Advanced QSPR modeling of profens using machine learning and molecular descriptors for NSAID analysis.

Journal: Scientific reports
Published Date:

Abstract

In this paper, we present a predictive model based on artificial neural network (ANN) to evaluate principal physicochemical properties of a set of anti-inflammatory drugs based on chosen topological indices. The molecular descriptors were calculated from molecular structures and employed as the inputs to the ANN model. Normalization of the feature set was carried out before training to maintain convergence and stability of the model. The ANN exhibited excellent predictive ability based on a [Formula: see text] value of 0.94 and a mean squared error (MSE) of 0.0087 on the test set. The chemical structure data used were mainly retrieved from ChemSpider. The method showcases the promise of machine learning models to facilitate better virtual screening and assist in rational drug design by making accurate predictions of properties.

Authors

  • W Eltayeb Ahmed
    Department of Mathematics and Statistics, College of Science, Imam Muhammad Ibn Saud Islamic University (IMSIU), PO Box 90950, Riyadh, Saudi Arabia.
  • Muhammad Farhan Hanif
    Department of Mathematics and Statistics, The University of Lahore, Lahore Campus, Pakistan. Electronic address: farhanlums@gmail.com.
  • Muhammad Kamran Siddiqui
    Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan. Electronic address: kamransiddiqui75@gmail.com.
  • Brima Gegbe
    Department of Mathematics and Statistics, Njala University, Freetown, Sierra Leone. bgegbe@njala.edu.sl.