Skip to main content
Log in

Efficient prediction of relative ligand binding affinity in drug discovery

  • Research Briefing
  • Published:

From Nature Computational Science

View current issue Submit your manuscript

A pairwise binding comparison network (PBCNet) has been established for predicting the relative binding affinity among congeneric ligands, using a physics-informed graph attention mechanism with a pair of protein pocket-ligand complexes as input. PBCNet shows practical value in guiding structure-based drug lead optimization with speed, precision, and ease-of-use.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1: The PBCNet framework.

References

  1. Cournia, Z. & Allen, B. Relative binding free energy calculations in drug discovery: Recent advances and practical considerations. J. Chem. Inf. Model. 57, 2911–2937 (2017). A review article that summarizes binding free energy calculation methods for lead optimization.

    Article  Google Scholar 

  2. McNutt, A. T. & Koes, D. R. Improving ΔΔG predictions with a multitask convolutional siamese network. J. Chem. Inf. Model. 62, 1819–1829 (2022). This paper reports two deep learning models for relative binding affinity calculation called Dense and Default2018, but their accuracy is not yet satisfactory.

    Article  Google Scholar 

  3. Theodoris, C. V. & Xiao, L. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023). The paper provides an introduction to transfer learning.

    Article  Google Scholar 

  4. Wang, L. & Wu, Y. J. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J. Am. Chem. Soc. 137, 2695–2703 (2015). This paper reports a binding free energy calculation method called FEP+.

    Article  Google Scholar 

  5. Jumper, J. & Evans, R. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). AlphaFold is a successful example that integrates domain-specific knowledge into its modelling framework.

    Article  Google Scholar 

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Yu, J. et al. Computing the relative binding affinity of ligands based on a pairwise binding comparison network. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00529-9 (2023).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Efficient prediction of relative ligand binding affinity in drug discovery. Nat Comput Sci 3, 829–830 (2023). https://doi.org/10.1038/s43588-023-00531-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-023-00531-1

  • Springer Nature America, Inc.

Navigation