Precision Network Pharmacology Revealed by High-throughput Blind Docking: Deciphering the Multitarget Synergy of Taohe Chengqi Decoction against Constipation.

Abstract

Introduction: Constipation, prevalent globally (16-35%), is particularly common in the elderly, reducing quality of life and causing complications like intestinal obstruction. Current treatments (e.g., methylcellulose, polyethylene glycol, bisacodyl, prucalopride) provide temporary relief but have side effects, such as electrolyte disturbances and drug dependence. It is worth noting that the traditional Chinese herbal drug Taohe Chengqi Decoction (TCD) has demonstrated good laxative efficacy and safety advantages in long-term clinical practice. However, its systematic mechanism of action involving multiple components, multiple targets, and multiple pathways remains unclear.

Methods: This study innovatively integrates network pharmacology prediction, high-throughput blind molecular docking assisted by machine learning (DiffDock), and molecular dynamics simulation to construct a multidimensional mechanism model for TCD in treating constipation.

Results: A total of 115 active molecules were identified from TCD through high-throughput blind docking and network pharmacology, targeting 153 constipation-related proteins. After an 80 ns molecular dynamics simulation, six core components demonstrated stable binding with key targets like AKT1 and MAPK3.

Discussions: TCD exerts its therapeutic effects by intervening in key signaling pathways such as PI3K-Akt, IL-17 inflammation, T cell receptor, MAPK, and cAMP. It regulates the dynamic balance of intestinal epithelial cell proliferation/apoptosis, inhibits inflammatory cascades, and enhances intestinal motility and secretory functions to relieve constipation.

Conclusion: The innovative integration of machine learning-driven high-throughput blind docking technology with network pharmacology establishes a novel paradigm for traditional Chinese medicine research, significantly enhancing target prediction reliability through advanced structural validation.