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<p>A combined computational workflow featuring response surface methodology, a hybrid teaching-learning-based optimization (TLBO)-ANN model, and Support Vector Regression (SVR) was used to design and optimize novel Schiff bases 1,2-bis(furan-2-ylmethylene)hydrazine (A<sub>1</sub>), 1,2-bis(furan-2-ylethylene)hydrazine (A<sub>2</sub>), 1,2-bis(thiophen-2-ylmethylene)hydrazine (A<sub>3</sub>), and 1,2-bis(thiophen-2-ylethylene)hydrazine (A<sub>4</sub>). The TLBO-ANN model achieved high predictive accuracy (R<sup>2</sup> = 0.98) for synthesis yield. However, the ANN model produced the best yield prediction accuracy, as confirmed by experiments (yield: 91%). The compounds were characterized by NMR, IR, UV-visible spectroscopy, mass spectrometry, and cyclic voltammetry, which reveal their structure, optical, and electrochemical properties with wide applications. Density Functional Theory (DFT) and molecular docking simulations elucidated molecular properties and binding affinities to antibacterial targets (– 7.4 to − 8.2&#xa0;kcal/mol). ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) predictions were also conducted to evaluate the compounds’ pharmacokinetic behavior. This integrated computational approach yielded compounds with potent antibacterial activity, with minimum inhibitory concentrations as low as 0.070&#xa0;mg/mL, validating the models’ utility in rational drug design.</p>

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Integrative design and optimization of bioactive schiff bases using computational intelligence and molecular modeling

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Safa Elgharbi,
  • Kamel Landolsi,
  • Fraj Echouchene,
  • Sonia Taamalli,
  • Florent Louis,
  • Wissal Rouihem,
  • Abdelkarim Mahdhi,
  • Moncef Msaddek,
  • Manal Alruwaili,
  • Mansour Alhabradi,
  • Hafedh Belmabrouk

摘要

A combined computational workflow featuring response surface methodology, a hybrid teaching-learning-based optimization (TLBO)-ANN model, and Support Vector Regression (SVR) was used to design and optimize novel Schiff bases 1,2-bis(furan-2-ylmethylene)hydrazine (A1), 1,2-bis(furan-2-ylethylene)hydrazine (A2), 1,2-bis(thiophen-2-ylmethylene)hydrazine (A3), and 1,2-bis(thiophen-2-ylethylene)hydrazine (A4). The TLBO-ANN model achieved high predictive accuracy (R2 = 0.98) for synthesis yield. However, the ANN model produced the best yield prediction accuracy, as confirmed by experiments (yield: 91%). The compounds were characterized by NMR, IR, UV-visible spectroscopy, mass spectrometry, and cyclic voltammetry, which reveal their structure, optical, and electrochemical properties with wide applications. Density Functional Theory (DFT) and molecular docking simulations elucidated molecular properties and binding affinities to antibacterial targets (– 7.4 to − 8.2 kcal/mol). ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) predictions were also conducted to evaluate the compounds’ pharmacokinetic behavior. This integrated computational approach yielded compounds with potent antibacterial activity, with minimum inhibitory concentrations as low as 0.070 mg/mL, validating the models’ utility in rational drug design.