⚡ Quick Summary
This study presents a novel approach to discovering VEGFR-2 inhibitors using a combination of Junction Tree Variational Autoencoder and advanced optimization techniques. The research successfully identified 493 new small molecules with promising inhibitory potential against VEGFR-2, a critical target in cancer therapy.
🔍 Key Details
- 📊 Dataset: Initial dataset and nine FDA-approved drugs targeting VEGFR-2
- 🧩 Techniques used: Junction Tree Variational Autoencoder, Bayesian Optimization, Gradient Ascent
- 🏆 Performance metrics: QSAR model R2 of 0.792 ± 0.075 (internal), 0.859 (external)
- 🔬 Molecular docking: ROC-AUC value of 0.710, binding activity threshold of -7.90 kcal/mol
🔑 Key Takeaways
- 💡 VEGFR-2 is a significant target in the development of anticancer medications.
- 🤖 Deep learning techniques were effectively utilized to explore chemical space for potential inhibitors.
- 🔍 493 novel small molecules were identified as potential VEGFR-2 inhibitors.
- 📈 QSAR models demonstrated strong predictive capabilities for the inhibitory potential of the generated molecules.
- 🧬 Molecular docking confirmed the binding affinity of the shortlisted molecules to VEGFR-2.
- 🌟 This research highlights the potential of AI in drug discovery, particularly in oncology.
- 🔗 Study published in ACS Omega, showcasing the integration of machine learning in medicinal chemistry.
📚 Background
The vascular endothelial growth factor receptor 2 (VEGFR-2) plays a crucial role in angiogenesis and is a prominent target for anticancer therapies. The ongoing need for effective VEGFR-2 inhibitors has led researchers to explore innovative methodologies, including the application of deep learning and machine learning techniques, to accelerate the drug discovery process.
🗒️ Study
This study utilized a combination of Junction Tree Variational Autoencoder and optimization strategies to navigate the vast chemical space for identifying small molecules that could inhibit VEGFR-2. The researchers employed both local Bayesian optimization and gradient ascent techniques to refine their search, ultimately generating a diverse set of 493 small molecules.
📈 Results
The results from the study were promising, with the QSAR model achieving coefficients of determination (R2) of 0.792 ± 0.075 for internal validation and 0.859 for external validation. Additionally, the molecular docking studies yielded a ROC-AUC value of 0.710, indicating a reliable predictive performance for the binding activity of the newly generated molecules.
🌍 Impact and Implications
The findings from this research have significant implications for the field of drug discovery, particularly in the context of cancer treatment. By leveraging advanced computational techniques, researchers can expedite the identification of effective VEGFR-2 inhibitors, potentially leading to the development of new therapeutic options. This study exemplifies how artificial intelligence can enhance the efficiency and effectiveness of drug discovery processes.
🔮 Conclusion
This study underscores the transformative potential of integrating machine learning with traditional drug discovery methods. The identification of 493 novel VEGFR-2 inhibitors represents a significant step forward in the quest for effective cancer therapies. Continued exploration in this area could pave the way for breakthroughs in oncology, improving patient outcomes and expanding treatment options.
💬 Your comments
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Discovery of Vascular Endothelial Growth Factor Receptor 2 Inhibitors Employing Junction Tree Variational Autoencoder with Bayesian Optimization and Gradient Ascent.
Abstract
In the development of anticancer medications, vascular endothelial growth factor receptor 2 (VEGFR-2), which belongs to the protein tyrosine kinase family, emerges as one of the most significant targets of interest. The ongoing Food and Drug Administration (FDA) approval of novel therapeutic medicines toward VEGFR-2 emphasizes the urgent need to discover sophisticated molecular structures that are capable of reliably limiting VEGFR-2 activity. Recognizing the huge potential of deep-learning-based molecular model advancements, we focused our study on exploring the chemical space to find small molecules potentially inhibiting VEGFR-2. To achieve this goal, we utilized the junction tree variational autoencoder in combination with two optimization approaches on the latent space: the local Bayesian optimization on the initial data set and the gradient ascent on nine FDA-approved drugs targeting VEGFR-2. The optimization results yielded a set of 493 uncharted small molecules. Quantitative structure-activity relationship (QSAR) models and molecular docking were used to assess the generated molecules for their inhibitory potential using their predicted pIC50 and binding affinity. The QSAR model constructed on RDK7 fingerprints using the CatBoost algorithm achieved remarkable coefficients of determination (R 2) of 0.792 ± 0.075 and 0.859 with respect to internal and external validation. Molecular docking was implemented using the 4ASD complex with optimistic retrospective control results (the ROC-AUC value was 0.710 and the binding activity threshold was -7.90 kcal/mol). Newly generated molecules possessing acceptable results corresponding to both assessments were shortlisted and checked for interactions with the protein at the binding site on important residues, including Cys919, Asp1046, and Glu885.
Author: [‘Truong GB’, ‘Pham TA’, ‘To VT’, ‘Le HL’, ‘Van Nguyen PC’, ‘Trinh TC’, ‘Phan TL’, ‘Truong TN’]
Journal: ACS Omega
Citation: Truong GB, et al. Discovery of Vascular Endothelial Growth Factor Receptor 2 Inhibitors Employing Junction Tree Variational Autoencoder with Bayesian Optimization and Gradient Ascent. Discovery of Vascular Endothelial Growth Factor Receptor 2 Inhibitors Employing Junction Tree Variational Autoencoder with Bayesian Optimization and Gradient Ascent. 2024; 9:47180-47193. doi: 10.1021/acsomega.4c07689