent algorithm was utilized for minimizing the energy. With constant volume of temperature and pressure conditions (NVT and NPT), the temperature was set. At last, the mesh was generated using the Ewald method, and the simulation was set to perform at 100ns to gain deeper insights. Every individual parameter was considered and analyzed carefully to obtain the refined compounds (Noumi et al., 2025; Petersen, 1995; Rahmati et al., 2025).

RESULTS

Screening of small molecules

A total of 100 molecules were generated along with Lipinski violations. The docking studies revealed binding energies ranging from -11.1 kcal/mol to -8.4 kcal/mol. All 100 molecules were tested for structural toxicity. The toxicity checker within the Mcule platform revealed 38 safer compounds in terms of structure. However, the inhibitor compound revealed toxicity with -7.8 kcal/mol binding energy, proving the compounds are better. The safer compounds with their binding energies and violations are listed in Table 1. The smiles of these compounds were extracted and saved for further parameterization.

Table 1: Docking scores with RO5 violations of filtered compounds.
MCULE IDDocking scoreRO5 violations
MCULE-6681529935-0-134-10.11
MCULE-6464780543-0-172-9.91
MCULE-9772505960-0-245-9.81
MCULE-8883993037-0-266-9.71
MCULE-7263785423-0-231-9.51
MCULE-8615044604-0-132-9.41
MCULE-3030350691-0-213-9.41
MCULE-3437839167-0-295-9.30
MCULE-1436369022-0-215-9.31
MCULE-8454446844-0-136-9.31
MCULE-4058053946-0-211-9.30
MCULE-7331047671-0-140-9.31
MCULE-2446676144-0-221-9.21
MCULE-3340241418-0-122-9.21
MCULE-7324342249-0-67-9.21
MCULE-8554429594-0-215-9.21
MCULE-2250129412-0-224-9.21
MCULE-8527570511-0-144-9.11
MCULE-7816669746-0-213-9.11
MCULE-1116419285-0-129-91
MCULE-7950646640-0-209-8.81
MCULE-3528442415-0-272-8.81
MCULE-5174431551-0-247-8.70
MCULE-7895890639-0-176-8.71
MCULE-1387096425-0-286-8.71
MCULE-9377071399-0-227-8.71
MCULE-9968692477-0-141-8.71
MCULE-8263048398-0-238-8.60
MCULE-1672373270-0-176-8.61
MCULE-6928036014-0-245-8.61
MCULE-9247823479-0-203-8.51
MCULE-6647072513-0-199-8.50
MCULE-7966990722-0-154-8.41
MCULE-4407680809-0-181-8.40
MCULE-5556268030-0-163-8.40
MCULE-4592638188-0-189-8.40
MCULE-9270762767-0-241-8.40
MCULE-7383585135 -Mi-63*-7.82

Assessing Biological Activity Parameters

The biological activity spectra of the compounds were predicted using the online software PASS prediction. The results in Table 2 revealed the compounds showing different probability scores against parameters. The activities related to MDM2 were focused on selecting parameters such as antineoplastic, Transcription Factor STAT inhibitors, MMP9 (Extracellular Matrix Modulation), and heat shock protein inhibitors, along with the control (Bhatia et al., 2023; Camps-Fajol et al., 2025; Wolosowicz et al., 2025). A total of 10 compounds with 0 violations have been opted out for investigating several different parameters. The results were analyzed based on the probability of being active and inactive.

Table 2: PASS Prediction Parameters.
CompoundsTF STAT3 inhibitorTF STAT inhibitorHSP-27 antagonistHSP-90 antagonistAntineoplasticMMP9 Expr. inhibitor
PaPiPaPiPaPiPaPiPaPiPaPi
MCULE-3437839167-0-2950,4390,0260,4790,0250,2950,231NANA0,1050,0650,2200,178
MCULE-4058053946-0-2110,5390,0130,5070,019NANANANANANANANA
MCULE-5174431551-0-2470,2670,0840,2320,1190,3360,149NANA0,2440,0170,6860,008
MCULE-8263048398-0-238NANA0,1790,171NANA0,0780,070NANANANA
MCULE-6647072513-0-199NANANANANANANANANANANANA
MCULE-4407680809-0-1810,4570,0230,4360,036NANANANANANANANA
MCULE-5556268030-0-163NANANANANANA0,0930,051NANANANA
MCULE-4592638188-0-1890,4850,0200,4580,0300,6070,0050,1070,037NANA0,2310,164
MCULE-9270762767-0-2410,5920,0080,5630,0100,3930,071NANA0,2010,0560,2790,117
MCULE-8527570511-0-1440,4880,0190,5150,018NANANANA0,3700,116NANA
ControlNANANANANANANANA0,7370,020NANA

The series demonstrated that the control compound exhibited a high confidence score only in general antineoplastic activity but lacked other targets such as STAT3 or MMP9. The MCULE-9270762767 emerged as a promising inhibitor for STAT3 inhibitors, having the favorable pa>pi values. Also showing favorable values for other inhibitors, as well as lacking HSP-90 activity. Furthermore, MCULE-4592638188 was also identified as a potential HSP27 antagonist, supporting other parameters as well. However, the antineoplastic activity was not found. The other compound, MCULE-8527570511, displayed a balanced profile with moderate STAT inhibition and the highest antineoplastic potential among the tested compounds, showing that it belongs to the class of anti-cancer agents as well.

Prediction of Cell Lines

The computational analysis of cell line cytotoxicity provides a correlation between predicted molecular targets and specific cancer types. Among the multiple cancerous cell lines, only those that are related to the MDM2 axis were selected. Cell lines such as breast and lung carcinomas significantly overexpressed the MDM2 or amplified gene alongside p53. The abnormal elevation results in excessive degradation of p53, leading to the inability to trigger apoptosis (Di Grazia et al., 2025; Hou et al., 2019). Similarly, it confers resistance to conventional treatments in lung cancer. The other cell line of leukemia was also focused on solid tumors like the lung and breast (Mirabelli et al., 2019; Whiteley et al., 2021). Likewise, other cell lines are also focused on. A detailed cell line type is mentioned for the chosen compounds in Table 3.

Table 3: Probability Score of Various Cancerous Cell Lines.
CompoundsPaPiCell-lineDescriptionTissue/OrganTypeIAP*
Control0.6460.004RS4-11Adult B acute lymphoblastic leukemiaBone MarrowLeukemia0.835
0.4720.009SW480Colon adenocarcinomaColonAdenocarcinoma0.932
0.4570.001SJSA-1OsteosarcomaBoneSarcoma0.94
0.3960.05HCT-116Colon carcinomaColonCarcinoma0.89
0.350.023HCC1954Breast CarcinomaBreastCarcinoma0.829
0.2660.179CL97Lung adenocarcinomaLungAdenocarcinoma0.872
0.2070.205MDA-MB-361Breast adenocarcinomaBreastAdenocarcinoma0.872
0.1180.102NCI-H128Small cell lung cancerLungCarcinoma0.908
10.3650.078MDA-MB-231Breast adenocarcinomaBreastAdenocarcinoma0.881
0.3590.076COR-L23Lung large cell carcinomaLungCarcinoma0.834
0.3120.094HCC1954Breast CarcinomaBreastCarcinoma0.829
0.2920.024MDA-MB-453Breast adenocarcinomaBreastAdenocarcinoma0.888
0.2380.074RKOColon carcinomaColonCarcinoma0.879
0.2090.009HOS-TE85OsteosarcomaBoneSarcoma0.844
0.2060.13MDA-MB-468Breast adenocarcinomaBreastAdenocarcinoma0.898
0.1440.076SJSA-1OsteosarcomaBoneSarcoma0.94
0.1320.094T47DBreast carcinomaBreastCarcinoma0.936
0.1110.022GHOSTCXCR4OsteosarcomaBoneSarcoma1
20.5080.006COR-L23Lung large cell carcinomaLungCarcinoma0.834
0.3290.048HTLymphomaHaematopoietic and lymphoid tissueLeukemia0.857
0.3080.103HCC1954Breast CarcinomaBreastCarcinoma0.829
0.2510.065U2OSOsteosarcomaBoneSarcoma0.885
0.2480.094MDA-MB-468Breast adenocarcinomaBreastAdenocarcinoma0.898
0.1740.036NCI-H1993Non-small cell lung cancerLungAdenocarcinoma0.913
0.1330.122LoVoColon adenocarcinomaColonAdenocarcinoma0.911
30.6740.006NCI-H460Non-small cell lung carcinomaLungCarcinoma0.906
0.5790.018HT-29Colon adenocarcinomaColonAdenocarcinoma0.888
0.3240.191MCF7Breast carcinomaBreastCarcinoma0.836
0.3220.045CL1-0Lung adenocarcinomaLungAdenocarcinoma0.83
0.310.017MDA-MB-453Breast adenocarcinomaBreastAdenocarcinoma0.888
0.2760.109K562ErythroleukemiaHaematopoietic and lymphoid tissueLeukemia0.892
0.2140.071HELErythroleukemiaBloodLeukemia0.903
0.1820.096LS174TColon adencocarcinomaColonAdenocarcinoma0.91
0.1510.055HOS-TE85OsteosarcomaBoneSarcoma0.844
0.1480.096Caco-2Colon adenocarcinomaColonAdenocarcinoma0.898
0.1060.097143BOsteosarcomaBoneOsteosarcoma0.931
0.1050.044LXFL529Lung non-small cell carcinomaLungCarcinoma0.979
0.0980.022MAXF401Breast carcinomaBreastCarcinoma0.983
0.0310.019H322Lung carcinomaLungCarcinoma1
0.0240.008MCF7RBreast carcinomaBreastCarcinoma0.996
0.0120.008Lu1Lung carcinomaLungCarcinoma0.995
0.0110.004Col2Colon carcinomaColonCarcinoma1
40.2850.014NCI-H1299Non-small cell lung carcinomaLungCarcinoma0.916
0.2650.236MCF7Breast carcinomaBreastCarcinoma0.836
0.2330.166MDA-MB-231Breast adenocarcinomaBreastAdenocarcinoma0.881
50.9230.003HCC2998Colon adenocarcinomaColonAdenocarcinoma0.872
0.9230.003NCI-H226Non-small cell lung carcinomaLungCarcinoma0.88
0.9140.003NCI-H322MNon-small cell lung carcinomaLungCarcinoma0.881
0.9020.004NCI-H522Non-small cell lung carcinomaLungCarcinoma0.877
0.8930.005HCT-116Colon carcinomaColonCarcinoma0.89
0.8880.004KM12Colon adenocarcinomaColonAdenocarcinoma0.875
0.7880.004MDA-MB-468Breast adenocarcinomaBreastAdenocarcinoma0.898
0.7630.004NCI-H23Non-small cell lung carcinomaLungCarcinoma0.883
0.7530.005OVCAR-3Ovarian adenocarcinomaOvariumAdenocarcinoma0.907
0.7330.004HOP-62Non-small cell lung carcinomaLungCarcinoma0.898
0.610.02MDA-MB-231Breast adenocarcinomaBreastAdenocarcinoma0.881
0.510.018NCI-H460Non-small cell lung carcinomaLungCarcinoma0.906
0.430.007U2OSOsteosarcomaBoneSarcoma0.885
0.2910.106HT-29Colon adenocarcinomaColonAdenocarcinoma0.888
0.2740.112C8166Leukemic T-cellsBloodLeukemia0.839
0.1350.115HOS-TE85OsteosarcomaBoneSarcoma0.844
60.3330.219DU-4475Breast CarcinomaBreastCarcinoma0.802
0.2910.059NCI-H1581Non Small Cell Lung CancerLungCarcinoma0.871
0.1910.026LoVoColon adenocarcinomaColonAdenocarcinoma0.911
0.1860.028NCI-H2228Non-small cell lung cancerLungAdenocarcinoma0.92
0.1460.035PC-9Lung adenocarcinomaLungAdenocarcinoma0.951
0.1430.078SJSA-1OsteosarcomaBoneSarcoma0.94
0.0320.019HCC78Lung adenocarcinomaLungAdenocarcinoma0.989
70.3120.01NCI-H1299Non-small cell lung carcinomaLungCarcinoma0.916
0.2730.132MDA-MB-231Breast adenocarcinomaBreastAdenocarcinoma0.881
0.2140.198HOP-92Non-small cell lung carcinomaLungCarcinoma0.862
0.1470.063SJSA-1OsteosarcomaBoneSarcoma0.94
80.3750.055COR-L23Lung large cell carcinomaLungCarcinoma0.834
0.3730.149DU-4475Breast CarcinomaBreastCarcinoma0.802
0.370.144HCC1937Breast CarcinomaBreastCarcinoma0.806
0.3440.038NCI-H661Lung carcinomaLungCarcinoma0.831
0.3360.134CAL-51Breast carcinomaBreastCarcinoma0.824
0.3160.126DMS-114Lung carcinomaLungCarcinoma0.834
0.3030.151RS4-11Adult B acute lymphoblastic leukemiaBone MarrowLeukemia0.835
0.2940.131CL97Lung adenocarcinomaLungAdenocarcinoma0.872
0.2850.094HOSOsteosarcomaBoneSarcoma0.863
0.2810.187HCC1954Breast CarcinomaBreastCarcinoma0.829
0.2650.109HCT-116Colon carcinomaColonCarcinoma0.89
0.2620.165NCI-H1703Non Small Cell Lung CancerLungCarcinoma0.851
0.2560.114NCI-H1581Non Small Cell Lung CancerLungCarcinoma0.871
0.2540.052MDA-MB-453Breast adenocarcinomaBreastAdenocarcinoma0.888
0.2480.132CL1-0Lung adenocarcinomaLungAdenocarcinoma0.83
0.2420.099MDA-MB-361Breast adenocarcinomaBreastAdenocarcinoma0.872
0.2260.111MDA-MB-468Breast adenocarcinomaBreastAdenocarcinoma0.898
0.2250.096U2OSOsteosarcomaBoneSarcoma0.885
0.2130.133RKOColon carcinomaColonCarcinoma0.879
0.210.125HOP-18Non-small cell lung carcinomaLungCarcinoma0.933
0.2060.02LoVoColon adenocarcinomaColonAdenocarcinoma0.911
0.2030.034SK-BR-3Breast adenocarcinomaBreastAdenocarcinoma0.95
0.1640.143LS174TColon adencocarcinomaColonAdenocarcinoma0.91
0.1570.031SJSA-1OsteosarcomaBoneSarcoma0.94
0.1330.024143BOsteosarcomaBoneOsteosarcoma0.931
0.0910.025NCI-H2286Small cell lung cancerLungAdenocarcinoma0.968
0.0880.007HEYOvarian carcinomaOvariumCarcinoma0.967
0.0450.007H322Lung carcinomaLungCarcinoma1
0.0240.017C180-13SOvarian carcinomaOvariumCarcinoma0.999
90.3770.013U2OSOsteosarcomaBoneSarcoma0.885
0.3770.038HEK293Embryonic kidney fibroblastKidneyNormal0.898
0.3610.022NCI-H661Lung carcinomaLungCarcinoma0.831
0.3090.056NCI-H1703Non Small Cell Lung CancerLungCarcinoma0.851
0.2260.176CL1-0Lung adenocarcinomaLungAdenocarcinoma0.83
0.1770.169HOP-18Non-small cell lung carcinomaLungCarcinoma0.933
0.1580.041HOS-TE85OsteosarcomaBoneSarcoma0.844
0.1310.128LoVoColon adenocarcinomaColonAdenocarcinoma0.911
0.0790.043NCI-H2286Small cell lung cancerLungAdenocarcinoma0.968
100.4880.013MDA-MB-468Breast adenocarcinomaBreastAdenocarcinoma0.898
0.2790.087NCI-H522Non-small cell lung carcinomaLungCarcinoma0.877
0.2350.076MDA-MB-453Breast adenocarcinomaBreastAdenocarcinoma0.888
0.1050.008NCI-H2171Small cell lung cancerLungCarcinoma0.953
0.7530.005MOLT-4Acute T-lymphoblastic leukemiaBloodLeukemia0.885

*IAP: Invariant Accuracy of Prediction.

The compounds are renamed in numeric form from MCULE IDs. From the table, it has been observed that the control inhibitor compound maintained a high-confidence activity profile against leukemia and colorectal models, lacking the targeting characteristics more often. Whereas, the tested compounds exhibited a refined profile for solid tumor lineages with the exceeding IAP values of 0.8, reinforcing robustness, especially in the fields of lung and breast cancers. Interestingly, the highest IAP values for lung cancer and more breast cell lines were demonstrated by compound 8, whereas compound 10 showed the highest IAP values, especially in breast cancer type, suggesting the reliability of addressing p53-mediated degradation in these specific malignancies.

Acute Rat Toxicity Analysis

The toxicity profile of the lead candidates was further scrutinized through quantitative-structure activity relationship. The evaluation was done on the basis of acute toxicity (LD50) across different administrative routes to determine if the compounds fall within the Applicable Domain (AD) to ensure reliability (Gadaleta et al., 2019; Zwickl et al., 2022). Every route was determined in mmol/kg, mg/kg, and with class shown in Table 4.

Table 4: Acute Toxicity Prediction.
Sl. No.IP LD50 Log 10IV LD50 Log 10oral LD50 Log 10SC LD50 Log 10
mmol/kgmg/kgClassmmol/kgmg/kgClassmmol/kgmg/kgClassmmol/kgMg/kgClass
10,097 in AD481,000 in ADIV in AD-0,847 in AD54,680 in ADIV in AD0,455 in AD1095,000 in ADIV in AD0,352 in AD865,500 in ADIV in AD
20,193 in AD630,500 in ADV in AD-0,687 in AD83,090 in ADIV in AD0,300 in AD807,500 in ADIV in AD0,686 out of AD1964,000 out of ADV out of AD
30,528 in AD871,500 in ADV in AD-0,795 in AD41,430 in ADIV in AD0,586 in AD996,500 in ADIV in AD0,328 in AD549,100 in ADIV in AD
40,262 in AD819,000 in ADV in AD-0,321 in AD213,800 in ADIV in AD0,189 in AD692,300 in ADIV in AD0,485 out of AD1368,000 out of ADV out of AD
50,252 out of AD551,300 out of ADV out of AD-0,058 in AD270,100 in ADIV in AD0,805 in AD1966,000 in ADIV in AD0,234 in AD528,200 in ADIV in AD
60,634 in AD1390,000 in ADNon-toxic in AD-0,687 in AD83,760 in ADIV in AD0,337 in AD700,900 in ADIV in AD0,486 in AD988,300 in ADV In AD
70,135 in AD502,800 in ADV in AD-0,217 in AD223,700 in ADIV in AD0,263 in AD675,500 in ADIV in AD0,819 in AD2340,000 in ADIV in AD
80,271 in AD602,300 in ADV in AD-0,281 in AD168,800 in ADIV in AD0,786 in AD1968,000 in ADIV in AD0,933 out of AD2762,000 out of ADV out of AD
90,483 in AD1150,000 in ADV in AD-0,427 in AD141,600 in ADIV in AD0,733 in AD2046,000 in ADV in of AD1,231 in AD6441,000 in ADNT Out Of AD
100,175 out of AD646,700 out of ADV out of AD-0,416 in AD165,800 in ADIV in AD0,263 in AD792,700 in ADIV in AD0,267 out of AD800,000 out of ADNT in AD
Control-0,039 out of AD527,800 out of ADV out of AD-1,150 in AD40,930 in ADIV in AD0,164 in AD841,700 in ADIV in AD0,518 out of AD175,000 out of ADIV Out Of AD

The table revealed that compounds possess a significant safety margin as compared to control one. All the compound showed higher values indicating higher dosage threshold before toxicity occurs. Whereas, most of the predictions including control falls into class IV and V which are characterized as low-toxicity categories.

Safety Profile Assessment

To assess the safety profile of the compounds, the possible side effects were predicted using the Adver-Pred server. The investigation evaluates the likelihood of compound based on its structural features by employing the Pa and Pi scores to ensure data reliability (Table 5).

Table 5: Predicted Adverse Reactions of Compounds.
CompoundsPa/PiHepatotoxicityArrhythmiaMyocardial infarctionCardiac failureNephrotoxicity
1Pa0.7730.5710.2870.24NA
Pi0.070.0610.2330.231NA
2PaNANANANANA
PiNANANANANA
3Pa0.6650.4280.5350.478NA
Pi0.1140.1640.0380.067NA
4Pa0.639NANA0.281NA
Pi0.125NANA0.192NA
5Pa0.7740.3660.6290.7050.379
Pi0.0830.2160.0240.0070.097
6Pa0.558NA0.5790.61NA
Pi0.165NA0.0310.018NA
7PaNANA0.3370.258NA
PiNANA0.1330.213NA
8Pa0.4250.4260.294NANA
Pi0.2410.1650.213NANA
9PaNA0.4580.3840.3540.336
PiNA0.1420.0790.1280.124
10Pa0.6770.435NANANA
Pi0.1090.089NANANA
ControlNANANANANA

The analysis revealed that the compounds maintained favorable safety profile. The primary concern identified across all compounds was hepatotoxicity. However, it was also noticed that compound 10 significantly showed lower probabilities exhibiting only 1 side effect. However, there are some compounds which also showed 2-3 side effects. Whereas, no side was observed for compound 2. The results indicate some possible side effects through which preventive steps could be taken.

Drug-likeness Profiling

The pharmacokinetics properties of the candidates were evaluated to determine ADMET (Absorption Distribution, Metabolism, excretion and Toxicity) parameter. Table 6 provides a detailed comparative assessment among the compounds along with the control compound across key physiological parameters.

Table 6: Pharmacokinetics Profiling.
Sl. No.water solubilityCaco2HIASkinBBBCYP2D6ClearanceRenalAMESMax DosehERG IhERG II
1-5.3511.24692.073-2.7070.015No0.979NoYes0.308NoYes
2-4.6640.51794.057-2.675-0.684No0.192NoYes-0.138NoYes
3-6.2731.61197.575-2.6640.716No0.139NoYes0.431NoYes
4-5.2971.57289.964-3.0260.184No0.034NoNo0.253NoYes
5-4.3261.29695.726-2.7730.2No0.192NoYes0.188NoYes
6-4.141.55395.384-2.7350.124No0.091NoNo-0.047NoYes
7-4.8121.40791.048-3.0310.215No0.128NoNo0.333NoNo
8-4.371.36192.317-3.064-0.032No0.178NoNo-0.286NoYes
9-5.771.25188.78-2.7150.119No0.004NoYes0.42NoYes
10-6.8591.25689.378-2.8390.121No0.207NoNo0.935NoYes
Ref-4.0270.99395.343-2.736-0.835No0.538NoNo-0.546NoYes

The analysis revealed maintenance of high human intestinal absorption levels, improving membrane permeability. The clearance parameter possesses reduce need for frequent dosing, lacking control, balanced by the BBB penetration potential. Majorly, compounds also showed non-mutagenic status. Additional analysis by the Deep-Pk predicted highly safe biodegradation and Avian highly safe-medium profile supporting compounds, environment-friendly (Sadia et al., 2024). Therefore, the compounds represent an overall safety window.

Overall, the integrative analysis highlights the selection of final top compounds 8 and 10 as candidates for simulation studies. The combined analysis provides multidimensional validation. The data revealed that these compounds demonstrated the refined potential as STAT3, HSP-27, and MMP-9 inhibitors, showing the broad spectrum of cell lines against lung and breast carcinomas. They also represented higher LD50 values, outperforming the control in the safety panel. They also predicted reduced hepatotoxicity, enhanced pharmacokinetics profiling, and a non-mutagenic status of AMES with lower systemic clearance. Therefore, these compounds were selected to check for further simulation studies. The interacting diagram of these compounds, along with the control has illustrated in Figures 1A-C.

Figure 1: (A) Interacting Illustration of Compound 8 represented with different Poses, including surface pose, (i) 2-D interaction, and (ii) 3-D interaction. (B) Interacting Illustration of Compound 10 represented with different Poses, including surface pose, (i) 2-D interaction, and (ii) 3-D interaction. (C) Interacting Illustration of Reference Compound represented with different Poses, including surface pose (i) 2-D interaction, and (ii) 3-D interaction.

The figure showed representations of different poses of compounds, including an inhibitor compound. Different interacting bonds have been observed in a 2D diagram, such as van der Waals interaction, conventional hydrogen bonds, Pi alkyl, alkyl, Pi sigma, carbon hydrogen bonds, and halogen interactions. Interestingly, the interaction diagram showed most of the common residues of inhibitors found in both compounds. The commonly residues are noted as PHE55, VAL93, LEU54, VAL75, GLY58, ILE61, PHE91, ILE99, MET62, TYR67, ILE103, PHE86, LEU57, and ILE19. However, some additional residues, TYR100, MET17, and HIS96, are also observed in compound 10 from the reference inhibitor. This indicates a strong interacting profile of the compounds and support the findings.

Simulation Studies

To evaluate the stability of the compound and the binding persistence of the selected lead candidates within active sites, a simulation was performed at 100 ns. The analysis provides atomistic insights into the structural integrity of the protein-ligand complex. To depict the results, plots of RMSF, RMSD, SASA, Solvation, and Rg were generated.

Predicting Stability and flexibility (RMSD and RMSF)

The structural stability of complexes was analyzed through RMSD throughout the simulation (Maruyama et al., 2023). The plot revealed that the reference molecule exhibited significant fluctuations ranging between 0.20 nm and 1.40 nm, particularly during approx. 60 ns, suggesting instability throughout the trajectory. In the contemporary, both complexes outperformed the control, showing the steadiness and stability throughout the simulation. Comparatively, complex 8 showed a range between 0.20-0.30 nm with some spikes reaching approx. 1.20 nm indicative of relatively stable binding. Similarly, compound 10 also demonstrated stable behavior maintained within 0.20-0.35 nm with limited fluctuations up to 1.10 nm, particularly during the later stage.

On the other hand, fluctuation analysis was performed through RMSF to evaluate the flexibility of the residues (Fatriansyah et al., 2022). Both compounds displayed lower values across residues, indicating rigidity of the protein. The major fluctuation was observed within 0.05-0.12 nm by compound 8, for compound 10, it maintained larger RMSF values within the range of 0.06-0.15 nm with minor peaks. Whereas the reference compound exhibited higher fluctuations across several regions throughout the simulation. A prominent peak was also noticed around 35-40 residues with average value 0.32-0.35 nm, demonstrating significant flexibility. The plots of RMSD and RMSF are given in Figure 2.

Figure 2: (A) RMSF and (B) RMSD plots throughout the simulation. Reference molecule is in black, blue color shows compound 8 and green shows compound 10.

Solvent Accessible Surface Area

The SASA (Solvent Accessible Surface Area) was calculated to determine the conformation and protein folding changes that occurred throughout the simulation (Mukherjee et al., 2018). The reference compound showed significant fluctuations ranging between 12 and 18, indicating compactness instability. In contrast, both compounds maintained consistent SASA values. Compound 8 displayed lower SASA values, mainly fluctuating between 5 nm2 and 8 nm2, indicating a more compact protein conformation with reduced solvation exposure. To some extent, compound 10 maintains values between approximately 2-6 nm2, reflecting the highest degree of compactness represented in Figure 3A.

Figure 3: (A) SASA plot of the protein complex. (B) Solvation free energy analysis of the protein complex. (C) Radius of gyration plot (nm). (D) Hydrogen bond analysis. In all panels, black represents the reference compound, blue represents Compound 8, and green represents Compound 10.

Solvation Free Energy Analysis

The solvation energy depicts changes in Gibbs free energy, which measures the energetic favorability of the interaction of the complexes. The lower values indicate stable solvation interaction (Huang et al., 2024). The values are plotted against a 100 ns simulation time depicted in Figure 3B. From the thorough analysis, the reference compound exhibited negative solvation values fluctuating between -15 and -25. However, it also showed noticeable fluctuations. For compound 8, it displayed moderate values ranging between -6 and 10, which remained stable across the entire simulation, indicating a stable conformation. For compound 10, the investigation highlights the fluctuation between -5 and -10 nm, showing almost the same as compound 8, indicating stable solvation behavior.

Radius of Gyration Determination

The Rg was analyzed to evaluate the compactness and overall structural stability of protein-ligand complexes, where lower values indicate more compactness and stability or protein conformation (Wang et al., 2024). The reference compound showed steadiness at 2 nm, showing some minute peaks. Whereas both compounds showed lower values relative to the control compound, ranging between 1.3 and 1.4 nm. A slight peak was observed by compound 8 at 1.4 nm. Both compounds overlapped each other, indicating favorable stability (Figure 3C).

Hydrogen Bonds Analysis

Hydrogen bonds play a vital role in simulation studies, often playing a role as anchors holding a drug in the preferred and correct orientation within the pocket. The HB (Hydrogen Bonds) plot (Figure 3D) represents higher variability in hydrogen bonding by the reference molecule. Peaks were observed at 3-4 bonds, particularly in the later stage of the trajectory, indicating less stability. The most consistent hydrogen bonding interactions were recorded for compound 8, typically maintained between 1 and 2 and increased to 3. However, 1-2 bonds are observed for compound 10 appears frequently less to compound 8.

DISCUSSION

Mouse Double Minute 2 is a critical E3 ubiquitin ligase that acts as a primary negative regulator of the p53 tumor suppressor. When the level of MDM2 increases, it likely blocks p53 activity, which is commonly found in several cancers occurred worldwide. The phenomenon makes MDM2 a hallmark for cancers (Lin et al., 2024; Wang et al., 2024). Therefore, the urgent need is to develop a potent small molecule that could terminate the upregulation effectively in silent wild-type p53. Looking for a small molecule that can disrupt the interaction is a vital strategy to restoring natural tumor suppressive function and inhibiting drug resistance. Our research was primarily focused on finding a small molecular inhibitor. This utilized a multi-stage computational approach to identify the best lead compound. The known inhibitor of MDM2, Mi-63, attached to the MDM2 protein, was taken as a reference molecule. Initially, the library screening and molecular docking interaction studies were performed within the Mcule platform. The screening resulted in 100 compounds, followed by Lipinski’s rule. The docking energies recorded for the control compound were -7.8 kcal/mol. For refining of the compound’s, screened compounds were checked for any structural toxicity by the Toxicity checker available in the Mcule platform. The structure-based toxic compounds were eliminated. Afterwards, further approaches were applied, such as biological activity prediction, cell lines prediction, LD50 predictions, possible side effects, and pharmacokinetics profiling (Tables 1-6).

The integrated analysis was compared carefully with the inhibitor compound. The analysis highlights two compounds, compound 8 with IUPAC 2-benzamido-N-cyclohexylbenzamide (MCULE-4592638188-0-189) and compound 10 (MCULE-8527570511) with IUPAC 3-amino-6-thiophen-2-yl-4-(trifluoromethyl)thieno[2,3-b]pyridin-2-yl]-(2,4-dimethylphenyl)methanone respectively. These molecules revealed favorable results in all the parameters. The screening of PASS predictions revealed high specificity for STAT3 and HSP-27, which are linked to MDM2 stabilization and p53 suppression. Followed by CLC-pred analysis confirmed that these compounds possess targeted cytotoxicity potential against breast and lung cancer cell lines, such as MDA-MB. The LD50 result relies on classes IV and V, which are considered a safe profile supported by fewer possible side effects analyzed by AdverPred analysis compared to the control, exhibiting higher LD50 values. Moreover, drug likeness profiles demonstrated several supporting parameters, including Caco2 permeability and low systemic clearance. The detailed analysis of the binding modes is visualized through 3D surface mapping, 2D diagram, and 3D diagram. On a favorable note, the interaction diagram revealed significant reliance on a conserved set of amino acid residues. The lead candidates and the control share critical interactions with VAL93, serving as the primary anchor. Compound 8 demonstrated a unique interaction with LEU54 via a conventional hydrogen bond, proving stable anchoring within the pocket. Also, there was a remarkable overlap in hydrophobic residues LEU54, ILE61, MET62, TYR67, VAL75, ILE99, and TYR100 that appear consistently forming deep hydrophobic cleft which were detected, which is essential for stabilization of the ligands. Residues like GLY 58, ALA21, and GLN24 in van der Waals interactions suggest a high degree of shape complementarity (Figures 1A-C). All these results worked as a supporting pillar in synthesizing the final lead candidate.

At last, the molecular dynamics studies were conducted at 100 ns, providing atomistic proof of the structural superiority of these compounds. Both compounds maintained highly stable trajectories. However, the hydrogen bonds were slightly lower than those of the reference compound. Comparatively, the study revealed through all analyses and post-simulation studies, compound MCULE-4592638188-0-189 has gained overall better results that can be considered as a promising lead inhibitor candidate.

Although we have performed multiple approach analyses, many possible research parameters can still be applied.

CONCLUSION

The study successfully identified the potent lead candidate for the targeted inhibition of the MDM2/p53/STAT3 signaling axis. By integrating multiple in silico approaches, we demonstrated that the compounds offer a superior balance of pharmacological efficacy and safety compared to conventional antineoplastic agents. Furthermore, an optimized pharmacokinetics profile highlights their potential against tumors. STAT3 and heat shock proteins offered a sophisticated strategy to overcome MDM2-mediated p53 silencing, particularly in lung and breast malignancies. Broad-spectrum cell lines were also observed to have high IAP and favorable activity and inactivity scores. The compound also showed significantly higher binding energy, exhibiting minimal side effects. The higher structural stability and robustness observed in simulation trajectories provide strong evidence to maintain the inhibitory function under physiological conditions. The study was truly based on an in silico approach, and therefore experimental validations need to be checked to support the prediction in order to perform proper clinical trials of the identified drug and to seek reassurance before moving forward with practical use.

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Bakhsh, T. (2026). Structure-Guided Virtual Screening and Molecular Dynamics Simulation of Selective Small-Molecule Inhibitors Targeting E3 Ubiquitin Ligase MDM2 in Breast Cancer. International Journal of Pharmaceutical Investigation, 16(4), 1642–1656. https://doi.org/10.5530/ijpi.20260058