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Developing a Hybrid Molecular Representation Combining Chemical Structure and MIR Spectral Data: A LogP Prediction

Kacper Tomaszewski1, Rafał Kurczab2

  • 1University of Applied Sciences in Tarnow, Faculty of Mathematics and Natural Sciences, Department of Chemistry, Mickiewicza 8, Tarnow 33-100, Poland.

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A new hybrid molecular fingerprint combines chemical structure and mid-infrared (MIR) spectral data. While not the most accurate for logP prediction, it offers an interpretable and efficient way to integrate spectral data into cheminformatics.

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Area of Science:

  • Cheminformatics
  • Spectroscopy
  • Quantitative Structure-Activity Relationship (QSAR)

Background:

  • Molecular fingerprints are crucial for representing chemical structures in computational modeling.
  • Integrating experimental data like mid-infrared (MIR) spectra into molecular representations can enhance predictive models.
  • Current methods for logP prediction often rely solely on structural information.

Purpose of the Study:

  • To develop and evaluate a novel hybrid molecular fingerprint combining structural and MIR spectral data.
  • To assess the performance of this fingerprint in a logP prediction task.
  • To explore the feasibility of incorporating MIR spectral information into Quantitative Structure-Activity Relationship (QSAR) workflows.

Main Methods:

  • A 101-bit binary hybrid molecular fingerprint was created, encoding both structural substructures and MIR absorption bands.
  • Support Vector Regression (SVR) was employed to predict logP values using the hybrid fingerprint.
  • Performance was benchmarked against traditional structure-based fingerprints and existing logP prediction tools.

Main Results:

  • The hybrid fingerprint achieved a Root Mean Square Error (RMSE) of 1.443 in logP prediction.
  • Traditional fingerprints (Morgan, MACCS) and descriptor-based models showed lower RMSEs.
  • Commercial and open-source logP tools also outperformed the hybrid fingerprint in this specific task.

Conclusions:

  • The proposed hybrid fingerprint, though modest in predictive accuracy for logP, provides a novel, interpretable, and computationally efficient method.
  • It successfully demonstrates the integration of MIR spectral data into cheminformatics modeling.
  • This work lays the groundwork for developing advanced spectrum-informed molecular representations for QSAR studies.