Screening, optimization, and ADMET evaluation of HCJ007 for pancreatic cancer treatment through active learning and dynamics simulation.

In this study, we leveraged a sophisticated active learning model to enhance virtual screening for SQLE inhibitors. The model's improved predictive accuracy identified compounds with significant advantages in binding affinity and thermodynamic stability. Detailed analyses, including molecular dynamics simulations and ADMET profiling, were conducted, particularly focusing on compounds CMNPD11566 and its derivative HCJ007.