Core clinical engineering and protocols for Geometric Deep Learning Docking.
Traditional molecular docking methods rely on brute-force grid search algorithms and simplified force fields that take minutes per compound and struggle to model induced-fit receptor flexibility. Screening ultra-large chemical libraries (billions of compounds) takes years of compute cluster time.
DrugInference applies equivariant neural networks that operate directly on continuous 3D atomic coordinates. The model predicts the thermodynamic ground-state binding pose of candidate ligands in milliseconds, accounting for protein side-chain flexibility while achieving sub-angstrom structural agreement with experimental crystallography.