QuickBind: A Light-Weight And Interpretable Molecular Docking Model
This addresses the need for moderately accurate but fast pose prediction in computational drug discovery, though it is incremental as it builds on existing methods to fill a capability gap.
The authors tackled the problem of fast molecular docking for high-throughput virtual screening by developing QuickBind, a light-weight algorithm that provides a trade-off between accuracy and runtime, achieving competitive results on benchmarks and demonstrating capabilities for clinically-relevant drug targets.
Predicting a ligand's bound pose to a target protein is a key component of early-stage computational drug discovery. Recent developments in machine learning methods have focused on improving pose quality at the cost of model runtime. For high-throughput virtual screening applications, this exposes a capability gap that can be filled by moderately accurate but fast pose prediction. To this end, we developed QuickBind, a light-weight pose prediction algorithm. We assess QuickBind on widely used benchmarks and find that it provides an attractive trade-off between model accuracy and runtime. To facilitate virtual screening applications, we augment QuickBind with a binding affinity module and demonstrate its capabilities for multiple clinically-relevant drug targets. Finally, we investigate the mechanistic basis by which QuickBind makes predictions and find that it has learned key physicochemical properties of molecular docking, providing new insights into how machine learning models generate protein-ligand poses. By virtue of its simplicity, QuickBind can serve as both an effective virtual screening tool and a minimal test bed for exploring new model architectures and innovations. Model code and weights are available at https://github.com/aqlaboratory/QuickBind .