AstraBIND: Fast, Accurate Ligand Binding Site Prediction Across a 2M+ Ligand Panel
AstraBIND is a state-of-the-art graph attention network trained on 250,000+ curated protein–ligand interactions, delivering residue-level binding maps and ligand type predictions in under 15 minutes — across a comprehensive library of over 2 million ligands.
Why AstraBIND?
Identifying ligand binding sites is one of the most time-consuming steps in drug discovery. Classical docking pipelines are slow, and sequence-only methods miss structural context. AstraBIND combines the speed of sequence-based models with the accuracy of structure-aware deep learning.
Residue-Level Precision
AstraBIND highlights exact residues involved in binding, grouping them into spatially coherent pockets rather than just flagging regions vaguely.
Ligand Classification
Beyond pockets, AstraBIND predicts which ligand class is most likely to bind, spanning 17 curated categories such as nucleotides, peptides, cofactors, and metal ions.
Near-Realtime
With a highly optimized GATv2 backbone, AstraBIND completes full residue-level predictions for most proteins in just <15 minutes.
Proven Performance Across Ligand Classes
Trained on 250K+ protein-ligand interactions, AstraBIND achieves state-of-the-art accuracy across diverse ligand types, with a weighted macro-F1 of 0.47. Its top performance on nucleotides (F1 = 0.79), porphyrins (F1 = 0.74), and cofactors (F1 = 0.73) highlights its ability to recover biologically meaningful pockets while keeping false positives low.
Graph-Aware, Context-Rich Predictions
Powered by a Graph Attention Network (GATv2), AstraBIND learns from full 3D structural graphs and integrates sequence, homology, and spatial features. Its binding-gated ligand head focuses predictions only where strong binding evidence exists, reducing spurious ligand calls and boosting interpretability.
Validated on Canonical Targets
AstraBIND reliably recovers hallmark binding pockets in well-characterized proteins: β2-adrenergic receptor (correct ranking of key ligands), hemoglobin β-chain (heme pocket fully recovered), and calmodulin (all four Ca²⁺-binding EF-hands identified). These results illustrate its real-world value in drug discovery and structural biology.
See AstraBIND in action.
Curious how AstraBIND pinpoints ligand binding pockets and predicts ligand types from your protein sequence or structure? Book a demo and see how AstraBIND can accelerate your drug discovery or structural biology projects.
