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A Simple Bioassay for the Evaluation of Vascular Endothelial Growth Factors
Published on: March 15, 2016
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.
Abstract:
Despite the proven inhibitory effects of drugs targeting vascular endothelial growth factor receptor 2 (VEGFR2) on solid tumors, including non-small cell lung cancer (NSCLC), the development of anti-NSCLC drugs solely targeting VEGFR2 still faces risks such as off-target effects and limited efficacy. This study aims to develop a novel fingerprint-enhanced graph attention convolutional network (FnGATGCN) model for predicting the activity of anti-NSCLC drugs. Employing a multimodal fusion strategy, the model integrates a feature extraction layer that comprises molecular graph feature extraction and molecular fingerprint feature extraction. The performance evaluation results indicate that the model exhibits high accuracy and stability in predicting activity. Moreover, we explored the relationship between molecular features and biological activity through visualization analysis, thus improving the interpretability of the approach. Utilizing this model, we screened the ZINC database and conducted high-precision molecular docking, leading to the identification of 11 potential active molecules. Subsequently, molecular dynamics simulations and free energy calculations were performed. The results demonstrate that all 11 aforementioned molecules can stably bind to VEGFR2 under dynamic conditions. Among the short-listed compounds, the top six exhibited satisfactory inhibitory activity against VEGFR2 and A549 cells. Especially, compound Z-3 displayed VEGFR2 inhibitory with IC50 values of 0.88 μM, and anti-proliferative activity against A549 cells with IC50 values of 4.23 ± 0.45 μM. This approach combines the advantages of target-based and phenotype-based screening, facilitating the rapid and efficient identification of candidate compounds with dual activity against VEGFR2 and A549 cell lines. It provides new insights and methods for the development of anti-NSCLC drugs. Furthermore, further biological activity tests revealed that Z1-Z3 and Z6 manifested relatively strong antiproliferative activities against NCI-H23 and NCI-H460, and relatively low toxicity towards GES-1. The hit compounds were promising candidates for the further development of novel VEGFR2 inhibitors against NSCLC.
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.
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