# Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem

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## Abstract

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## 1. Introduction

## 2. Results

## 3. Discussion

## 4. Materials and Methods

#### 4.1. Data Set and Chemical Representation

#### 4.2. Machine Learning Methods

^{1}-norm penalty). The hyperparameters to be optimized are usually the regression coefficients (weights, bias) and the penalty. RF is an ensemble classifier. Ensemble classification algorithms are following a paradigm where multiple “weak classifiers” are trained and aggregated to improve the prediction capabilities and lower the prediction error. The weak learners here are decision trees and the aggregation is conducted by means of bootstrapping (each tree trained on a part of data and subset of features) and final voting. RF is considered a non-linear method. The hyperparameter for RF can be large and complex. Commonly optimized hyperparameters are tree depth, number of trees, class-weights, and the number of features utilized. The MLP is a fully-connected neural network. Neural networks machine learning algorithm where multiple learners are connected in layers. The learners (neurons) learn parameters (weights, bias) from the data and are “activated” by means of a non-linear function such as the sigmoid function. Hyperparameters which are commonly optimized in MLP are the number of layers, penalty function, learning rate and activation function.

_{2}[30] expressed, which is named here Real-Accuracy (RA) and defined by Equations (2) and (3):

#### 4.3. Modelling

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Sample Availability

## Appendix A

## References

- Williams, A.J.; Grulke, C.M.; Edwards, J.; McEachran, A.D.; Mansouri, K.; Baker, N.C.; Patlewicz, G.; Shah, I.; Wambaugh, J.F.; Judson, R.S.; et al. The CompTox Chemistry Dashboard: A community data resource for environmental chemistry. J. Cheminform.
**2017**, 9, 1–27. [Google Scholar] [CrossRef] [PubMed] - Morger, A.; Mathea, M.; Achenbach, J.H.; Wolf, A.; Buesen, R.; Schleifer, K.J.; Landsiedel, R.; Volkamer, A. KnowTox: Pipeline and case study for confident prediction of potential toxic effects of compounds in early phases of development. J. Cheminform.
**2020**, 12, 1–17. [Google Scholar] [CrossRef][Green Version] - Thomas, R.S.; Bahadori, T.; Buckley, T.J.; Cowden, J.; Dionisio, K.L.; Frithsen, J.B.; Grulke, C.M.; Maureen, R.; Harrill, J.A.; Higuchi, M.; et al. The next generation blueprint of computational toxicology at the U.S. Environmental Protection Agency. Toxicol. Sci.
**2020**, 169, 1–29. [Google Scholar] [CrossRef][Green Version] - Padilla, S.; Corum, D.; Padnos, B.; Hunter, D.L.; Beam, A.; Houck, K.A.; Sipes, N.; Kleinstreuer, N.; Knudsen, T.; Dix, D.J.; et al. Zebrafish developmental screening of the ToxCastTM Phase I chemical library. Reprod. Toxicol.
**2012**, 33, 174–187. [Google Scholar] [CrossRef] - Noyes, P.D.; Garcia, G.R.; Tanguay, R.L. Zebrafish as an: In vivo model for sustainable chemical design. Green Chem.
**2016**, 18, 6410–6430. [Google Scholar] [CrossRef] [PubMed][Green Version] - Pham, D.H.; De Roo, B.; Nguyen, X.B.; Vervaele, M.; Kecskés, A.; Ny, A.; Copmans, D.; Vriens, H.; Locquet, J.P.; Hoet, P.; et al. Use of Zebrafish Larvae as a Multi-Endpoint Platform to Characterize the Toxicity Profile of Silica Nanoparticles. Sci. Rep.
**2016**, 6, 1–13. [Google Scholar] [CrossRef][Green Version] - Ducharme, N.A.; Peterson, L.E.; Benfenati, E.; Reif, D.; McCollum, C.W.; Gustafsson, J.Å.; Bondesson, M. Meta-analysis of toxicity and teratogenicity of 133 chemicals from zebrafish developmental toxicity studies. Reprod. Toxicol.
**2013**, 41, 98–108. [Google Scholar] [CrossRef] - Klüver, N.; Vogs, C.; Altenburger, R.; Escher, B.I.; Scholz, S. Development of a general baseline toxicity QSAR model for the fish embryo acute toxicity test. Chemosphere
**2016**, 164, 164–173. [Google Scholar] [CrossRef] [PubMed] - Liu, T.; Yan, F.; Jia, Q.; Wang, Q. Norm index-based QSAR models for acute toxicity of organic compounds toward zebrafish embryo. Ecotoxicol. Environ. Saf.
**2020**, 203, 110946. [Google Scholar] [CrossRef] - Qiao, K.; Fu, W.; Jiang, Y.; Chen, L.; Li, S.; Ye, Q.; Gui, W. QSAR models for the acute toxicity of 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos. Environ. Pollut.
**2020**, 265, 114837. [Google Scholar] [CrossRef] - Ghorbanzadeh, M.; Zhang, J.; Andersson, P.L. Binary classification model to predict developmental toxicity of industrial chemicals in zebrafish. J. Chemom.
**2016**, 30, 298–307. [Google Scholar] [CrossRef] - Lavado, G.J.; Gadaleta, D.; Toma, C.; Golbamaki, A.; Toropov, A.A.; Toropova, A.P.; Marzo, M.; Baderna, D.; Arning, J.; Benfenati, E. Zebrafish AC50 modelling: (Q)SAR models to predict developmental toxicity in zebrafish embryo. Ecotoxicol. Environ. Saf.
**2020**, 202, 110936. [Google Scholar] [CrossRef] [PubMed] - Toropov, A.A.; Toropova, A.P.; Benfenati, E. The index of ideality of correlation: QSAR model of acute toxicity for zebrafish (Danio rerio) embryo. Int. J. Environ. Res.
**2019**, 13, 387–394. [Google Scholar] [CrossRef] - Malev, O.; Lovrić, M.; Stipaničev, D.; Repec, S.; Martinović-Weigelt, D.; Zanella, D.; Ivanković, T.; Đuretec, V.S.; Barišić, J.; Li, M.; et al. Toxicity prediction and effect characterization of 90 pharmaceuticals and illicit drugs measured in plasma of fish from a major European river (Sava, Croatia). Environ. Pollut.
**2020**, 115162. [Google Scholar] [CrossRef] - Babić, S.; Barišić, J.; Stipaničev, D.; Repec, S.; Lovrić, M.; Malev, O.; Martinović-Weigelt, D.; Čož-Rakovac, R.; Klobučar, G. Assessment of river sediment toxicity: Combining empirical zebrafish embryotoxicity testing with in silico toxicity characterization. Sci. Total Environ.
**2018**, 643, 435–450. [Google Scholar] [CrossRef] [PubMed] - Henn, K.; Braunbeck, T. Dechorionation as a tool to improve the fish embryo toxicity test (FET) with the zebrafish (Danio rerio). Comp. Biochem. Physiol. C Toxicol. Pharmacol.
**2011**, 153, 91–98. [Google Scholar] [CrossRef] [PubMed] - Nishimura, Y.; Inoue, A.; Sasagawa, S.; Koiwa, J.; Kawaguchi, K.; Kawase, R.; Maruyama, T.; Kim, S.; Tanaka, T. Using zebrafish in systems toxicology for developmental toxicity testing. Congenit. Anom.
**2016**, 56, 18–27. [Google Scholar] [CrossRef] - Truong, L.; Reif, D.M.; Mary, L.S.; Geier, M.C.; Truong, H.D.; Tanguay, R.L. Multidimensional in vivo hazard assessment using zebrafish. Toxicol. Sci.
**2014**, 137, 212–233. [Google Scholar] [CrossRef] [PubMed][Green Version] - Villalobos, S.A.; Hamm, J.T.; Teh, S.J.; Hinton, D.E. Thiobencarb-induced embryotoxicity in medaka (Oryzias latipes): Stage- specific toxicity and the protective role of chorion. Aquat. Toxicol.
**2000**, 48, 309–326. [Google Scholar] [CrossRef] - Scholz, S.; Klüver, N.; Kühne, R. Analysis of the Relevance and Adequateness of Using Fish Embryo Acute Toxicity (FET) Test Guidance (OECD 236) to Fulfil the Information Requirements and Addressing Concerns under REACH; European Chemicals Agency: Helsinki, Finland, 2016. [Google Scholar]
- Panzica-Kelly, J.M.; Zhang, C.X.; Augustine-Rauch, K.A. Optimization and performance assessment of the chorion-off [Dechorinated] Zebrafish Developmental toxicity assay. Toxicol. Sci.
**2015**, 146, 127–134. [Google Scholar] [CrossRef] [PubMed][Green Version] - Tran, C.M.; Lee, H.; Lee, B.; Ra, J.S.; Kim, K.T. Effects of the chorion on the developmental toxicity of organophosphate esters in zebrafish embryos. J. Hazard. Mater.
**2021**, 401, 123389. [Google Scholar] [CrossRef] - Golbraikh, A.; Muratov, E.; Fourches, D.; Tropsha, A. Data set modelability by QSAR. J. Chem. Inf. Model.
**2014**, 54, 1–4. [Google Scholar] [CrossRef][Green Version] - Marcou, G.; Horvath, D.; Varnek, A. Kernel Target Alignment Parameter: A New Modelability Measure for Regression Tasks. J. Chem. Inf. Model.
**2016**, 56, 6–11. [Google Scholar] [CrossRef] - Ruiz, I.L.; Gómez-Nieto, M.Á. Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes. Molecules
**2018**, 23, 2756. [Google Scholar] [CrossRef][Green Version] - Thomas, R.S.; Black, M.B.; Li, L.; Healy, E.; Chu, T.M.; Bao, W.; Andersen, M.E.; Wolfinger, R.D. A comprehensive statistical analysis of predicting in vivo hazard using high-throughput in vitro screening. Toxicol. Sci.
**2012**, 128, 398–417. [Google Scholar] [CrossRef] [PubMed][Green Version] - Ruiz, I.L.; Gómez-Nieto, M.Á. Study of Data Set Modelability: Modelability, Rivality, and Weighted Modelability Indexes. J. Chem. Inf. Model.
**2018**, 58, 1798–1814. [Google Scholar] [CrossRef] [PubMed] - Boughorbel, S.; Jarray, F.; El-Anbari, M. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE
**2017**, 12. [Google Scholar] [CrossRef] [PubMed] - Czodrowski, P. Count on kappa. J. Comput. Aided. Mol. Des.
**2014**, 28, 1049–1055. [Google Scholar] [CrossRef] - Lučić, B.; Batista, J.; Bojović, V.; Lovrić, M.; Sović Kržić, A.; Bešlo, D.; Nadramija, D.; Vikić-Topić, D. Estimation of Random Accuracy and its Use in Validation of Predictive Quality of Classification Models within Predictive Challenges. Croat. Chem. Acta
**2019**, 92. [Google Scholar] [CrossRef][Green Version] - Kurosaki, K.; Wu, R.; Uesawa, Y. A toxicity prediction tool for potential agonist/antagonist activities in molecular initiating events based on chemical structures. Int. J. Mol. Sci.
**2020**, 21, 7853. [Google Scholar] [CrossRef] - Rácz, A.; Bajusz, D.; Héberger, K. Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification. Molecules
**2021**, 26, 1111. [Google Scholar] [CrossRef] - Abdelaziz, A.; Spahn-Langguth, H.; Schramm, K.W.; Tetko, I.V. Consensus modeling for HTS assays using in silico descriptors calculates the best balanced accuracy in Tox21 challenge. Front. Environ. Sci.
**2016**, 4, 1–12. [Google Scholar] [CrossRef][Green Version] - Idakwo, G.; Thangapandian, S.; Luttrell, J.; Li, Y.; Wang, N.; Zhou, Z.; Hong, H.; Yang, B.; Zhang, C.; Gong, P. Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets. J. Cheminform.
**2020**, 12, 1–19. [Google Scholar] [CrossRef] - Hemmerich, J.; Asilar, E.; Ecker, G.F. Conformational Oversampling as Data Augmentation for Molecules. In Artificial Neural Networks and Machine Learning–ICANN 2019: Workshop and Special Sessions; Tetko, I., Kůrková, V., Karpov, P., Theis, F., Eds.; Springer: Cham, Switzerland; New York, NY, USA, 2019; Volume 11731. [Google Scholar] [CrossRef][Green Version]
- Fernandez, M.; Ban, F.; Woo, G.; Hsing, M.; Yamazaki, T.; Leblanc, E.; Rennie, P.S.; Welch, W.J.; Cherkasov, A. Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images. J. Chem. Inf. Model.
**2018**, 58, 1533–1543. [Google Scholar] [CrossRef] [PubMed] - Mayr, A.; Klambauer, G.; Unterthiner, T.; Hochreiter, S. DeepTox: Toxicity prediction using deep learning. Front. Environ. Sci.
**2016**, 3. [Google Scholar] [CrossRef][Green Version] - Rogers, D.; Hahn, M. Extended-Connectivity Fingerprints. J. Chem. Inf. Model.
**2010**, 50, 742–754. [Google Scholar] [CrossRef] - Kausar, S.; Falcao, A.O. Analysis and comparison of vector space and metric space representations in QSAR modeling. Molecules
**2019**, 24, 1698. [Google Scholar] [CrossRef] [PubMed][Green Version] - Gütlein, M.; Kramer, S. Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability. J. Cheminform.
**2016**, 8, 1–16. [Google Scholar] [CrossRef] [PubMed][Green Version] - Landrum, G. RDKit: Colliding Bits III. Available online: http://rdkit.blogspot.com/2016/02/colliding-bits-iii.html (accessed on 23 December 2019).
- Žuvela, P.; Lovric, M.; Yousefian-Jazi, A.; Liu, J.J. Ensemble Learning Approaches to Data Imbalance and Competing Objectives in Design of an Industrial Machine Vision System. Ind. Eng. Chem. Res.
**2020**, 59, 4636–4645. [Google Scholar] [CrossRef] - Lovrić, M.; Pavlović, K.; Žuvela, P.; Spataru, A.; Lučić, B.; Kern, R.; Wong, M.W. Machine learning in prediction of intrinsic aqueous solubility of drug-like compounds: Generalization, complexity or predictive ability? chemrxiv
**2020**. [Google Scholar] [CrossRef] - Huang, R.; Xia, M.; Nguyen, D.-T.; Zhao, T.; Sakamuru, S.; Zhao, J.; Shahane, S.A.; Rossoshek, A.; Simeonov, A. Tox21Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs. Front. Environ. Sci.
**2016**, 3, 85. [Google Scholar] [CrossRef][Green Version] - Matsuzaka, Y.; Uesawa, Y. Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library. Molecules
**2020**, 25, 2764. [Google Scholar] [CrossRef] - Wang, Z.; Boulanger, L.; Berger, D.; Gaudreau, P.; Marrie, R.A.; Potter, B.; Wister, A.; Wolfson, C.; Lefebvre, G.; Sylvestre, M.P.; et al. Development and internal validation of a multimorbidity index that predicts healthcare utilisation using the Canadian Longitudinal Study on Aging. BMJ Open
**2020**, 10, 1–9. [Google Scholar] [CrossRef] [PubMed][Green Version] - Correlation and regression. Available online: https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/11-correlation-and-regression (accessed on 3 March 2021).
- Hulzebos, E.; Sijm, D.; Traas, T.; Posthumus, R.; Maslankiewicz, L. Validity and validation of expert (Q)SAR systems. SAR QSAR Environ. Res.
**2005**, 16, 385–401. [Google Scholar] [CrossRef] [PubMed] - Patlewicz, G.; Ball, N.; Booth, E.D.; Hulzebos, E.; Zvinavashe, E.; Hennes, C. Use of category approaches, read-across and (Q)SAR: General considerations. Regul. Toxicol. Pharmacol.
**2013**, 67, 1–12. [Google Scholar] [CrossRef] - Lo Piparo, E.; Worth, A. Review of QSAR Models and Software Tools for predicting Developmental and Reproductive Toxicity. JRC Rep. EUR
**2010**, 24522. [Google Scholar] [CrossRef] - Han, J.; Wang, Q.; Wang, X.; Li, Y.; Wen, S.; Liu, S.; Ying, G.; Guo, Y.; Zhou, B. The synthetic progestin megestrol acetate adversely affects zebrafish reproduction. Aquat. Toxicol.
**2014**, 150, 66–72. [Google Scholar] [CrossRef] - McGee, S.P.; Cooper, E.M.; Stapleton, H.M.; Volz, D.C. Early zebrafish embryogenesis is susceptible to developmental TDCPP exposure. Environ. Health Perspect.
**2012**, 120, 1585–1591. [Google Scholar] [CrossRef] - Wang, Q.; Liang, K.; Liu, J.; Yang, L.; Guo, Y.; Liu, C.; Zhou, B. Exposure of zebrafish embryos/larvae to TDCPP alters concentrations of thyroid hormones and transcriptions of genes involved in the hypothalamic-pituitary-thyroid axis. Aquat. Toxicol.
**2013**, 126, 207–213. [Google Scholar] [CrossRef] - Noyes, P.D.; Haggard, D.E.; Gonnerman, G.D.; Tanguay, R.L. Advanced morphological - behavioral test platform reveals neurodevelopmental defects in embryonic zebrafish exposed to comprehensive suite of halogenated and organophosphate flame retardants. Toxicol. Sci.
**2015**, 145, 177–195. [Google Scholar] [CrossRef][Green Version] - Wilson, L.B.; Truong, L.; Simonich, M.T.; Tanguay, R.L. Systematic Assessment of Exposure Variations on Observed Bioactivity in Zebrafish Chemical Screening. Toxics
**2020**, 8, 87. [Google Scholar] [CrossRef] - Mandrell, D.; Truong, L.; Jephson, C.; Sarker, M.R.; Moore, A.; Lang, C.; Simonich, M.T.; Tanguay, R.L. Automated zebrafish chorion removal and single embryo placement: Optimizing Throughput of zebrafish developmental toxicity screens. J. Lab. Autom.
**2012**, 17, 66–74. [Google Scholar] [CrossRef] [PubMed][Green Version] - Kim, K.-T.; Tanguay, R.L. The role of chorion on toxicity of silver nanoparticles in the embryonic zebrafish assay. Environ. Health Toxicol.
**2014**, 29, e2014021. [Google Scholar] [CrossRef] - Volz, D.C.; Hipszer, R.A.; Leet, J.K.; Raftery, T.D. Leveraging Embryonic Zebrafish to Prioritize ToxCast Testing. Environ. Sci. Technol. Lett.
**2015**, 2, 171–176. [Google Scholar] [CrossRef][Green Version] - Lovrić, M.; Molero, J.M.; Kern, R. PySpark and RDKit: Moving towards Big Data in Cheminformatics. Mol. Inform.
**2019**, 38. [Google Scholar] [CrossRef] [PubMed] - Fourches, D.; Muratov, E.; Tropsha, A. Trust, but verify: On the importance of chemical structure curation in cheminformatics and QSAR modeling research. J. Chem. Inf. Model.
**2010**, 50, 1189–1204. [Google Scholar] [CrossRef] [PubMed] - Lovrić, M. CompTox Zebrafish Developmental Toxicity Processed Data. 2021. Available online: https://zenodo.org/record/4400418#.YE619J0zaUk (accessed on 25 January 2021).
- Landrum, G. RDKit: Open-Source Cheminformatics Software. Available online: http://rdkit.org/ (accessed on 25 January 2021).
- Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning; Springer Series in Statistics; Springer: New York, NY, USA, 2009; ISBN 978-0-387-84857-0. [Google Scholar]
- Murtagh, F. Multilayer perceptrons for classification and regression. Neurocomputing
**1991**, 2, 183–197. [Google Scholar] [CrossRef] - Breiman, L. Random Forests. Mach. Learn.
**2001**, 45, 5–32. [Google Scholar] [CrossRef][Green Version] - Pedregosa, F.; Michel, V.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Vanderplas, J.; Cournapeau, D.; Varoquaux, G.; Gramfort, A.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res.
**2011**, 12, 2825–2830. [Google Scholar] [CrossRef] - Mansouri, K.; Kleinstreuer, N.; Abdelaziz, A.M.; Alberga, D.; Alves, V.M.; Andersson, P.L.; Andrade, C.H.; Bai, F.; Balabin, I.; Ballabio, D.; et al. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. Environ. Health Perspect.
**2020**, 128, 027002. [Google Scholar] [CrossRef] - He, H.; Garcia, E.A. Learning from imbalanced data. IEEE Trans. Knowl. Data Eng.
**2009**, 21, 1263–1284. [Google Scholar] [CrossRef] - Snoek, J.; Larochelle, H.; Adams, R.P. Practical Bayesian Optimization of Machine Learning Algorithms. In Proceedings of the 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012, Lake Tahoe, NV, USA, 3–6 December 2012; Advances in Neural Information Processing Systems: Lake Tahoe, NV, USA, 2012; Volume 4, pp. 2951–2959. [Google Scholar]
- Lovric, M.; Banic, I.; Lacic, E.; Kern, R.; Pavlovic, K.; Turkalj, M. Predicting treatment outcomes using explainable machine learning in children with asthma. Authorea Prepr.
**2020**. [Google Scholar] [CrossRef]

**Figure 1.**Scatter plots of values of four model quality parameters against MCC corresponding to 209 models on the respective test sets (

**a**) Real Accuracy, (

**b**) Cohen’s Kappa, (

**c**) Accuracy and (

**d**) Balanced Accuracy.

**Figure 2.**Boxplot diagrams of MCC CV values for the training set (

**a**) and MCC Test values for the test set (

**b**) for 209 models generated for 19 endpoints (on the X-axis). The threshold MCC value of 0.20 is marked by the dashed horizontal line. Median value of quality metrics for each endpoint is given by horizontal line in each box.

**Figure 3.**Structural fragments presented by fingerprints utilized in the final model for the JAW endpoint. The purple circle denotes the center of the fingerprint with a radius which involves atoms denoted by the yellow-colored circles. The asterisk denotes a continuation of the structure.

**Table 1.**Pearson correlation coefficients between quality metrics obtained for the test set across the 209 models.

Real Accuracy | MCC | Cohen’s Kappa | Accuracy | Balanced Accuracy | |
---|---|---|---|---|---|

Real Accuracy | 1 | 0.84 | 0.86 | −0.39 | −0.28 |

MCC | 0.84 | 1 | 0.97 | −0.24 | −0.19 |

Cohen’s Kappa | 0.86 | 0.97 | 1 | −0.18 | −0.21 |

Accuracy | −0.39 | −0.24 | −0.18 | 1 | 0.59 |

Balanced Accuracy | −0.28 | −0.19 | −0.21 | 0.59 | 1 |

**Table 2.**Data set overview sorted by the number of active compounds per endpoint. All endpoints are binary variables having only values 1 or 0 (active or inactive). The number of missing data in each endpoint is given in the last column (“missing”).

Endpoint | Negative (0) | Positive (1) | Missing Values |
---|---|---|---|

AXIS | 882 | 108 | 28 |

ActivityScore | 812 | 187 | 19 |

BRAI | 930 | 60 | 28 |

CFIN | 942 | 48 | 28 |

CIRC | 972 | 18 | 28 |

EYE | 913 | 77 | 28 |

JAW | 881 | 109 | 28 |

MORT | 884 | 115 | 19 |

NC | 977 | 13 | 28 |

OTIC | 949 | 41 | 28 |

PE | 874 | 116 | 28 |

PFIN | 936 | 54 | 28 |

PIG | 945 | 45 | 28 |

SNOU | 883 | 107 | 28 |

SOMI | 952 | 38 | 28 |

SWIM | 958 | 32 | 28 |

TRUN | 934 | 56 | 28 |

TR | 912 | 78 | 28 |

YSE | 867 | 123 | 28 |

Positive (Model) (1) | Negative (Model) (0) | |
---|---|---|

Positive (Experimental) (1) | TP | FN |

Negative (Experimental) (0) | FP | TN |

Classifier | Feature Set | * Scaling | ** Feat. Sel. | Endpoints |
---|---|---|---|---|

Logistic regression | Fingerprints | No | No | 19 |

Multilayer perceptron | Fingerprints | No | No | 19 |

Random forest | Descriptors | No | No | 19 |

Random forest | Descriptors | No | Yes | 19 |

Random forest | Fingerprints | No | No | 19 |

Logistic regression | Descriptors | Yes | No | 19 |

Logistic regression | Descriptors | Yes | Yes | 19 |

Multilayer perceptron | Descriptors | Yes | No | 19 |

Multilayer perceptron | Descriptors | Yes | Yes | 19 |

Random forest | Descriptors | Yes | No | 19 |

Random forest | Descriptors | Yes | Yes | 19 |

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**MDPI and ACS Style**

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* **2021**, *26*, 1617.
https://doi.org/10.3390/molecules26061617

**AMA Style**

Lovrić M, Malev O, Klobučar G, Kern R, Liu JJ, Lučić B. Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem. *Molecules*. 2021; 26(6):1617.
https://doi.org/10.3390/molecules26061617

**Chicago/Turabian Style**

Lovrić, Mario, Olga Malev, Göran Klobučar, Roman Kern, Jay J. Liu, and Bono Lučić. 2021. "Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem" *Molecules* 26, no. 6: 1617.
https://doi.org/10.3390/molecules26061617