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<p>Pipestelides are a class of natural products with a unique chemical structure that has attracted significant interest from the scientific community due to their potential as pharmaceutical drugs. In this study, we employed Computational Peptidology and Conceptual Density Functional Theory (CDFT) to investigate the chemical reactivity and bioactivity of three marine cyclopeptides: Pipestelides A, B, and C. Our analysis identified several chemical and pharmacological properties of these cyclopeptides that suggest their potential as drug candidates, particularly as anti-cancer, and anti-inflammatory agents. This conclusion was supported by the estimation of CDFT reactivity descriptors and local hypersoftness graphical representations. Additionally, we validated the KID technique and the MN12SX/def2-SVP computational method while identifying parameters relevant to future QSAR studies, such as the pKa and LogP values of the peptides. These insights were derived from calculated CDFT descriptors, predicted biological targets, and an evaluation of ADMET parameters related to the pharmacokinetics of these marine cyclopeptides. Our findings provide valuable insights into the therapeutic potential of pipestelides and underscore the importance of computational methods in modern drug discovery.</p>

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Unveiling Pipestelides A–C: conceptual density functional theory-based computational peptidology approach to their chemical and pharmacological potential

  • 期刊论文

  • 发表日期：

    2025年7月4日

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  • Norma Flores-Holguín,
  • Juan Frau,
  • Daniel Glossman-Mitnik

摘要

Pipestelides are a class of natural products with a unique chemical structure that has attracted significant interest from the scientific community due to their potential as pharmaceutical drugs. In this study, we employed Computational Peptidology and Conceptual Density Functional Theory (CDFT) to investigate the chemical reactivity and bioactivity of three marine cyclopeptides: Pipestelides A, B, and C. Our analysis identified several chemical and pharmacological properties of these cyclopeptides that suggest their potential as drug candidates, particularly as anti-cancer, and anti-inflammatory agents. This conclusion was supported by the estimation of CDFT reactivity descriptors and local hypersoftness graphical representations. Additionally, we validated the KID technique and the MN12SX/def2-SVP computational method while identifying parameters relevant to future QSAR studies, such as the pKa and LogP values of the peptides. These insights were derived from calculated CDFT descriptors, predicted biological targets, and an evaluation of ADMET parameters related to the pharmacokinetics of these marine cyclopeptides. Our findings provide valuable insights into the therapeutic potential of pipestelides and underscore the importance of computational methods in modern drug discovery.