  Protein-ligand structure prediction is an essential task in drug discovery,
predicting the binding interactions between small molecules (ligands) and
target proteins (receptors). Although conventional physics-based docking tools
are widely utilized, their accuracy is compromised by limited conformational
sampling and imprecise scoring functions. Recent advances have incorporated
deep learning techniques to improve the accuracy of structure prediction.
Nevertheless, the experimental validation of docking conformations remains
costly, it raises concerns regarding the generalizability of these deep
learning-based methods due to the limited training data. In this work, we show
that by pre-training a geometry-aware SE(3)-Equivariant neural network on a
large-scale docking conformation generated by traditional physics-based docking
tools and then fine-tuning with a limited set of experimentally validated
receptor-ligand complexes, we can achieve outstanding performance. This process
involved the generation of 100 million docking conformations, consuming roughly
1 million CPU core days. The proposed model, HelixDock, aims to acquire the
physical knowledge encapsulated by the physics-based docking tools during the
pre-training phase. HelixDock has been benchmarked against both physics-based
and deep learning-based baselines, showing that it outperforms its closest
competitor by over 40% for RMSD. HelixDock also exhibits enhanced performance
on a dataset that poses a greater challenge, thereby highlighting its
robustness. Moreover, our investigation reveals the scaling laws governing
pre-trained structure prediction models, indicating a consistent enhancement in
performance with increases in model parameters and pre-training data. This
study illuminates the strategic advantage of leveraging a vast and varied
repository of generated data to advance the frontiers of AI-driven drug
discovery.

Machine Learning;Computational Engineering, Finance, and Science;Biomolecules