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Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space

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  • Domonkos Pogány
  • Péter Antal

Abstract

Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainability of machine learning models remains a challenge. Our work aims to provide more interpretable interaction prediction models using similarity-based prediction in a latent space aligned to biological hierarchies. We investigated integrating drug and protein hierarchies into a joint-embedding drug-target latent space via embedding regularization by conducting a comparative analysis between models employing traditional flat Euclidean vector spaces and those utilizing hyperbolic embeddings. Besides, we provided a latent space analysis as an example to show how we can gain visual insights into the trained model with the help of dimensionality reduction. Our results demonstrate that hierarchy regularization improves interpretability without compromising predictive performance. Furthermore, integrating hyperbolic embeddings, coupled with regularization, enhances the quality of the embedded hierarchy trees. Our approach enables a more informed and insightful application of interaction prediction models in drug discovery by constructing an interpretable hyperbolic latent space, simultaneously incorporating drug and target hierarchies and pairing them with available interaction information. Moreover, compatible with pairwise methods, the approach allows for additional transparency through existing explainable AI solutions.

Suggested Citation

  • Domonkos Pogány & Péter Antal, 2024. "Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-23, March.
  • Handle: RePEc:plo:pone00:0300906
    DOI: 10.1371/journal.pone.0300906
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    References listed on IDEAS

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    1. Anna Klimovskaia & David Lopez-Paz & Léon Bottou & Maximilian Nickel, 2020. "Poincaré maps for analyzing complex hierarchies in single-cell data," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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    3. Anna Cichonska & Balaguru Ravikumar & Elina Parri & Sanna Timonen & Tapio Pahikkala & Antti Airola & Krister Wennerberg & Juho Rousu & Tero Aittokallio, 2017. "Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-28, August.
    4. Ayan Chatterjee & Robin Walters & Zohair Shafi & Omair Shafi Ahmed & Michael Sebek & Deisy Gysi & Rose Yu & Tina Eliassi-Rad & Albert-László Barabási & Giulia Menichetti, 2023. "Improving the generalizability of protein-ligand binding predictions with AI-Bind," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
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