Virtual Screening

Virtual screening aims to narrow large compound libraries to candidates worth testing experimentally, using protein–ligand docking and scoring, ligand-based machine learning, and learned molecular or 3D structure representations. Newer approaches use contrastive learning, generative models, active learning, and target-specific adaptation to improve ranking or search very large libraries efficiently. Success is often measured by early hit enrichment and recovery of diverse compounds, not just overall ranking accuracy. A central challenge is that performance can fall on realistic hard-negative benchmarks or new targets, while calibration, minority-class reliability, and limited experimental data complicate trustworthy selection.