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Atomic-level-based AI topological descriptors for structure-property correlations.

Biye Ren1

  • 1Research Institute of Materials Science, South China University of Technology, Guangzhou 510640, P. R. China. renbiye@163.net

Journal of Chemical Information and Computer Sciences
|January 28, 2003
PubMed
Summary
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This study develops structure-boiling point models for sulfur compounds using multiple linear regression (MLR). The models accurately predict boiling points, highlighting the importance of molecular size and atomic interactions.

Area of Science:

  • Physical Chemistry
  • Computational Chemistry
  • Organic Chemistry

Background:

  • Accurate prediction of boiling points is crucial for understanding and utilizing organic compounds.
  • Quantitative Structure-Property Relationship (QSPR) models offer a powerful approach to predict chemical properties based on molecular structure.

Purpose of the Study:

  • To develop robust quantitative structure-property relationship (QSPR) models for predicting the boiling points of sulfur-containing organic compounds.
  • To identify key molecular descriptors that influence the boiling points of sulfides and thiols.
  • To validate the predictive power and reliability of the developed models.

Main Methods:

  • Multiple Linear Regression (MLR) analysis was employed to build the QSPR models.
  • The Xu index and atomic-level AI indices were utilized as molecular descriptors.

Related Experiment Videos

  • Models were developed for a dataset of 71 sulfur-containing compounds, including subsets of 45 sulfides and 26 thiols.
  • Leave-one-out cross-validation was performed for model validation.

    • Main Results:

    • High-quality QSPR models were successfully constructed with correlation coefficients (r) exceeding 0.997.
    • The best models achieved standard errors of 3.14°C (sulfides), 2.48°C (thiols), and 3.48°C (all compounds).
    • Molecular size was identified as the dominant factor, with atomic types and their interactions also contributing significantly to boiling points.

    Conclusions:

    • The developed MLR-based QSPR models provide accurate and reliable predictions of boiling points for sulfur-containing organic compounds.
    • Both molecular size and specific atomic interactions play critical roles in determining boiling points.
    • The models are statistically significant and validated, demonstrating their utility in chemical research and development.