8 research outputs found
PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences
The last few years have seen the development of numerous deep learning-based
protein-ligand docking methods. They offer huge promise in terms of speed and
accuracy. However, despite claims of state-of-the-art performance in terms of
crystallographic root-mean-square deviation (RMSD), upon closer inspection, it
has become apparent that they often produce physically implausible molecular
structures. It is therefore not sufficient to evaluate these methods solely by
RMSD to a native binding mode. It is vital, particularly for deep
learning-based methods, that they are also evaluated on steric and energetic
criteria. We present PoseBusters, a Python package that performs a series of
standard quality checks using the well-established cheminformatics toolkit
RDKit. Only methods that both pass these checks and predict native-like binding
modes should be classed as having "state-of-the-art" performance. We use
PoseBusters to compare five deep learning-based docking methods (DeepDock,
DiffDock, EquiBind, TankBind, and Uni-Mol) and two well-established standard
docking methods (AutoDock Vina and CCDC Gold) with and without an additional
post-prediction energy minimisation step using a molecular mechanics force
field. We show that both in terms of physical plausibility and the ability to
generalise to examples that are distinct from the training data, no deep
learning-based method yet outperforms classical docking tools. In addition, we
find that molecular mechanics force fields contain docking-relevant physics
missing from deep-learning methods. PoseBusters allows practitioners to assess
docking and molecular generation methods and may inspire new inductive biases
still required to improve deep learning-based methods, which will help drive
the development of more accurate and more realistic predictions.Comment: 10 pages, 6 figures, version 2 added an additional filter to the
PoseBusters Benchmark set to remove ligands with crystal contacts, version 3
corrected the description of the binding site used for Uni-Mo