Work

Prediction of Surface Tension of Propane Derivatives Using QSPR Approach and Artificial Neural Networks

Year: 2025

Type: article

Abstract: : A Quantitative Structure-Activity Relation study (QSPR) was conducted to evaluate the surface tension of a series of 30 propane derivatives. The surface tension (TS) of these derivatives was correlated with a single calculated descriptor, namely (Mor13v).In the present work, multiple linear regression (MLR) and artificial neural network (ANN) techniques were used for QSPR studies of the tension surface of 30 propane derivatives.The results of MLR model were compared with those of the ANN model. the comparison showed that the R2 = 96.58%, s = 1.4178 of ANN were higher and lower, respectively, which illustrates that an ANN presents an excellent alternative for developing a QSPR model for the surface tension (ST) values of propane derivatives compared to MLR.

Source: Moroccan Journal of chemistry

DOI: 10.48317/imist.prsm/morjchem-v13i2.54664

Authors: Touam, S., NACER, Houria, Ziani, N., Lebrazi, N., Kertiou, N., Khibech, O., Abbaoui, Z.

Institutions: Larbi Ben M'hidi University of Oum El Bouaghi, Badji Mokhtar-Annaba University, Sidi Mohamed Ben Abdellah University, Mohamed I University, Euro-Mediterranean University of Fes

Language: English

Topic: Graph theory and applications

Subfield: Geometry and Topology

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W6891682413

FWCI: 3.55

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Cited by: 2

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