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<p>Alzheimer’s disease (AD) is a progressive neurodegenerative disorder primarily linked to the abnormal accumulation of beta-amyloid plaques, facilitated by the beta-secretase-1 (BACE-1) enzyme. Inhibiting BACE-1 has emerged as a promising approach for developing anti-Alzheimer’s therapeutics. Computational drug design is gaining recognition as a rapid and cost-effective strategy for identifying lead compounds in a shorter timeframe. This study focuses on the identification of natural BACE-1 inhibitors using advanced receptor-based virtual screening, molecular docking, and dynamic simulation techniques. E-pharmacophore modeling generated a hypothesis, ADHRR, characterized by one acceptor (A2), one donor (D5), one hydrophobic region (H7), and two aromatic rings (R11, R12). Furthermore, virtual screening of the natural product atlas was performed using the generated e-pharmacophore hypothesis. The top 572 hits with fit values above 1.8 were subjected to high-throughput virtual screening (HTVS) docking, from which the top 100 were further analyzed using standard precision (SP) docking. Subsequently, the top 9 hits underwent extra precision (XP) docking. The most promising candidates were evaluated through ADMET analysis. Among them, NAP036351 exhibited the highest potency, with a superior docking score of −11.21 and a binding free energy of −38.5396&#xa0;kcal/mol. To further assess the stability and binding efficiency of the lead compound, molecular dynamics (MD) simulations were conducted for 200&#xa0;ns, followed by MM/GBSA analysis. The results demonstrated significant stability and favorable binding free energy throughout the simulation. This study successfully identified key natural compounds with strong BACE-1 inhibitory potential, providing valuable insights into their mechanism of action and suitability as drug candidates.</p> Graphical abstract <p></p>

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Identification of natural BACE-1 inhibitors for Alzheimer's disease treatment through Advanced receptor-based virtual screening, molecular docking, and dynamic simulations: an in-silico approach

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Shweta Gandhi,
  • Swapnil Goyal,
  • Nandan Dixit,
  • Saumya K. Patel,
  • Palmi Modi

摘要

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder primarily linked to the abnormal accumulation of beta-amyloid plaques, facilitated by the beta-secretase-1 (BACE-1) enzyme. Inhibiting BACE-1 has emerged as a promising approach for developing anti-Alzheimer’s therapeutics. Computational drug design is gaining recognition as a rapid and cost-effective strategy for identifying lead compounds in a shorter timeframe. This study focuses on the identification of natural BACE-1 inhibitors using advanced receptor-based virtual screening, molecular docking, and dynamic simulation techniques. E-pharmacophore modeling generated a hypothesis, ADHRR, characterized by one acceptor (A2), one donor (D5), one hydrophobic region (H7), and two aromatic rings (R11, R12). Furthermore, virtual screening of the natural product atlas was performed using the generated e-pharmacophore hypothesis. The top 572 hits with fit values above 1.8 were subjected to high-throughput virtual screening (HTVS) docking, from which the top 100 were further analyzed using standard precision (SP) docking. Subsequently, the top 9 hits underwent extra precision (XP) docking. The most promising candidates were evaluated through ADMET analysis. Among them, NAP036351 exhibited the highest potency, with a superior docking score of −11.21 and a binding free energy of −38.5396 kcal/mol. To further assess the stability and binding efficiency of the lead compound, molecular dynamics (MD) simulations were conducted for 200 ns, followed by MM/GBSA analysis. The results demonstrated significant stability and favorable binding free energy throughout the simulation. This study successfully identified key natural compounds with strong BACE-1 inhibitory potential, providing valuable insights into their mechanism of action and suitability as drug candidates.

Graphical abstract