Deep Confident Steps to New Pockets: Strategies for Docking
  Generalization
Gabriele Corso Arthur Deng Benjamin Fry Nicholas Polizzi Regina Barzilay Tommi Jaakkola
Accurate blind docking has the potential to lead to new biological
breakthroughs, but for this promise to be realized, docking methods must
generalize well across the proteome. Existing benchmarks, however, fail to
rigorously assess generalizability. Therefore, we develop DockGen, a new
benchmark based on the ligand-binding domains of proteins, and we show that
existing machine learning-based docking models have very weak generalization
abilities. We carefully analyze the scaling laws of ML-based docking and show
that, by scaling data and model size, as well as integrating synthetic data
strategies, we are able to significantly increase the generalization capacity
and set new state-of-the-art performance across benchmarks. Further, we propose
Confidence Bootstrapping, a new training paradigm that solely relies on the
interaction between diffusion and confidence models and exploits the
multi-resolution generation process of diffusion models. We demonstrate that
Confidence Bootstrapping significantly improves the ability of ML-based docking
methods to dock to unseen protein classes, edging closer to accurate and
generalizable blind docking methods.