Skip to main content

Quantitative Structure-Activity Relationship Modeling for the Prediction of Fish Toxicity Lethal Concentration on Fathead Minnow

  • Conference paper
  • First Online:
Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15310))

Included in the following conference series:

  • 118 Accesses

Abstract

As there has been a rise in the usage of in silico approaches, for assessing the risks of harmful chemicals upon animals, more researchers focus on the utilization of Quantitative Structure Activity Relationship models. A number of machine learning algorithms link molecular descriptors that can infer chemical structural properties associated with their corresponding biological activity. Efficient and comprehensive computational methods which can process huge set of heterogeneous chemical datasets are in demand. In this context, this study establishes the usage of various machine learning algorithms in predicting the acute aquatic toxicity of diverse chemicals on Fathead Minnow (Pimephales promelas). Sample drive approach is employed on the train set for binning the data so that they can be located in a domain space having more similar chemicals, instead of using the dataset that covers a wide range of chemicals at the entirety. Here, bin wise best learning model and subset of features that are minimally required for the classification are found for further ease. Several regression methods are employed to find the estimation of toxicity LC50 value by adopting several statistical measures and hence bin wise strategies are determined. Through experimentation, it is evident that the proposed model surpasses the other existing models by providing an R2 of 0.8473 with RMSE 0.3035 which is comparable. Hence, the proposed model is competent for estimating the toxicity in new and unseen chemical.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Cassotti, M., Ballabio, D., Todeschini, R., Consonni, V.: A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephalespromelas). SAR QSAR Environ. Res. 26(3), 217–243 (2015)

    Article  Google Scholar 

  2. In, Y., Lee, S.K., Kim, P.J., No, K.T.: Prediction of acute toxicity to fathead minnow by local model based QSAR and global QSAR approaches. Bull. Korean Chem. Soc. 33(2), 613–619 (2012)

    Google Scholar 

  3. Devillers, J.: A new strategy for using supervised artificial neural networks in QSAR. SAR QSAR Environ. Res. 16(5), 433–442 (2005)

    Article  Google Scholar 

  4. Sheffield, T.Y., Judson, R.S.: Ensemble QSAR modeling to predict multispecies fish toxicity lethal concentrations and points of departure. Environ. Sci. Technol. 53, 12793−12802 (2019)

    Google Scholar 

  5. Gajewicz-Skretna, A., Furuhama, A., Yamamoto, H., Suzuki, N.: Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: towards similarity-based machine learning methods. Chemosphere 280, 130681 (2021)

    Google Scholar 

  6. Karim, A., et al.: Quantitative toxicity prediction via meta ensembling of multitask deep learning models. ACS Omega 6, 12306−12317 (2021)

    Google Scholar 

  7. Singh, K.P., Gupta, S., Kumar, A., Mohan, D.: Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology. Chem. Res. Toxicol. 27, 741−753 (2014)

    Google Scholar 

  8. Nendzat, M., Russomi, C.L.: QSAR modelling of the ERL-D fathead minnow acute toxicity database. Xenobiotica 27(2), 147–170 (1991)

    Article  Google Scholar 

  9. Wang, Y., Chen, X.: A joint optimization QSAR model of fathead minnow acute toxicity based on a radial basis function neural network and its consensus modeling. RSC Adv. 10, 21292 (2020)

    Google Scholar 

  10. Liu, H., Setiono, R.: Chi2: feature selection and discretization of numeric attributes. In: 7th IEEE International Conference Proceedings on Tools with Artificial Intelligence, pp. 388–391 (1995)

    Google Scholar 

  11. Lozano, S., Lescot, E., Halm, M.-P., Lepailleur, A., Bureau, R., Rault, S.: Prediction of acute toxicity in fish by using QSAR methods and chemical modes of action. J. Enzyme Inhibit. Med. Chem. 25(2), 195–203 (2010)

    Google Scholar 

  12. Dearden, J.C., Cronin, M.T.D., Kaiser, K.L.E.: How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR). SAR QSAR Environ. Res. 20(3–4), 241–266 (2009)

    Article  Google Scholar 

  13. Rakhimbekova, A., et al.: Cross-validation strategies in QSPR modelling of chemical reactions. SAR QSAR Environ. Res. 32(3), 207–219 (2021)

    Article  Google Scholar 

  14. Lovrić, M., Malev, O., Klobučar, G., Kern, R., Liu, J.J., Lučić, B.: Predictive capability of QSAR models based on the CompTox zebrafish embryo assays: an imbalanced classification problem. Molecules 26, 1617 (2021)

    Google Scholar 

  15. Judson, R.: ToxValDB: Compiling Publicly Available In Vivo Toxicity Data. Presented at EPA’s Computational Toxicology Communities of Practice Monthly Meeting, RTP, NC, (2018)

    Google Scholar 

  16. Cassotti, M., Ballabio, D., Consonni, V., Mauri, A., Tetko, I.V., Todeschini, R.: Prediction of acute aquatic toxicity towards Daphnia magna by using the GA-kNN method. ATLA-Alternatives to Laboratory Animals 42, 31–41 (2014)

    Article  Google Scholar 

  17. Enoch, S.J., Cronin, M.T.D., Schultz, T.W., Madden, J.C.: An evaluation of global QSAR models for the prediction of the toxicity of phenols to Tetrahymena pyriformis. Chemosphere 71, 1225–1232 (2008)

    Article  Google Scholar 

  18. Toma, C., Cappelli, C.I., Manganaro, A., Lombardo, A., Arning, J., Benfenati, E.: New models to predict the acute and chronic toxicities of representative species of the main trophic levels of aquatic environments. Molecules 26, 6983 (2021)

    Article  Google Scholar 

  19. Wu, X., Zhang, Q., Hu, J.: QSAR study of the acute toxicity to fathead minnow based on a large dataset. SAR QSAR Environ. Res. 27(2), 147–164 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Kavitha .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kavitha, R., Guru, D.S. (2025). Quantitative Structure-Activity Relationship Modeling for the Prediction of Fish Toxicity Lethal Concentration on Fathead Minnow. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15310. Springer, Cham. https://doi.org/10.1007/978-3-031-78192-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-78192-6_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78191-9

  • Online ISBN: 978-3-031-78192-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics