ETD

• biology
• docking
• drug discovery
• machine learning
• pretraining

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.