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How quantum-chemical geometry optimization level affects classical 3D descriptors and QSAR performance: a comparative

Jianmin Li1, Rongling Gu2, Shijie Du1

  • 1College of Material and Chemical Engineering, Tongren University, Tongren, 554300, Guizhou, PR China.

Journal of Cheminformatics
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

Higher-level quantum-chemical (QM) geometry optimization for molecular structures offers minimal practical gains in quantitative structure-activity relationship (QSAR) performance. Despite varying descriptor values, advanced QM methods do not significantly improve predictive accuracy for anticancer drug discovery compared to lower-level methods.

Keywords:
3D molecular descriptorsComputational efficiencyDensity functional theoryQuantitative structure-activity relationshipQuantum chemical methods

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Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Quantitative Structure-Activity Relationship (QSAR) Modeling

Background:

  • Accurate three-dimensional (3D) molecular structures are crucial for quantitative structure-activity relationship (QSAR) modeling.
  • The impact of quantum-chemical (QM) geometry optimization levels on classical 3D descriptors and QSAR performance remains unclear.
  • Classical conformation-dependent 3D descriptors (Dragon 3D) are widely used in QSAR.

Purpose of the Study:

  • To benchmark eight QM geometry optimization protocols for their effect on 3D descriptors and QSAR performance.
  • To assess the practical benefits of increasing QM theory level for QSAR modeling.
  • To propose a framework for pragmatic method selection in QSAR studies.

Main Methods:

  • Benchmarking of eight QM geometry optimization protocols (HF/STO-3G to DFT and composite methods).
  • Evaluation across three anticancer activity datasets and ten machine-learning classifiers.
  • Descriptor-level analyses (deviation, correlation, similarity) and QSAR performance metrics (balanced accuracy).

Main Results:

  • High-accuracy QM protocols yield consistent descriptor spaces, while lower-level methods introduce variability but maintain molecular rankings.
  • QSAR performance is only weakly affected by QM level, with mean balanced accuracies clustering tightly (0.852-0.871).
  • B3LYP/3-21G showed the highest mean balanced accuracy, but performance differences were marginal (<1-2%) and statistically insignificant relative to computational cost.

Conclusions:

  • Increasing QM geometry optimization level offers limited practical benefit for classical 3D descriptor fidelity and QSAR predictive accuracy.
  • QM upgrades primarily alter descriptor values without commensurate gains in predictive power, especially considering computational costs.
  • A proposed Absolute Efficiency Ratio (AER) framework aids pragmatic method selection by balancing performance and efficiency.