Skip to main content

Multi-scale QSAR Approach for Simultaneous Modeling of Ecotoxic Effects of Pesticides

  • Protocol
  • First Online:
Ecotoxicological QSARs

Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

Abstract

Pesticides are chemical or biological agents, whose ultimate purpose is to eradicate pests, thus preventing crop losses by protecting the plants from multiple diseases. Despite the importance of their use, pesticides constitute a focus of serious concern because of their harmful effects on the environment. In silico approaches have played a key role in diminishing time and financial resources when assessing the ecotoxicity of the pesticides. While many models based on quantitative structure-activity relationships (QSARs) have been reported to predict specific ecotoxicological endpoints, to date, there is no model capable of simultaneously predicting the ecotoxicological profiles of the pesticides under a wide spectrum of experimental conditions. This book chapter introduces for the first time a multi-scale QSAR model able to assess the ecotoxicity of the pesticides by considering different measures of ecotoxic effects, many bioindicator species, several different assay guidelines, and the multiple times during which the bioindicator species have been exposed to the pesticides. The multi-scale QSAR model correctly classified/predicted more than 75% of the data in both training and test sets. By interpreting different molecular descriptors in the models, this work offers the first view regarding the physicochemical properties and structural features that are common for the appearance of multiple ecotoxic effects in any chemical used as a pesticide. Finally, several molecular fragments are suggested as substructural features that can positively contribute to the diminution of the ecotoxic potential of pesticides.

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

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Plimmer JR, Gammon DW, Ragsdale NN (2003) Encyclopedia of agrochemicals. Hoboken, Wiley

    Book  Google Scholar 

  2. Monteiro HR, Pestana JLT, Novais SC, Soares A, Lemos MFL (2019) Toxicity of the insecticides spinosad and indoxacarb to the non-target aquatic midge Chironomus riparius. Sci Total Environ 666:1283–1291

    Article  CAS  PubMed  Google Scholar 

  3. He J, He H, Yan Z, Gao F, Zheng X, Fan J, Wang Y (2019) Comparative analysis of freshwater species sensitivity distributions and ecotoxicity for priority pesticides: implications for water quality criteria. Ecotoxicol Environ Saf 176:119–124

    Article  CAS  PubMed  Google Scholar 

  4. Bunin BA, Bajorath J, Siesel B, Morales G (2007) Chemoinformatics: theory, practice and products. Springer, Dordrecht

    Google Scholar 

  5. Oprea T (2005) Chemoinformatics in drug discovery. Weinheim, Wiley-VCH Verlag GmbH & Co. KGaA

    Book  Google Scholar 

  6. Cruz-Monteagudo M, Ancede-Gallardo E, Jorge M, Cordeiro MNDS (2013) Chemoinformatics profiling of ionic liquids – automatic and chemically interpretable cytotoxicity profiling, virtual screening, and cytotoxicophore identification. Toxicol Sci 136:548–565

    Article  CAS  PubMed  Google Scholar 

  7. Gonzalez-Durruthy M, Alberici LC, Curti C, Naal Z, Atique-Sawazaki DT, Vazquez-Naya JM, Gonzalez-Diaz H, Munteanu CR (2017) Experimental-computational study of carbon nanotube effects on mitochondrial respiration: in silico nano-QSPR machine learning models based on New Raman spectra transform with Markov-Shannon entropy invariants. J Chem Inf Model 57:1029–1044

    Article  CAS  PubMed  Google Scholar 

  8. Duardo-Sanchez A, Munteanu CR, Riera-Fernandez P, Lopez-Diaz A, Pazos A, Gonzalez-Diaz H (2013) Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors. J Chem Inf Model 54:16–29

    Article  PubMed  CAS  Google Scholar 

  9. Gonzalez-Diaz H, Arrasate S, Gomez-SanJuan A, Sotomayor N, Lete E, Besada-Porto L, Ruso JM (2013) General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry. Curr Top Med Chem 13:1713–1741

    Article  CAS  PubMed  Google Scholar 

  10. Gonzalez-Diaz H, Riera-Fernandez P, Pazos A, Munteanu CR (2013) The Rucker-Markov invariants of complex bio-systems: applications in parasitology and neuroinformatics. Biosystems 111:199–207

    Article  PubMed  Google Scholar 

  11. Gonzalez-Diaz H, Arrasate S, Juan AG, Sotomayor N, Lete E, Speck-Planche A, Ruso JM, Luan F, Cordeiro MNDS (2014) Matrix trace operators: from spectral moments of molecular graphs and complex networks to perturbations in synthetic reactions, micelle nanoparticles, and drug ADME processes. Curr Drug Metab 15:470–488

    Article  CAS  PubMed  Google Scholar 

  12. He L, Xiao K, Zhou C, Li G, Yang H, Li Z, Cheng J (2019) Insights into pesticide toxicity against aquatic organism: QSTR models on Daphnia Magna. Ecotoxicol Environ Saf 173:285–292

    Article  CAS  PubMed  Google Scholar 

  13. Toropov AA, Toropova AP, Marzo M, Dorne JL, Georgiadis N, Benfenati E (2017) QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA’s OpenFoodTox database. Environ Toxicol Pharmacol 53:158–163

    Article  CAS  PubMed  Google Scholar 

  14. Basant N, Gupta S, Singh KP (2016) Modeling the toxicity of chemical pesticides in multiple test species using local and global QSTR approaches. Toxicol Res (Camb) 5:340–353

    Article  CAS  Google Scholar 

  15. Basant N, Gupta S, Singh KP (2015) Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches. Chemosphere 139:246–255

    Article  CAS  PubMed  Google Scholar 

  16. Basant N, Gupta S, Singh KP (2015) Predicting toxicities of diverse chemical pesticides in multiple avian species using tree-based QSAR approaches for regulatory purposes. J Chem Inf Model 55:1337–1348

    Article  CAS  PubMed  Google Scholar 

  17. Hamadache M, Benkortbi O, Hanini S, Amrane A, Khaouane L, Si Moussa C (2016) A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: validation, domain of application and prediction. J Hazard Mater 303:28–40

    Article  CAS  PubMed  Google Scholar 

  18. Simon-Vidal L, Garcia-Calvo O, Oteo U, Arrasate S, Lete E, Sotomayor N, Gonzalez-Diaz H (2018) Perturbation-Theory and Machine Learning (PTML) model for high-throughput screening of parham reactions: experimental and theoretical studies. J Chem Inf Model 58:1384–1396

    Article  CAS  PubMed  Google Scholar 

  19. Aranzamendi E, Arrasate S, Sotomayor N, Gonzalez-Diaz H, Lete E (2016) Chiral bronsted acid-catalyzed enantioselective alpha-amidoalkylation reactions: a Joint Experimental and Predictive Study. ChemistryOpen 5:540–549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Blay V, Yokoi T, Gonzalez-Diaz H (2018) Perturbation theory-machine learning study of zeolite materials desilication. J Chem Inf Model 58:2414–2419

    Article  CAS  PubMed  Google Scholar 

  21. Gonzalez-Durruthy M, Werhli AV, Seus V, Machado KS, Pazos A, Munteanu CR, Gonzalez-Diaz H, Monserrat JM (2017) Decrypting strong and weak single-walled carbon nanotubes interactions with mitochondrial voltage-dependent anion channels using molecular docking and perturbation theory. Sci Rep 7:13271

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Concu R, Kleandrova VV, Speck-Planche A, Cordeiro M (2017) Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory. Nanotoxicology 11:891–906

    Article  CAS  PubMed  Google Scholar 

  23. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2015) Computational modeling in nanomedicine: prediction of multiple antibacterial profiles of nanoparticles using a quantitative structure-activity relationship perturbation model. Nanomedicine (Lond) 10:193–204

    Article  CAS  Google Scholar 

  24. Luan F, Kleandrova VV, Gonzalez-Diaz H, Ruso JM, Melo A, Speck-Planche A, Cordeiro MNDS (2014) Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. Nanoscale 6:10623–10630

    Article  CAS  PubMed  Google Scholar 

  25. Kleandrova VV, Luan F, Gonzalez-Diaz H, Ruso JM, Speck-Planche A, Cordeiro MNDS (2014) Computational tool for risk assessment of nanomaterials: novel QSTR-perturbation model for simultaneous prediction of ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under multiple experimental conditions. Environ Sci Technol 48:14686–14694

    Article  CAS  PubMed  Google Scholar 

  26. Kleandrova VV, Luan F, Gonzalez-Diaz H, Ruso JM, Melo A, Speck-Planche A, Cordeiro MNDS (2014) Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. Environ Int 73C:288–294

    Article  CAS  Google Scholar 

  27. Ferreira da Costa J, Silva D, Caamano O, Brea JM, Loza MI, Munteanu CR, Pazos A, Garcia-Mera X, Gonzalez-Diaz H (2018) Perturbation theory/machine learning model of ChEMBL data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics. ACS Chem Nerosci 9:2572–2587

    Article  CAS  Google Scholar 

  28. Abeijon P, Garcia-Mera X, Caamano O, Yanez M, Lopez-Castro E, Romero-Duran FJ, Gonzalez-Diaz H (2017) Multi-target mining of Alzheimer disease proteome with Hansch’s QSBR-perturbation theory and experimental-theoretic study of new thiophene isosters of rasagiline. Curr Drug Targets 18:511–521

    Article  CAS  PubMed  Google Scholar 

  29. Romero-Duran FJ, Alonso N, Yanez M, Caamano O, Garcia-Mera X, Gonzalez-Diaz H (2016) Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives. Neuropharmacology 103:270–278

    Article  CAS  PubMed  Google Scholar 

  30. Speck-Planche A, Luan F, Cordeiro MNDS (2012) Role of ligand-based drug design methodologies toward the discovery of new anti-Alzheimer agents: futures perspectives in Fragment-Based Ligand Design. Curr Med Chem 19:1635–1645

    Article  CAS  PubMed  Google Scholar 

  31. Molina E, Sobarzo-Sanchez E, Speck-Planche A, Matos MJ, Uriarte E, Santana L, Yanez M, Orallo F (2012) Monoamino oxidase a: an interesting pharmacological target for the development of multi-target QSAR. Mini Rev Med Chem 12:947–958

    Article  CAS  PubMed  Google Scholar 

  32. Bediaga H, Arrasate S, Gonzalez-Diaz H (2018) PTML combinatorial model of ChEMBL compounds assays for multiple types of cancer. ACS Comb Sci 20:621–632

    Article  CAS  PubMed  Google Scholar 

  33. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2013) Unified multi-target approach for the rational in silico design of anti-bladder cancer agents. Anticancer Agents Med Chem 13:791–800

    Article  CAS  PubMed  Google Scholar 

  34. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2012) Chemoinformatics in multi-target drug discovery for anti-cancer therapy: in silico design of potent and versatile anti-brain tumor agents. Anticancer Agents Med Chem 12:678–685

    Article  CAS  PubMed  Google Scholar 

  35. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2012) Chemoinformatics in anti-cancer chemotherapy: multi-target QSAR model for the in silico discovery of anti-breast cancer agents. Eur J Pharm Sci 47:273–279

    Article  CAS  PubMed  Google Scholar 

  36. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2012) Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorg Med Chem 20:4848–4855

    Article  CAS  PubMed  Google Scholar 

  37. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2011) Multi-target drug discovery in anti-cancer therapy: fragment-based approach toward the design of potent and versatile anti-prostate cancer agents. Bioorg Med Chem 19:6239–6244

    Article  CAS  PubMed  Google Scholar 

  38. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2011) Fragment-based QSAR model toward the selection of versatile anti-sarcoma leads. Eur J Med Chem 46:5910–5916

    Article  CAS  PubMed  Google Scholar 

  39. Martinez-Arzate SG, Tenorio-Borroto E, Barbabosa Pliego A, Diaz-Albiter HM, Vazquez-Chagoyan JC, Gonzalez-Diaz H (2017) PTML model for proteome mining of B-cell epitopes and theoretical-experimental study of Bm86 protein sequences from Colima. Mexico J Proteome Res 16:4093–4103

    Article  CAS  PubMed  Google Scholar 

  40. Tenorio-Borroto E, Ramirez FR, Speck-Planche A, Cordeiro MNDS, Luan F, Gonzalez-Diaz H (2014) QSPR and flow cytometry analysis (QSPR-FCA): review and new findings on parallel study of multiple interactions of chemical compounds with immune cellular and molecular targets. Curr Drug Metab 15:414–428

    Article  CAS  PubMed  Google Scholar 

  41. Tenorio-Borroto E, Penuelas-Rivas CG, Vasquez-Chagoyan JC, Castanedo N, Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H (2014) Model for high-throughput screening of drug immunotoxicity – Study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur J Med Chem 72:206–220

    Article  CAS  PubMed  Google Scholar 

  42. Herrera-Ibata DM, Pazos A, Orbegozo-Medina RA, Romero-Duran FJ, Gonzalez-Diaz H (2015) Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties. Biosystems 132–133:20–34

    Article  PubMed  Google Scholar 

  43. Herrera-Ibata DM, Orbegozo-Medina RA, Gonzalez-Diaz H (2015) Multiscale mapping of AIDS in U.S. countries vs anti-HIV drugs activity with complex networks and information indices. Curr Bioinform 10:639–657

    Article  CAS  Google Scholar 

  44. Gonzalez-Diaz H, Herrera-Ibata DM, Duardo-Sanchez A, Munteanu CR, Orbegozo-Medina RA, Pazos A (2014) ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks. J Chem Inf Model 54:744–755

    Article  CAS  PubMed  Google Scholar 

  45. Speck-Planche A, Cordeiro MNDS (2014) Review of current chemoinformatic tools for modeling important aspects of CYPs-mediated drug metabolism. Integrating metabolism data with other biological profiles to enhance drug discovery. Curr Drug Metab 15:429–440

    Article  CAS  PubMed  Google Scholar 

  46. Speck-Planche A, Kleandrova VV, Cordeiro MNDS (2013) New insights toward the discovery of antibacterial agents: multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs. Eur J Pharm Sci 48:812–818

    Article  CAS  PubMed  Google Scholar 

  47. Speck-Planche A, Cordeiro MNDS (2014) Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: a chemoinformatic complementary approach for high-throughput screening. ACS Comb Sci 16:78–84

    Article  CAS  PubMed  Google Scholar 

  48. Speck-Planche A, Kleandrova VV, Ruso JM, Cordeiro MNDS (2016) First multitarget chemo-bioinformatic model to enable the discovery of antibacterial peptides against multiple Gram-positive pathogens. J Chem Inf Model 56:588–598

    Article  CAS  PubMed  Google Scholar 

  49. Speck-Planche A, Cordeiro MNDS (2017) Speeding up early drug discovery in antiviral research: a fragment-based in silico approach for the design of virtual anti-hepatitis C leads. ACS Comb Sci 19:501–512

    Article  CAS  PubMed  Google Scholar 

  50. Speck-Planche A, Cordeiro MNDS, Guilarte-Montero L, Yera-Bueno R (2011) Current computational approaches towards the rational design of new insecticidal agents. Curr Comput Aided Drug Des 7:304–314

    Article  CAS  PubMed  Google Scholar 

  51. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS (2012) Predicting multiple ecotoxicological profiles in agrochemical fungicides: a multi-species chemoinformatic approach. Ecotoxicol Environ Saf 80:308–313

    Article  CAS  PubMed  Google Scholar 

  52. Speck-Planche A, Kleandrova VV, Scotti MT (2012) Fragment-based approach for the in silico discovery of multi-target insecticides. Chemom Intel Lab Syst 111:39–45

    Article  CAS  Google Scholar 

  53. Perez Gonzalez M, Gonzalez Diaz H, Molina Ruiz R, Cabrera MA, Ramos de Armas R (2003) TOPS-MODE based QSARs derived from heterogeneous series of compounds Applications to the design of new herbicides. J Chem Inf Comput Sci 43:1192–1199

    Article  CAS  PubMed  Google Scholar 

  54. EPA. OPP pesticide ecotoxicity database. Access Date: 28 Feb 2019. Available from: www.ipmcenters.org/ecotox/

  55. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR (2011) Open Babel: an open chemical toolbox. J Cheminform 3:33

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Valdes-Martini JR, Marrero-Ponce Y, Garcia-Jacas CR, Martinez-Mayorga K, Barigye SJ, Vaz d’Almeida YS, Pham-The H, Perez-Gimenez F, Morell CA (2017) QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations. J Cheminform 9:35

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Medina Marrero R, Marrero-Ponce Y, Barigye SJ, Echeverria Diaz Y, Acevedo-Barrios R, Casanola-Martin GM, Garcia Bernal M, Torrens F, Perez-Gimenez F (2015) QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents. SAR QSAR Environ Res 26:943–958

    Article  CAS  PubMed  Google Scholar 

  58. Marrero-Ponce Y, Siverio-Mota D, Galvez-Llompart M, Recio MC, Giner RM, Garcia-Domenech R, Torrens F, Aran VJ, Cordero-Maldonado ML, Esguera CV, de Witte PA, Crawford AD (2011) Discovery of novel anti-inflammatory drug-like compounds by aligning in silico and in vivo screening: the nitroindazolinone chemotype. Eur J Med Chem 46:5736–5753

    Article  CAS  PubMed  Google Scholar 

  59. Montero-Torres A, Garcia-Sanchez RN, Marrero-Ponce Y, Machado-Tugores Y, Nogal-Ruiz JJ, Martinez-Fernandez AR, Aran VJ, Ochoa C, Meneses-Marcel A, Torrens F (2006) Non-stochastic quadratic fingerprints and LDA-based QSAR models in hit and lead generation through virtual screening: theoretical and experimental assessment of a promising method for the discovery of new antimalarial compounds. Eur J Med Chem 41:483–493

    Article  CAS  PubMed  Google Scholar 

  60. Marrero-Ponce Y, Medina-Marrero R, Torrens F, Martinez Y, Romero-Zaldivar V, Castro EA (2005) Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity. Bioorg Med Chem 13:2881–2899

    Article  CAS  PubMed  Google Scholar 

  61. Speck-Planche A, Cordeiro MNDS (2014) Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents. Future Med Chem 6:2013–2028

    Article  CAS  PubMed  Google Scholar 

  62. Urias RW, Barigye SJ, Marrero-Ponce Y, Garcia-Jacas CR, Valdes-Martini JR, Perez-Gimenez F (2015) IMMAN: free software for information theory-based chemometric analysis. Mol Divers 19:305–319

    Article  CAS  PubMed  Google Scholar 

  63. Pearson K (1895) Notes on regression and inheritance in the case of two parents. Proc R Soc Lond 58:240–242

    Article  Google Scholar 

  64. Statsoft-Team (2001) STATISTICA. Data analysis software system. v6.0. Tulsa

    Google Scholar 

  65. Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405:442–451

    Article  CAS  PubMed  Google Scholar 

  66. Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17:4791–4810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Speck-Planche A (2018) Combining ensemble learning with a fragment-based topological approach to generate new molecular diversity in drug discovery: in silico design of Hsp90 inhibitors. ACS Omega 3:14704–14716

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Speck-Planche A, Kleandrova VV (2012) QSAR and molecular docking techniques for the discovery of potent monoamine oxidase B inhibitors: computer-aided generation of new rasagiline bioisosteres. Curr Top Med Chem 12:1734–1747

    Article  CAS  PubMed  Google Scholar 

  69. Speck-Planche A (2019) Multicellular target QSAR model for simultaneous prediction and design of anti-pancreatic cancer agents. ACS Omega 4:3122–3132

    Article  CAS  Google Scholar 

  70. Baskin II, Skvortsova MI, Stankevich IV, Zefirov NS (1995) On the basis of invariants of labeled molecular graphs. J Chem Inf Comput Sci 35:527–531

    Article  CAS  Google Scholar 

  71. Speck-Planche A, Cordeiro MNDS (2017) De novo computational design of compounds virtually displaying potent antibacterial activity and desirable in vitro ADMET profiles. Med Chem Res 26:2345–2356

    Article  CAS  Google Scholar 

  72. Ghose AK, Crippen GM (1986) Atomic physicochemical parameters for three-dimensional structure-directed quantitative structure-activity relationships I. Partition coefficients as a measure of hydrophobicity. J Comput Chem 7:565–577

    Article  CAS  Google Scholar 

  73. Ghose AK, Crippen GM (1987) Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. J Chem Inf Comput Sci 27:21–35

    Article  CAS  PubMed  Google Scholar 

  74. Ghose AK, Pritchett A, Crippen GM (1988) Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships III: modeling hydrophobic interactions. J Comput Chem 9:80–90

    Article  CAS  Google Scholar 

  75. Viswanadhan VN, Ghose AK, Revankar GR, Robins RK (1989) Atomic physicochemical parameters for three dimensional structure directed quantitative structure-activity relationships. 4. Additional parameters for hydrophobic and dispersive interactions and their application for an automated superposition of certain naturally occurring nucleoside antibiotics. J Chem Inf Comput Sci 29:163–172

    Article  CAS  Google Scholar 

Download references

Acknowledgments

Speck-Planche acknowledges the financial support provided by the I.M. Sechenov First Moscow State Medical University under the agreement № У-187.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

1 Electronic Supplementary Material

1

Electronic Supplementary Material 1 (XLSX 7904 kb)

2

Electronic Supplementary Material 2 (XLSX 1072 kb)

3

Electronic Supplementary Material 3 (XLSX 43 kb)

4

Electronic Supplementary Material 4 (XLSX 552 kb)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Speck-Planche, A. (2020). Multi-scale QSAR Approach for Simultaneous Modeling of Ecotoxic Effects of Pesticides. In: Roy, K. (eds) Ecotoxicological QSARs. Methods in Pharmacology and Toxicology. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0150-1_26

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0150-1_26

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0149-5

  • Online ISBN: 978-1-0716-0150-1

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics