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    Artificial Neural Network and Multiple RegresAnalysis Applied to 2D-QSAR Studies: the Caof Imidazolidine-2,4-dione as PTP1B Inhibito
    (Department of Science and Technology, 2024) Aggoun, Siham; Belaidi, Salah; Ghamri, Meriam; Kerassa, Aicha; Cinar, Mehmet; Yamari, Imane; Abchir, Oussama
    In this investigation, we undertook a comprehensive quantitative structure-activity relationship (QSAR) analysis using both modern artificial neural network (ANN) methods and classical multiple linear regression methods (MLR). Our primary focus was on revealing the intricate connection between antidiabetic activity and the molecular structure of thirty-nine imidazolidine-2,4-dione derivatives. The B3LYP hybrid functional and 6-31G (d) basis set computed electronic properties at the quantum level. Rigorous benchmarking against experimental data validated the reliability of our quantum theory approach. Our statistical model effectively predicted activities closely aligned with experimental antidiabetic activities, quantified by the IC50 values. They also revealed a significant superiority of the ANN architecture (6-4-1) over the MLR method. This study stands as a meaningful contribution to the field, providing valuable insights for designing new antidiabetic drugs, particularly as potential inhibitors of tyrosine phosphate 1B (PTP1B). The explicit articulation of our primary aim and emphasis on the study’s significance underscore its potential impact on advancing drug development within the realm of antidiabetic therapeutics. © 2024, Department of Science and Technology. All rights reserved.