Condensing Molecular Docking CNNs via Knowledge Distillation

Knowledge of the bound protein-ligand structure is critical to many drug discovery tasks. One tool for in silico bound structure elucidation is molecular docking, which samples and scores ligand binding conformations. Recent work has demonstrated that convolutional neural networks (CNNs) for protein-ligand pose scoring outperform conventional scoring functions. Scoring performance can be further increased by taking the average of multiple CNN models, termed ensembles. However, ensembles of large parameter models require significant computational resources and therefore are difficult to apply to high-throughput molecular docking for virtual screening. We investigate knowledge distillation as a framework to condense the knowledge of large, powerful CNN model ensembles into a single reduced CNN model for a significant reduction in computational cost. Ensemble KD produces single models that outperform non-KD trained single models.