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<p>1,3,4-Oxadiazole derivatives have demonstrated significant efficacy in protecting rice crops against bacterial pathogens, particularly <i>Xanthomonas oryzae</i> (<i>Xo</i>). Modern molecular modeling strategies are cutting-edge methods for designing chemicals that target enzymes responsible for plant diseases. In this study, we present quantitative structure–activity relationship (QSAR) analyses of a series of 1,3,4-oxadiazoles, evaluating their activity against <i>Xo</i> pv. <i>oryzicola</i> (<i>Xoc</i>) and <i>Xo</i> pv. <i>oryzae</i> (<i>Xoo</i>). Our goal is to identify candidate derivatives capable of targeting both bacteria simultaneously. Using a partial least squares model, we achieved moderate predictability; however, support vector machine regression proved more robust and reliable in estimating the antibacterial potential of the 1,3,4-oxadiazoles. The novel candidates exhibited superior calculated bioactivities compared to the existing library, with compounds containing a 2,4-dichlorophenyl substituent showing significant dual antibacterial activity against <i>Xoc</i> and <i>Xoo</i>. Docking studies and molecular dynamics simulations corroborated the QSAR findings, highlighting the chemical features critical for ligand–enzyme affinities.</p>

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QSAR and machine learning-driven proposition of novel 1,3,4-oxadiazoles and structure-based studies of their antibacterial activities against Xanthomonas oryzae

  • 期刊论文

  • 发表日期：

    2025年2月10日

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  • Ingrid V. P. de Faria,
  • Antônio Pedro Lemos de Mesquita,
  • Elaine F. F. da Cunha,
  • Matheus P. Freitas

摘要

1,3,4-Oxadiazole derivatives have demonstrated significant efficacy in protecting rice crops against bacterial pathogens, particularly Xanthomonas oryzae (Xo). Modern molecular modeling strategies are cutting-edge methods for designing chemicals that target enzymes responsible for plant diseases. In this study, we present quantitative structure–activity relationship (QSAR) analyses of a series of 1,3,4-oxadiazoles, evaluating their activity against Xo pv. oryzicola (Xoc) and Xo pv. oryzae (Xoo). Our goal is to identify candidate derivatives capable of targeting both bacteria simultaneously. Using a partial least squares model, we achieved moderate predictability; however, support vector machine regression proved more robust and reliable in estimating the antibacterial potential of the 1,3,4-oxadiazoles. The novel candidates exhibited superior calculated bioactivities compared to the existing library, with compounds containing a 2,4-dichlorophenyl substituent showing significant dual antibacterial activity against Xoc and Xoo. Docking studies and molecular dynamics simulations corroborated the QSAR findings, highlighting the chemical features critical for ligand–enzyme affinities.