Applying artificial intelligence and machine learning framework for de novo design of pyrazole-based VEGFR-2...

Area of Science:

• Oncology
• Medicinal Chemistry
• Computational Drug Discovery

Background:

• Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2) is crucial for tumor angiogenesis and metastasis, making it a key target in cancer therapy.
• Pyrazole derivatives show potential as VEGFR-2 inhibitors due to their diverse structures and biological activities.
• Targeted blockade of VEGFR-2 is a promising strategy for treating various cancers.

Purpose of the Study:

• To utilize artificial intelligence and machine learning (AI-ML) to design novel pyrazole derivatives as anticancer agents targeting VEGFR-2 kinase.
• To identify potent inhibitors and modulators of VEGFR-2 kinase activity.

Main Methods:

• Employed AI-ML frameworks including Reinvent4, ADMET-AI, and SwissADME for drug design and property prediction.
• Utilized AutoDock Vina, molecular dynamics (MD) simulations with Gromacs, and MM-GBSA for molecular interaction and property analysis.
• Systematically refined chemical space to identify promising drug candidates.

Main Results:

• Successfully developed novel pyrazole derivatives with potential VEGFR-2 kinase inhibitory and modulatory activities.
• Identified five lead compounds demonstrating favorable molecular properties and interactions relevant to VEGFR-2 inhibition.
• Established a robust AI-ML framework for efficient drug discovery and optimization.

Conclusions:

• The study identified five lead pyrazole derivatives as potential therapeutics targeting VEGFR-2.
• These compounds require further biological efficacy assessment and optimization for preclinical development.
• The integrated AI-ML approach provides a powerful platform for modern drug discovery targeting kinases like VEGFR-2.