Published March 21, 2026 | Version v3
Model Open

Multiscale simulation assisted discovery and optimization of spirooxindole MDM2 inhibitors

Authors/Creators

  • 1. ROR icon China Pharmaceutical University

Contributors

Data curator:

Project manager:

Project member:

  • 1. ROR icon China Pharmaceutical University

Description

Dataset Description
This repository contains the computational datasets and molecular design files associated with the study "Multiscale simulation assisted discovery and optimization of spirooxindole MDM2 inhibitors" by Xuchen Xu, Kexin Cheng, Zigui Kan* (Department of Chemistry, China Pharmaceutical University).
Background & Objectives The p53-MDM2 interaction is a critical target for anticancer drug development. This dataset documents a comprehensive in-silico workflow combining deep learning (Modof graph-remodeling network), pharmacophore modeling, 3D-QSAR, molecular docking, molecular dynamics (MD) simulations, and density functional theory (DFT) calculations for the optimization of spirooxindole-based MDM2 inhibitors.
Contents The dataset includes:
  1. Chemical Structures & Activity Data
    • 53 spirooxindole-based literature compounds with experimental pIC₅₀ values (training and test sets)
    • 22 newly designed molecules (N01-N22) generated via Modof deep learning network with predicted activities and physicochemical properties (MW, cLogP, TPSA, QED, SA scores)
    • SMILES strings and 3D optimized structures (OPLS-2005 force field)
  2. Pharmacophore & QSAR Models
    • Ligand-based pharmacophore model ADHHR_1 (features: H-bond donor, H-bond acceptor, hydrophobic regions, aromatic ring)
    • PLS-5 3D-QSAR model statistical metrics and contour maps
    • Fitness scores for all compounds mapped to pharmacophore hypotheses
  3. Molecular Dynamics Simulation Data
    • 100-ns MD trajectories (GROMACS 2019+/2021+) for MDM2 complexes with reference compound 47 and top candidates N11, N14, N15, N17
    • System setup: Amber99SB-ILDN (protein), GAFF (ligands), TIP3P water, 300K, 1 bar
    • Analysis outputs: RMSD, RMSF, Rg, SASA, PCA, free energy landscapes, DCCM matrices
    • MM-PBSA binding free energy decomposition data
  4. DFT Calculation Results
    • Frontier molecular orbital (FMO) data: HOMO-LUMO energies, energy gaps (E_gap), ionization potential, electron affinity, electronegativity, hardness, softness, electrophilicity index
    • Molecular electrostatic potential (MEP) maps
    • Optimization level: B3LYP/6-311G(d) using Gaussian 16
  5. Molecular Docking Data
    • AutoDock Vina docking scores (affinity energies)
    • Binding poses and interaction analyses (hydrogen bonds, hydrophobic interactions, π-π stacking)
    • Grid box parameters: 22.5×22.5×22.5 ų, center coordinates (39.817, 11.052, 27.178)
Key Findings
  • Four candidates (N11, N14, N15, N17) showed superior binding affinities (-9.2 to -8.8 kcal/mol) compared to reference compound 47 (-8.4 kcal/mol)
  • MM-PBSA calculations confirmed N11@MDM2 (-61.18 kJ/mol) and N15@MDM2 (-73.84 kJ/mol) as the most stable complexes
  • DFT analysis revealed N11, N14, and N15 possess smaller HOMO-LUMO gaps (4.24-4.80 eV) indicating enhanced chemical reactivity
File Formats
  • Molecular structures: .sdf, .mol2, .pdb
  • Trajectory files: .xtc/.trr (GROMACS)
  • Energy data: .xvg, .csv
  • SMILES and properties: .xlsx, .csv
  • Figures: .png, .pdf (pharmacophore alignments, contour maps, MD analysis plots)
Software Used Schrödinger Suite (PHASE, OPLS-2005), Modof (graph-remodeling network), AutoDock Vina, GROMACS, Gaussian 16, Multiwfn, Bio3D R package, PyMOL, LigPlot+
Associated Publication This dataset supports the manuscript: "Multiscale simulation assisted discovery and optimization of spirooxindole MDM2 inhibitors" (currently under submission/review).
Contact For questions regarding this dataset, please contact:
License CC BY 4.0 - This dataset is released under the Creative Commons Attribution 4.0 International license, permitting sharing and adaptation with appropriate credit to the original authors.

Files

Files (40.0 GB)

Additional details

Identifiers

DOI
10.5281/zenodo.18622729

Related works

Is supplemented by
Model: 10.5281/zenodo.18622729 (DOI)

Dates

Updated
2026-03-21

Software

Programming language
Python , R , Shell
Development Status
Active

References

  • [1]A. J. Levine, p53: 800 million years of evolution and 40 years of discovery, Nat. Rev. Cancer. 20 (2020) 471-480, https://doi.org/10.1038/s41568-020-0262-1. [2]O. Hassin, M. Oren, Drugging p53 in cancer: one protein, many targets, Nat. Rev. Drug Discov. 22 (2023) 127-144, https://doi.org/10.1038/s41573-022-00571-8. [3]M. M. J. Fallatah, F. V. Law, W. A. Chow, et al., Small-molecule correctors and stabilizers to target p53, Trends Pharmacol. Sci. 44 (2023) 274-289, https://doi.org/10.1016/j.tips.2023.02.007. [4]N. Raj, L. D. Attardi, The transactivation domains of the p53 protein, CSH Perspect Med. 7 (2017) a026047, https://doi.org/10.1021/bi1012996. [5]Y. Zhao, H. Yu and W. Hu, The regulation of MDM2 oncogene and its impact on human cancers, Acta Biochim Biophys Sin. 46 (2014) 180-189, https://doi.org/10.1093/abbs/gmt147. [6]J. Momand, A. Villegas, V. A. Belyi, The evolution of MDM2 family genes, Gene. 486 (2011) 23-30, https://doi.org/10.1016/j.gene.2011.06.030. [7]G. Lozano, R. M. d. O. Luna, MDM2 function, BBA-REV CANCER. 1377 (1998) M55-M59, https://doi.org/10.1016/S0304-419X(97)00037-1. [8]M. Mendoza, G. Mandani, J. Momand, The MDM2 gene family, Biomol. Concepts. 5 (2014) 9-19, https://doi.org/10.1515/bmc-2013-0027. [9]D. Michael and M. Oren, The p53 and Mdm2 families in cancer, Curr. Opin. Genet. Dev. 12 (2002) 53-59, https://doi.org/10.1016/S0959-437X(01)00264-7. [10]G. Lotfy, Y. M. A. Aziz, M. M. Said et al., Molecular hybridization design and synthesis of novel spirooxindole-based MDM2 inhibitors endowed with BCL2 signaling attenuation; a step towards the next generation p53 activators, Bioorg. Chem. 117 (2021) 105427, https://doi.org/10.1016/j.bioorg.2021.105427. [11]A. Barakat, M. S. Islam, H. M. Ghawas, et al., Design and synthesis of new substituted spirooxindoles as potential inhibitors of the MDM2–p53 interaction, Bioorg. Chem. 86 (2019) 598-608, https://doi.org/10.1016/j.bioorg.2019.01.053. [12]A. Aguilar, J. Lu, L. Liu, et al., Discovery of 4 ((3′R,4′S,5′R) 6″-Chloro-4′-(3-chloro-2-fluorophenyl)- 1′-ethyl-2″-oxodispiro[cyclohexane-1,2′-pyrrolidine-3′,3″-indoline]- 5′-carboxamido)bicyclo[2.2.2]octane-1-carboxylic Acid (AA-115/APG-115): a potent and orally active Murine Double Minute 2 (MDM2) Inhibitor in Clinical Development, J. Med. Chem. 60 (2017) 2819-2839, https://doi.org/10.1021/acs.jmedchem.6b01665 [13]D. Vemula, P. Jayasurya, V. Sushmitha, et al., CADD, AI and ML in drug discovery: A comprehensive review, Eur. J. Pharm. Sci. 181 (2023) 106324, https://doi.org/10.1016/j.ejps.2022.106324. [14]V. T. Sabe, T. Ntombela, L. A. Jhamba, et al., Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review, Eur. J. Med. Chem. 224 (2021) 113705, https://doi.org/10.1016/j.ejmech.2021.113705. [15]O. Iwaloye, P. O. Ottu, F. Olawale, et al., Computer-aided drug design in anti-cancer drug discovery: What have we learnt and what is the way forward?, Inform. Med. Unlocked. 41 (2023) 101332, https://doi.org/10.1016/j.imu.2023.101332. [16]S. Goyal, S. Grover, J. K. Dhanjal, et al., Group-based QSAR and molecular dynamics mechanistic analysis revealing the mode of action of novel piperidinone derived protein–protein inhibitors of p53-MDM2, J. Mol. Graph. Model. 51 (2014) 64–72, https://doi.org/10.1016/j.jmgm.2014.04.015. [17]C. Soriano-Correa, M. M. Vichi-Ramírez, E. E. Herrera-Valencia, et al., The role of ETFS amino acids on the stability and inhibition of p53-MDM2 complex of anticancer p53-derivatives peptides: Density functional theory and molecular docking studies, J. Mol. Graph. Model. 122 (2023) 108472, https://doi.org/10.1016/j.jmgm.2023.108472. [18]P. Csizmadia, MarvinSketch and MarvinView: molecule applets for the world wide web, proceedings of ecsoc & proceedings of ecso. (1999), https://doi.org/10.3390/ecsoc-3-01775. [19]https://www.rcsb.org/structure/1t4e [20]Delano W. L., The PyMol molecular graphics system, Proteins. 30 (2002) 442-454. [21]Schrödinger Release 2024-3: PIPER, Schrödinger, LLC, New York, NY, 2024. [22]S. L. Dixon, A. M. Smondyrev, E. H. Knoll, et al., PHASE: A new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening. 1. Methodology and preliminary results, J. Comput. Aided Mol. Des. 20 (2006) 647-671, https://doi.org/10.1007/s10822-006-9087-6. [23]Danishuddin, A. U. Khan, Descriptors and their selection methods in QSAR analysis: paradigm for drug design, Drug Discov. Today. 21 (2016) 1291-1302, https://doi.org/10.1016/j.drudis.2016.06.013. [24]Z. Chen, M. R. Min, S. Parthasarathy, et al., A deep generative model for molecule optimization via one fragment modification, Nat. Mach. Intell. 3 (2021) 1040–1049, https://doi.org/10.1038/s42256-021-00410-2. [25]K. Sunghwan, J. Chen, T. Cheng et al., PubChem 2023 update, Nucleic Acids Res. 51 (2023) 1373-1380, https://doi.org/10.1093/nar/gkac956. [26]G.M. Morris, R. Huey, W. Lindstrom, et al., AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility, J. Comput. Chem. 30 (2009) 2785-2791, https://doi.org/10.1002/jcc.21256. [27]O. Trott, A. J. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading, J. Comput. Chem. 31 (2010) 455-461, https://doi.org/10.1002/jcc.21334. [28]R. A. Laskowski, M. B. Swindells, LigPlot+: multiple ligand-protein interaction diagrams for drug discovery, J. Chem. Inf. Model. 51 (2011) 2778-2786, https://pubs.acs.org/doi/10.1021/ci200227u. [29]V. D. Spoel, GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit, Bioinformatics. 29 (2013) 845-854, https://doi.org/10.1093/bioinformatics/btt055. [30]Gaussian 16, Revision G16W, M. J. Frisch, et al. Gaussian, Inc., Wallingford CT, 2016. [31]B.J. Grant, L. Skjaerven, X. Yao, The Bio3D packages for structural bioinformatics, Protein Sci. 30 (2021) 20–30, https://doi.org/10.1002/pro.3923. [32]R. Kumari, R. Kumar, A. Lynn, et al., g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations, J. Chem. Inf. Model. 54 (2014) 1951–1962, https://doi.org/10.1021/ci500020m. [33]H. Chermette, Chemical reactivity indexes in density functional theory, J. Comput. Chem. 20 (1999) 129-154, https://doi.org/10.1002/(SICI)1096-987X(19990115)20. [34]R. Pal, P. K. Chattaraj, Chemical reactivity from a conceptual density functional theory perspective, J. Indian Chem. Soc. 98 (2021) 100008, https://doi.org/10.1016/j.jics.2021.100008. [35]V. Rossum, Guido. Python tutorial, Technical Report CS-R9526, Centrum voor Wiskunde en Informatica (CWI), Amsterdam. Available at https://www.python.org/doc/essays/blurb/.1995. [36]B. A. Alpay, M. Gosink, D. Aguiar, Evaluating molecular fingerprint-based models of drug side effects against a statistical control, Drug Discov. Today. 27(11) (2022) 103364, https://doi.org/10.1016/j.drudis.2022.103364. [37]Y. Du, X. Liu, N. Shah, et al., ChemSpace: interpretable and interactive chemical space exploration, ChemRxiv. 05 (2022) 3. [38]S.Y. Yang, Pharmacophore modeling and applications in drug discovery: challenges and recent advances, Drug Discov. Today. 15 (11-12) (2010) 444-450, https://doi.org/10.1016/j.drudis.2010.03.013. [39]H. Kubinyi, QSAR and 3D QSAR in drug design Part 2: applications and problems, Drug Discov. Today. 2(12) (1997) 538-546, https://doi.org/10.1016/S1359-6446(97)01084-2. [40]A. K. Padhi, M. Janežič, K. Y.J. Zhang, Chapter 26 - molecular dynamics simulations: principles, methods, and applications in protein conformational dynamics, Adv. Protein Mol. Struct. Biol. Methods. (2022) 439-454. [41]S. Singh, V.K. Singh, (2020). Molecular dynamics simulation: methods and application. in: Singh, D., Tripathi, T. (eds) frontiers in protein structure, function, and dynamics. Springer, Singapore, https://doi.org/10.1007/978-981-15-5530-5_9 [42]S. Sasidharan, V. Gosu, T. Tripathi, et al., Molecular dynamics simulation to study protein conformation and ligand interaction, In: Saudagar, P., Tripathi, T. (eds) protein folding dynamics and stability. Springer, Singapore. (2023). [43]S. Wei, C. L. Brooks III, A. T. Frank, A rapid solvent accessible surface area estimator for coarse grained molecular simulations, J. Comput. Chem. 38(15) (2017) 1270-1274, https://doi.org/10.1002/jcc.24709. [44]B. Kuhlman, P. Bradley, Advances in protein structure prediction and design, Nat. Rev. Mol. Cell. Biol. 20 (2019) 681–697, https://doi.org/10.1038/s41580-019-0163-x. [45]J. Lever, M. Krzywinski & Altman, N. Principal component analysis. Nat Methods 14, 641–642 (2017). https://doi.org/10.1038/nmeth.4346 [46]H. Yu, P. A. Dalby, Chapter Two - A beginner's guide to molecular dynamics simulations and the identification of cross-correlation networks for enzyme engineering, Methods Enzymol. 643 (2020) 15-49. https://doi.org/10.1016/bs.mie.2020.04.020. [47]L. Skjærven, X. Yao, G. Scarabelli, et al., Integrating protein structural dynamics and evolutionary analysis with Bio3D. BMC Bioinf. 15 (2014) 399, https://doi.org/10.1186/s12859-014-0399-6. [48]J. Yu, N. Q. Su, Weitao Yang, Describing chemical reactivity with frontier molecular orbitalets, JACS Au. 2(6) (2022) 1383–1394, https://doi.org/10.1021/jacsau.2c00085. [49]T. Lu, F. Chen, Multiwfn: A multifunctional wavefunction analyzer, J. Comput. Chem. 33 (2012) 580-592, https://doi.org/10.1002/jcc.22885. [50]T. Lu, F. Chen, Quantitative analysis of molecular surface based onimproved Marching Tetrahedra algorithm, J Mol. Graph. Model. 38 (2012) 314-323, https://doi.org/10.1016/j.jmgm.2012.07.004. [51]T. Lu, F. Chen, Calculation of molecular orbital composition, Acta Chim. Sinica. 69 (2011) 2393-2406. [52]W. Humphrey, A. Dalke, Schulten, K., VMD: visual molecular dynamics, J. Mol. Graph. Model. 14 (1996) 33-38, https://doi.org/10.1016/0263-7855(96)00018-5. [53]D. Shin and Y. Jung, Molecular electrostatic potential as a general and versatile indicator for electronic substituent effects: statistical analysis and applications, Phys. Chem. Chem. Phys. 24 (2022) 25740-25752, https://doi.org/10.1039/D2CP03244A.