Discovery of novel VEGFR2 inhibitors against non-small cell lung cancer based on fingerprint-enhanced graph attention

Zixiao Wang1, Lili Sun2, Yu Xu3

  • 1Department of Pharmacy, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, China. zixiaowang1112@foxmail.com.

PubMed

Insights

A novel AI model accurately predicts anti-non-small cell lung cancer (NSCLC) drug activity, identifying promising VEGFR2 inhibitors. This approach accelerates the discovery of effective NSCLC treatments with dual target and cellular activity.

Area of Science:

  • Computational chemistry and drug discovery
  • Artificial intelligence in pharmacology
  • Oncology and cancer therapeutics

Background:

  • Targeting vascular endothelial growth factor receptor 2 (VEGFR2) shows promise for solid tumors like non-small cell lung cancer (NSCLC).
  • Existing VEGFR2 inhibitors face challenges including off-target effects and limited efficacy.
  • Developing novel anti-NSCLC drugs requires innovative approaches to overcome these limitations.

Purpose of the Study:

  • To develop a novel fingerprint-enhanced graph attention convolutional network (FnGATGCN) model for predicting anti-NSCLC drug activity.
  • To integrate molecular graph and fingerprint features using a multimodal fusion strategy for enhanced prediction.
  • To identify novel drug candidates with dual activity against VEGFR2 and NSCLC cell lines.

Main Methods:

  • Developed and validated a fingerprint-enhanced graph attention convolutional network (FnGATGCN) model.
  • Employed multimodal fusion integrating molecular graph and fingerprint feature extraction.
  • Screened the ZINC database, followed by molecular docking, dynamics simulations, and free energy calculations.
  • Assessed inhibitory activity against VEGFR2 and proliferation in NSCLC cell lines (A549, NCI-H23, NCI-H460).

Main Results:

  • The FnGATGCN model demonstrated high accuracy and stability in predicting drug activity.
  • Eleven potential active molecules were identified, with 11 showing stable binding to VEGFR2.
  • Compound Z-3 exhibited potent VEGFR2 inhibition (IC50 = 0.88 μM) and anti-proliferative activity against A549 cells (IC50 = 4.23 ± 0.45 μM).
  • Compounds Z1-Z3 and Z6 showed significant antiproliferative activity against multiple NSCLC cell lines with low toxicity to normal cells.

Conclusions:

  • The FnGATGCN model provides an efficient and interpretable approach for anti-NSCLC drug discovery.
  • The identified compounds, particularly Z-3, Z1-Z3, and Z6, are promising candidates for novel VEGFR2 inhibitors against NSCLC.
  • This study offers a hybrid strategy combining target-based and phenotype-based screening for rapid identification of effective anti-cancer agents.