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Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling

Author

Listed:
  • Ruibo Zhang

    (Texas Tech University)

  • Daniel Nolte

    (Texas Tech University)

  • Cesar Sanchez-Villalobos

    (Texas Tech University)

  • Souparno Ghosh

    (University of Nebraska - Lincoln)

  • Ranadip Pal

    (Texas Tech University)

Abstract

Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learning-based QSAR models and provide better intuitive interpretation by extracting an approximate isometry between the chemical space of the drugs and their activity space.

Suggested Citation

  • Ruibo Zhang & Daniel Nolte & Cesar Sanchez-Villalobos & Souparno Ghosh & Ranadip Pal, 2024. "Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49372-0
    DOI: 10.1038/s41467-024-49372-0
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    References listed on IDEAS

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    1. Eugen Lounkine & Michael J. Keiser & Steven Whitebread & Dmitri Mikhailov & Jacques Hamon & Jeremy L. Jenkins & Paul Lavan & Eckhard Weber & Allison K. Doak & Serge Côté & Brian K. Shoichet & Laszlo U, 2012. "Large-scale prediction and testing of drug activity on side-effect targets," Nature, Nature, vol. 486(7403), pages 361-367, June.
    2. Søren Asmussen & Jens Ledet Jensen & Leonardo Rojas-Nandayapa, 2016. "On the Laplace Transform of the Lognormal Distribution," Methodology and Computing in Applied Probability, Springer, vol. 18(2), pages 441-458, June.
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