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MF-DCCA between molecular properties and aqueous solubility

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  • Chen, Hong
  • Zhu, Li
  • Jia, GuoZhu

Abstract

In this paper, we propose a framework, based on Multi-Fractal De-trended Fluctuation Cross-Correlation Analysis (MF-DCCA), to investigate the cross-correlation features between the molecular properties and aqueous solubility using the data set of Delaney-processed widely used for Deep Learning (DL). The molecular properties of Minimum Degree, Number of H-Bond Donors, Number of Rings, Number of Rotatable bonds and Polar Surface Area have a weak power-law cross-correlation with aqueous solubility; the Molecular Weight has a weak long-range cross-correlation. Obvious oscillation of long-range cross-correlation captures through a sliding window approach between molecular properties and aqueous solubility based on the pseudo-time series. Experimental comparison of time-varying cross-correlation widths shows that window widths are more robust to time-varying Hurst exponent. This work can provide a reference for reducing the data dimension for neural network molecular property prediction tasks and help in high-accuracy predicting molecular aqueous solubility on DL.

Suggested Citation

  • Chen, Hong & Zhu, Li & Jia, GuoZhu, 2020. "MF-DCCA between molecular properties and aqueous solubility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    • Handle: RePEc:eee:phsmap:v:556:y:2020:i:c:s0378437120303502
    DOI: 10.1016/j.physa.2020.124708
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