In-silico prediction of multi‑target mechanisms of Pinellia ternata phytochemicals in lung cancer: Evidence from a graph‑attention‑guided virtual screening and multi‑scale simulations
Abstract
Pinellia ternate has long been used to treat respiratory diseases, possessing potential anti-tumor activity and exhibiting multi-component, multi-target characteristics. This study prioritized lung cancer-related targets using the HERBGAT framework based on graph attention networks (GAT). High-quality PDB structures were retrieved, and diffusion-generative docking was performed to construct complex conformations and assess their confidence levels. Molecular dynamics simulations of representative complexes were conducted over 200 ns, and binding free energies were estimated using the MM/PBSA method. The pharmacokinetic characteristics of the bioactive compounds were evaluated using Swiss ADME and PreADMET computational tools, and density functional theory (DFT) analysis using ORCA software was combined to explore their electronic structure and properties. In this study, the potential targets of Pinellia ternata highly overlap with lung cancer pathological genes, with FGFR4, CDK2, JAK2, KDR, PAK4, PTK2 and PDGFRA being the core. Baicalein exhibits a conserved binding mode of "hinge hydrogen bond-aromatic interlayer-hydrophobic groove" at targets such as PTK2/KDR/JAK2. Energy decomposition indicates that van der Waals forces and nonpolar solvation are the main thermodynamic driving forces for complex formation. Density functional theory (DFT) analysis further reveals that the high electronic "softness" of baicalein and its sensitive response to the environment in terms of…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Quantum dots: synthesis, bioapplications, and toxicity
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- A Guide to In Silico Drug Design
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.