Work

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

Year: 2025

Type: article

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.

Source: Scientific Reports

DOI: 10.1038/s41598-025-09878-z

Authors: W. Eltayeb Ahmed, Muhammad Farhan Hanif, Muhammad Kamran Siddiqui, Brima Gegbe

Institutions: Imam Mohammad ibn Saud Islamic University, University of Lahore, COMSATS University Islamabad, Njala University

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,690