A HybridEnergy-Based and AI-Based Screening Approachfor the Discovery of Novel Inhibitors of AXL

AXL, part of the TAM receptor tyrosine kinase family, plays a significant role in the growth and survival of various tissues and tumors, making it a critical target for cancer therapy. This study introduces a novel high-throughput virtual screening (HTVS) methodology that merges an AI-enhanced graph neural network, PLANET, with a geometric deep learning algorithm, DeepDock. Using this approach, we identified potent AXL inhibitors from our database. Notably, compound <b>9</b>, with an IC₅₀ of 9.378 nM, showed excellent inhibitory activity, suggesting its potential as a candidate for further research. We also performed molecular dynamics simulations to explore the interactions between compound <b>9</b> and AXL, providing insights for future enhancements. This hybrid screening method proves effective in finding promising AXL inhibitors, and advancing the development of new cancer therapies.