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An AI pipeline to investigate the binding properties of poorly annotated molecules

Nature Reviews Physics volume 4, page 359 (2022)Cite this article

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Binding between proteins and small molecules is an important link-prediction task affecting biological processes such as metabolic reactions, regulatory mechanisms and signal transduction pathways. However, binding annotations are available only for a small subset of the possible combinations of proteins and molecules. For instance, in the Foodome project, we have data for more than 135,000 small molecules found in foods, but only 4.58% of the collected molecules have known binding annotations with proteins. Machine learning (ML) models aim to fill the gap by predicting binding, but — given the paucity of data — their reliability remains questionable. Similar issues affect all areas leveraging sparse and biased network data such as recommendation systems, citation networks and collaboration networks.

We showed analytically that excellent performance in transductive tests can be achieved by algorithms that are much simpler than neural networks and that disregard the molecular structures of proteins and ligands. These configuration models are inspired by network science, and we use them as the baseline of the binding prediction task. Their success in transductive tests is driven by the underlying network topology of the training datasets, which presents an extremely biased picture of binding: molecular structures tend to either have an excessive ratio of positive annotations (binding) to negative (non-binding), or vice versa. In scenarios affected by this annotation imbalance, ML models behave like configuration models, disregarding structural information, and performing poorly in inductive tests.

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Acknowledgements

The author would like to acknowledge the whole team of experts behind AI-Bind and, in particular, A. Chatterjee, T. Eliassi-Rad and A.-L. Barábasi. The author also thanks C. Both and G. Kitzinger for helping in the visualization of the protein–ligand bipartite network.

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  1. Network Science Institute and Department of Physics, Northeastern University, Boston, MA, USA

    Giulia Menichetti

  2. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Giulia Menichetti

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  1. Giulia Menichetti
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Correspondence to Giulia Menichetti.

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Menichetti, G. An AI pipeline to investigate the binding properties of poorly annotated molecules. Nat Rev Phys 4, 359 (2022). https://doi.org/10.1038/s42254-022-00471-1

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