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QSPR study on Hydrophobicity of Pt(II) complexes with surface electrostatic potential-based descriptors. | LitMetric

QSPR study on Hydrophobicity of Pt(II) complexes with surface electrostatic potential-based descriptors.

J Mol Graph Model

School of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, BT9 5AG, UK.

Published: November 2022

AI Article Synopsis

  • The study investigates the hydrophobicity of Pt(II) complexes, which are significant in bioinorganic chemistry due to their antitumor properties, by developing predictive models using various machine learning techniques.* -
  • A five-parameter model based on frontier orbital energies and electrostatic potential descriptors was first created using multiple linear regression, followed by more sophisticated models such as support vector machines and random forests to improve prediction accuracy.* -
  • Validation of the models was thorough, showing reasonable performance with peak correlation values of 0.88 for the best Gaussian process model, while root mean squared errors for the test set were between 0.62 and 0.71, indicating that the models are satisfactory despite not being the best

Article Abstract

Pt(II) complexes play an important role in bioinorganic chemistry due to their antitumor activities. In the present study, we focused on building predictive models for the hydrophobicity of Pt(II) complexes. A five-parameter model, integrating frontier orbital energies (E, E) and descriptors derived from electrostatic potentials on molecular surface, was firstly constructed by using multiple linear regression (MLR) method. Mechanistic interpretations of the introduced descriptors were elucidated in terms of intermolecular interactions in the n-octanol/water partition system. Then, four up-to-date modeling methods, including support vector machine (SVM), least-squares support vector machine (LSSVM), random forest (RF) and Gaussian process (GP), were utilized to build the nonlinear models. Systematical validations including leave-one-out cross-validation, the validation for test set, as well as a very rigorous Monte Carlo cross-validation (MCCV) were performed to verify the reliability of the constructed models. The peak, median and integralR values of the best GP model are 0.88, 0.86 and 0.84, respectively. The root mean squared errors for the test set (RMSEP) of the MLR, SVM, LSSVM and GP models fall in the range of 0.62-0.71. Although they are not superior to prior models built with the use of a number of descriptors, the results are satisfactory. Applicability domain of the model was evaluated.

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Source
http://dx.doi.org/10.1016/j.jmgm.2022.108256DOI Listing

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