Author(s)

Yafigui Traoré^1,2*, Jean Missa Ehouman¹, Mamadou Guy-Richard Koné^1,3, Donourou Diabaté², Nahossé Ziao^1,3

¹Laboratory of Thermodynamics and Environmental Physico-Chemistry, Nangui Abrogoua University, Abidjan, Ivory Coast.
²Training and Research Unit Biological Sciences, University Peleforo Gon Coulibaly, Korhogo, Ivory Coast.
³Ivorian Research Group in Disease Modeling (GIR2M), Abidjan, Ivory Coast.

Anti-inflammatory activity of a series of tri-substituted pyrimidine derivatives was predicted using two Quantitative Structure-Activity Relationship models. These relationships were developed from molecular descriptors calculated using the DFT quantum chemistry method using the B3LYP/6-31G(d,p) level of theory and molecular lipophilicity. Thus, the four descriptors which are the dipole moment μ_D, the energy of the highest occupied molecular orbital E_HOMO, the isotropic polarizability α and the ACD/logP lipophilicity were selected for this purpose. The Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are respectively accredited with the following statistical indicators: R²=91.28%, R²_aj=89.11%, RMCE = 0.2831, R²_ext=86.50% and R²=98.22%, R²_aj=97.75%, RMCE = 0.1131, R²_ext=98.54%. The results obtained with the artificial neural network are better than those of the multiple linear regression. However, these results show that the two models developed have very good predictive performance of anti-inflammatory activity. These two models can therefore be used to predict anti-inflammatory activity of new similar pyrimidine derivatives.

Anti-Inflammatory Activity, Multiple Linear Regression, Artificial Neural Network, QSAR

Traoré, Y. , Ehouman, J. , Koné, M. , Diabaté, D. and Ziao, N. (2022) Prediction of Anti-Inflammatory Activity of a Series of Pyrimidine Derivatives, by Multiple Linear Regression and Artificial Neural Networks. Computational Chemistry, 10, 186-202. doi: 10.4236/cc.2022.104009.