IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-49372-0.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-49372-0
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-49372-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christopher Dobronyi & Christian Gouri'eroux, 2020. "Consumer Theory with Non-Parametric Taste Uncertainty and Individual Heterogeneity," Papers 2010.13937, arXiv.org, revised Jan 2021.
    2. Choo Jun Tan & Siew Chin Neoh & Chee Peng Lim & Samer Hanoun & Wai Peng Wong & Chu Kong Loo & Li Zhang & Saeid Nahavandi, 2019. "Application of an evolutionary algorithm-based ensemble model to job-shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 879-890, February.
    3. Azar, Macarena & Carrasco, Rodrigo A. & Mondschein, Susana, 2022. "Dealing with uncertain surgery times in operating room scheduling," European Journal of Operational Research, Elsevier, vol. 299(1), pages 377-394.
    4. Manuel D. Ortigueira, 2022. "A New Series Representation and the Laplace Transform for the Lognormal Distribution," Mathematics, MDPI, vol. 10(19), pages 1-13, September.
    5. Armaghan W Naik & Joshua D Kangas & Christopher J Langmead & Robert F Murphy, 2013. "Efficient Modeling and Active Learning Discovery of Biological Responses," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    6. Zheni Zeng & Yuan Yao & Zhiyuan Liu & Maosong Sun, 2022. "A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Julian E Fuchs & Susanne von Grafenstein & Roland G Huber & Christian Kramer & Klaus R Liedl, 2013. "Substrate-Driven Mapping of the Degradome by Comparison of Sequence Logos," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-15, November.
    8. Claire Mouminoux & Christophe Dutang & Stéphane Loisel & Hansjoerg Albrecher, 2022. "On a Markovian Game Model for Competitive Insurance Pricing," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 1061-1091, June.
    9. McFadden, Daniel, 2022. "Instability in mixed logit demand models," Journal of choice modelling, Elsevier, vol. 43(C).
    10. Qiyao Luo & Liang Zhao & Jianxing Hu & Hongwei Jin & Zhenming Liu & Liangren Zhang, 2017. "The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-19, February.
    11. Qing Ye & Chang-Yu Hsieh & Ziyi Yang & Yu Kang & Jiming Chen & Dongsheng Cao & Shibo He & Tingjun Hou, 2021. "A unified drug–target interaction prediction framework based on knowledge graph and recommendation system," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    12. Zied Chaieb & Djibril Gueye, 2022. "Pricing zero-coupon CAT bonds using the enlargement of ltration theory: a general framework ," Post-Print hal-03745077, HAL.
    13. Zied Chaieb & Djibril Gueye, 2022. "Pricing zero-coupon CAT bonds using the enlargement of ltration theory: a general framework," Papers 2208.02609, arXiv.org.
    14. Laurence Carassus & Massinissa Ferhoune, 2021. "Efficient approximations for utility-based pricing," Papers 2105.08804, arXiv.org, revised Feb 2024.
    15. Furman, Edward & Hackmann, Daniel & Kuznetsov, Alexey, 2020. "On log-normal convolutions: An analytical–numerical method with applications to economic capital determination," Insurance: Mathematics and Economics, Elsevier, vol. 90(C), pages 120-134.
    16. Lorenzo Cappello & Stephen G. Walker, 2018. "A Bayesian Motivated Laplace Inversion for Multivariate Probability Distributions," Methodology and Computing in Applied Probability, Springer, vol. 20(2), pages 777-797, June.
    17. Dang, Chao & Xu, Jun, 2020. "Unified reliability assessment for problems with low- to high-dimensional random inputs using the Laplace transform and a mixture distribution," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49372-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.