Skip to main content
SpringerLink
Log in

Electrophilicity-based charge transfer for developing aquatic-quantitative structure toxicity relationships (Aqua-QSTR)

  • Regular Article
  • Published:
Theoretical Chemistry Accounts Aims and scope Submit manuscript

Abstract

In this study, aquatic-quantitative structure toxicity relationship (Aqua-QSTR) models for predicting chemical toxicity in aquatic organisms is developed using the electrophilicity-based charge transfer (ECT) descriptor. Firstly, the nature of charge transfer between the selected series of chemical compounds and deoxyribonucleic acid (DNA) bases is carried out to know their electron-donating or accepting nature. Based on the nature of the interaction, Aqua-QSTR studies were carried out for Fathead minnow and Tetrahymena pyriformis using linear regression (LR), machine learning-based random forest regression (RFR), and support vector regression (SVR) methods. LR-derived QSTR on the first set of compounds against Fathead minnow based on maximum ECT values provides 87.7% variation in data with root mean square error (RMSE) of 0.145. Similarly, LR-derived Aqua-QSTR studies on the second set of compounds against Tetrahymena pyriformis based on maximum ECT values give a 90.6% variation in data with an RMSE of 0.163. Further, RFR (SVR) model provides 96.8% (87.7%) variation in data with RMSE of 0.074 (0.145) for the Fathead minnow and 98.1% (90.4%) variation in data with RMSE of 0.073 (0.165) for Tetrahymena pyriformis. The results revealed the utility of ECT in the toxicity prediction of chemical compounds in aquatic organisms.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Chermette H (1999) Chemical reactivity indexes in density functional theory. J Comput Chem 20:129–154

    Article  CAS  Google Scholar 

  2. Geerlings P, De Proft F, Langenaeker W (2003) Conceptual density functional theory. Chem Rev 103:1793–1874

    Article  CAS  PubMed  Google Scholar 

  3. Chattaraj PK, Sarkar U, Roy DR (2006) Electrophilicity index. Chem Rev 106:2065–2091

    Article  CAS  PubMed  Google Scholar 

  4. Padmanabhan J, Parthasarathi R, Elango M, Subramanian V, Krishnamoorthy BS, Gutierrez-Oliva S, Toro-Labbe A, Roy DR, Chattaraj PK (2007) Multiphilic descriptor for chemical reactivity and selectivity. J Phys Chem A 111:9130–9138

    Article  CAS  PubMed  Google Scholar 

  5. Padmanabhan J, Parthasarathi R, Subramanian V, Chattaraj PK (2006) Chemical information insights into the series of chloroanisoles-A theoretical approach. J Mol Struct: Theochem 774:49–57

    Article  CAS  Google Scholar 

  6. Padmanabhan J, Parthasarathi R, Subramanian V, Chattaraj PK (2006) Theoretical study on the complete series of chloroanilines. J Phys Chem A 110:9900–9907

    Article  CAS  PubMed  Google Scholar 

  7. Roy DR, Sarkar U, Chattaraj PK, Mitra A, Padmanabhan J, Parthasarathi R, Subramanian V, Van Damme S, Bultinck P (2006) Analyzing toxicity through electrophilicity. Mol Div 10:119–131

    Article  CAS  Google Scholar 

  8. Parthasarathi R, Padmanabhan J, Elango M, Chitra K, Subramanian V, Chattaraj PK (2006) pKa prediction using group philicity. J Phys Chem A 110:6540–6544

    Article  CAS  PubMed  Google Scholar 

  9. Roy DR, Parthasarathi R, Padmanabhan J, Sarkar U, Subramanian V, Chattaraj PK (2006) Careful scrutiny of the philicity concept. J Phys Chem A 110:1084–1093

    Article  CAS  PubMed  Google Scholar 

  10. Parthasarathi R, Elango M, Padmanabhan J, Subramanian V, Roy DR, Sarkar U, Chattaraj PK (2006) Application of quantum chemical descriptors in computational medicinal chemistry and chemoinformatics. Indian J Chem 45A:111–125

    CAS  Google Scholar 

  11. Padmanabhan J, Parthasarathi R, Subramanian V, Chattaraj PK (2007) Electrophilicity-based charge transfer descriptor. J Phys Chem A 111:1358–1361

    Article  CAS  PubMed  Google Scholar 

  12. Roy SM, Roy DR, Sahoo SK (2015) Toxicity prediction of PHDDs and phenols in the light of nucleic acidbases and DNA base pair interaction. J Mol Graph Modell 62:128–137

    Article  Google Scholar 

  13. Ciaburro G (2018) Regression analysis with R: design and develop statistical nodes to identify unique relationships within data at scale. Packt Publishing Ltd., Birmingham, United Kingdom

    Google Scholar 

  14. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    Article  Google Scholar 

  15. Breiman L (2008) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  16. Basak D, Pal S, Ch D, Patranabis R (2007) Support vector regression neural info process. Lett and Rev 11(10):203–224

    Google Scholar 

  17. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43:1947–1958

    Article  CAS  PubMed  Google Scholar 

  18. Rustam Z, Zhafarina F, Saragih GS, Hartini S (2021) Pancreatic cancer classification using logistic regression and random forest. IAES Int J Artif Intell 10:476–481

    Google Scholar 

  19. Zhou Y, Li S, Zhao Y, Guo M, Liu Y, Li M, Wen Z (2021) Quantitative structure-activity relationship (QSAR) model for the severity prediction of drug-induced rhabdomyolysis by using random forest. Chem Res Tox 34(2):514–521

    Article  CAS  Google Scholar 

  20. Sun G, Li S, Cao Y, Lang F (2017) Cervical cancer diagnosis based on random forest. Int J Perform Eng 13:446–457

    Google Scholar 

  21. Dai B, Chen R, Zhu S, Zhang W (2018) Using random forest algorithm for breast cancer diagnosis. In Proceedings of the international symposium on computer, consumer and control (IS3C) Taichung Taiwan 6–8 December 449–452

  22. Fang X, Liu W, Ai J, He M, Wu Y, Shi Y, Shen W, Bao C (2020) Forecasting incidence of infectious diarrhea using random forest in Jiangsu Province China. BMC Infect Dis 20:1–8

    Article  Google Scholar 

  23. Kamal S, Urata J, Cavassini M, Liu H, Kouyos R, Bugnon O, Wang W, Schneider M (2020) Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence AIDS. Care 33:530–560

    Google Scholar 

  24. Moorthy K, Mohamad M (2011) Random Forest for gene selection and microarray data classification In: Proceedings of the third knowledge technology week, Kajang, Malaysia. 18–22 July 174–183

  25. Anaissi A, Kennedy PJ, Goyal M, Catchpoole DR (2013) A balanced iterative random forest for gene selection from microarray data. BMC Bioinform 14:1–10

    Article  Google Scholar 

  26. Wu CH, Ho JM, Lee DT (2004) Travel-time prediction with support vector regression. IEEE 5(4):276–281

    Google Scholar 

  27. Jain RK, Smith KM, Culligan PJ, Taylor JE (2014) Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Appl Energy 123:168–178

    Article  Google Scholar 

  28. Moguerza JM, Muñoz A, Psarakis S (2007) Monitoring nonlinear profiles using support vector machines. CIARP lecture notes in computer science 4756:574–583 Springer

  29. Thissen U, Pepers M, Üstön B, Melssen WJ, Buydens LMC (2004) Comparing support vector machines to PLS for spectral regression applications. Chemom Intell Lab Syst 73(2):169–179

    Article  CAS  Google Scholar 

  30. Mei H, Zhou Y, Liang G, Li ZL (2005) Support vector machine applied in QSAR modelling. Chin Sci Bull 50(20):2291–2296

    Article  Google Scholar 

  31. Huang M, Wei Y, Wang J, Zhang Y (2016) Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation. Sci Rep 6(1):32368–32382

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yang X, Li M, Su Q, Wu M, Gu T, Lu W (2013) QSAR studies on pyrrolidine amides derivatives as DPP-IV inhibitors for type 2 diabetes. Med Chem Res 22(11):5274–5283

    Article  CAS  Google Scholar 

  33. He L, Jurs PC (2005) Assessing the reliability of a QSAR model’s predictions. J Mol Graph Modell 23:503–523

    Article  CAS  Google Scholar 

  34. Elidrissi B, Ousaa A, Ghamali M, Chtita S, Ajana MA, Bouachrine M, Lakhlifi T (2015) The acute toxicity of nitrobenzenes to tetrahymena pyriformis: combining DFT and QSAR studies. Mor J Chem 3:848–860

    CAS  Google Scholar 

  35. Parr RG, Szentpaly LV, Liu S (1999) Electrophilicity index. J Am Chem Soc 121:1922–1924

    Article  CAS  Google Scholar 

  36. Becke AD (1988) Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A 38:3098–3100

    Article  CAS  Google Scholar 

  37. Lee C, Yang W, Parr RG (1988) Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys Rev B 37:785–789

    Article  CAS  Google Scholar 

  38. Hariharan PC, Pople JA (1973) The influence of polarization functions on molecular orbital hydrogenation energies. Theor Chim Acta 28:213–222

    Article  CAS  Google Scholar 

  39. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich AV, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark MJ, Heyd JJ, Brothers EN, Kudin KN, Staroverov VN, Keith TA, Kobayashi R, Normand J, Raghavachari K, Rendell AP, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ (2016) Gaussian 16 Revision A.03. Gaussian Inc Wallingford CT

  40. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  41. Project Jupyter, an open-source software. https://jupyter.org/

Download references

Acknowledgements

The authors thank the Department of Science and Technology (DST), New Delhi, India, and the Council of Scientific & Industrial Research (CSIR), New Delhi, India, for providing financial support. Authors thank Professor Paul W Ayers, Professor Frank De Proft, Professor Shubin Liu, Professor Utpal Sarkar, and Professor Alejandro Toro-Labbe for their efforts on “Festschrift” in honor of Professor Pratim Kumar Chattaraj, on the occasion of his 65th birthday. CSIR-IITR Communication no. IITR/SEC/MS/2023/05.

Author information

Author notes

  1. Zeeshan Arif and Prakrity Singh have contributed equally to this work.

Authors and Affiliations

  1. Computational Toxicology Facility, Toxicoinformatics and Industrial Research, CSIR—Indian Institute of Toxicology Research, 31 Mahatma Gandhi Marg, Lucknow, Uttar Pradesh, 226001, India

    Zeeshan Arif, Prakrity Singh & Ramakrishnan Parthasarathi

  2. Academy of Scientific & Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, 201002, India

    Zeeshan Arif, Prakrity Singh & Ramakrishnan Parthasarathi

  3. Department of Physics, Government Arts College for Men - Autonomous, Nandanam, Chennai, Tamil Nadu, 600035, India

    Jaganathan Padmanabhan

Authors
  1. Zeeshan Arif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prakrity Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ramakrishnan Parthasarathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jaganathan Padmanabhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZA and PS performed the study, tabulated the results, and wrote the manuscript draft. JP did the conceptualization, compilation, tables/figures preparation and draft writing. The work was conceptualized, edited, and supervised by RP. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Ramakrishnan Parthasarathi or Jaganathan Padmanabhan.

Ethics declarations

Conflict of interest

The authors declare no competing financial interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

214_2023_2977_MOESM1_ESM.docx

Supplementary file 1 Tables S1-S6 provide the codes and their output for LR, RFR and SVR algorithms as implemented using the Scikit-learn (machine learning in Python) [40] in Jupyter notebook (version 5.7.8).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arif, Z., Singh, P., Parthasarathi, R. et al. Electrophilicity-based charge transfer for developing aquatic-quantitative structure toxicity relationships (Aqua-QSTR). Theor Chem Acc 142, 38 (2023). https://doi.org/10.1007/s00214-023-02977-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00214-023-02977-y

Keywords

Search

Navigation