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Published February 15, 2026 | Version v2
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Deep-Learning-Driven Optimization of Spirooxindole Inhibitors Targeting p53–MDM2 Interaction: Multiscale In-Silico Validation

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 "Deep-Learning-Driven Optimization of Spirooxindole Inhibitors Targeting p53-MDM2 Interaction: Multiscale In-Silico Validation" 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: "Deep-Learning-Driven Optimization of Spirooxindole Inhibitors Targeting p53-MDM2 Interaction: Multiscale In-Silico Validation" (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)

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Additional details

Identifiers

DOI
10.5281/zenodo.18622729

Related works

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

Dates

Updated
2026-02-15

Software

Programming language
Python , R , Shell
Development Status
Active

References

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