Impact of Pre-trained Representations in Machine Learning-Based Protein-Ligand Docking

This work analyzes possible avenues for improving performance and generalization of machine learning-based models for protein-ligand docking. Learned representations from models pre-trained on different chemical tasks are leveraged by introducing them as features in the molecular graphs, which generative docking models are then trained on.