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<p>Hypercholesterolemia is a pivotal risk factor for cardiovascular diseases, and its effective management is critical to reducing cardiovascular event incidence. The protein–protein interaction (PPI) between PCSK9 and low-density lipoprotein receptor (LDLR) drives hyperlipidemia progression, rendering PCSK9 a key therapeutic target for coronary heart disease (CHD). Despite the promising prospect of PCSK9 inhibitors in CHD treatment, clinically approved agents of this class remain limited. To address this gap, this study first established a pharmacophore model to screen 4495 terpenoid compounds, identifying 14 candidates via molecular docking. Subsequent fragment substitution generated 99 derivatives with improved ADMET properties, and 6 representative compounds were structurally generated and optimized(based on artificial intelligence techniques), with their activity evaluated using the LogitBoost model (AUC = 0.864). Further quantum chemical calculations, conformation screening, 200&#xa0;ns molecular dynamics simulations and free energy analyses demonstrated that Molecule3 (docking score: 123.629&#xa0;kcal/mol) binds PCSK9 with higher affinity and forms more stable complexes than the positive control Brazilin. In summary, computational evidence based on the integration of traditional CADD and AI technologies suggests that Molecule3 has the potential to be a PCSK9 inhibitor, identifying it as a high-priority lead candidate for further development.</p>

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Artificial intelligence-driven rational design and optimization of a potent terpenoid-derived PCSK9 inhibitor

  • 期刊论文

  • 发表日期：

    2026年5月9日

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  • Heng Jiang,
  • ZiYan Huang,
  • HongHui Hu,
  • JiaHui Tu,
  • LianXiang Luo

摘要

Hypercholesterolemia is a pivotal risk factor for cardiovascular diseases, and its effective management is critical to reducing cardiovascular event incidence. The protein–protein interaction (PPI) between PCSK9 and low-density lipoprotein receptor (LDLR) drives hyperlipidemia progression, rendering PCSK9 a key therapeutic target for coronary heart disease (CHD). Despite the promising prospect of PCSK9 inhibitors in CHD treatment, clinically approved agents of this class remain limited. To address this gap, this study first established a pharmacophore model to screen 4495 terpenoid compounds, identifying 14 candidates via molecular docking. Subsequent fragment substitution generated 99 derivatives with improved ADMET properties, and 6 representative compounds were structurally generated and optimized(based on artificial intelligence techniques), with their activity evaluated using the LogitBoost model (AUC = 0.864). Further quantum chemical calculations, conformation screening, 200 ns molecular dynamics simulations and free energy analyses demonstrated that Molecule3 (docking score: 123.629 kcal/mol) binds PCSK9 with higher affinity and forms more stable complexes than the positive control Brazilin. In summary, computational evidence based on the integration of traditional CADD and AI technologies suggests that Molecule3 has the potential to be a PCSK9 inhibitor, identifying it as a high-priority lead candidate for further development.