In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Data Collection
2.2. Generation of Descriptors
2.3. QSAR Modeling and Validation
Support Vector Regression and Ensemble Model
2.4. Analysis of Descriptors in Models
3. Results
3.1. Distribution of Molecular Weights and Toxicity
3.2. Ensemble Model
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method/Model | Runtime Parameters |
---|---|
SVR_A and SVR_B | Kernel = ’rbf’, degree = 3, gamma = ’auto’, coef 0 = 0.0, tol = 0.001, C = 5.0, epsilon = 0.1, shrinking = Ture, cache_size = 200, verbose = False, max_iter = −1 |
MLR | Fir_intercept = True, normalize = ’False’, copy_X = True, n_jobs = −1, positive = False |
Parameters | Regression Model | Ensemble Model | |
---|---|---|---|
SVR_A | SVR_B | ||
No. of descriptors | 11 | 8 | _ |
R2 (training) | 0.83 | 0.81 | 0.88 |
RMSE (training) | 0.111 | 0.127 | 0.093 |
MAE (training) | 0.221 | 0.226 | 0.199 |
MAECV(5-Fold) | 0.484 | 0.486 | 0.480 |
R2 (test) | 0.92 | 0.85 | 0.95 |
RMSE (test) | 0.056 | 0.096 | 0.041 |
MAE (test) | 0.191 | 0.250 | 0.155 |
CCC (test) | 0.968 | 0.946 | 0.978 |
R2 (external test) | 0.74 | 0.88 | 0.92 |
RMSE (external test) | 0.132 | 0.123 | 0.061 |
MAE (external test) | 0.320 | 0.319 | 0.202 |
CCC (external test) | 0.898 | 0.931 | 0.961 |
0.945 | 0.906 | 0.960 | |
0.943 | 0.903 | 0.958 | |
0.510 | 0.536 | 0.560 | |
0.955 | 0.981 | 0.975 | |
1.041 | 1.007 | 1.021 |
Descriptor | SVR_A | SVR_B | Definition and Scope | Descriptor Type |
---|---|---|---|---|
AVS_B(e) | X | X | average vertex sum from Burden matrix weighted by Sanderson electronegativity | 2D matrix-based descriptors |
HATS7s | X | X | leverage-weighted autocorrelation of lag 7/weighted by I-state | GETAWAY descriptors |
Eta_sh_y | X | X | Eta y shape index | ETA indices |
GATS2v | X | Geary autocorrelation of lag 2 weighted by van der Waals volume | 2D autocorrelations | |
GATS8m | X | Geary autocorrelation of lag 8 weighted by mass | 2D autocorrelations | |
P_VSA_LogP_3 | X | P_VSA-like on LogP, bin 3 | P_VSA-like descriptors | |
nHM | X | number of heavy atoms | Constitutional indices | |
RDF060s | X | Radial Distribution Function—060/weighted by I-state | RDF descriptors | |
Dm | X | D total accessibility index/weighted by mass | WHIM descriptors | |
H8u | X | H autocorrelation of lag 8/unweighted | GETAWAY descriptors | |
O-059 | X | Al-O-Al | Atom-centred fragments | |
B09[C-C] | X | Presence/absence of C—C at topological distance 9 | 2D Atom Pairs | |
SpMax3_Bh(m) | X | largest eigenvalue n. 3 of Burden matrix weighted by mass | Burden eigenvalues | |
CATS2D_05_NL | X | CATS2D Negative-Lipophilic at lag 05 | CATS 2D | |
Eig02_EA(dm) | X | eigenvalue n. 2 from edge adjacency mat. weighted by dipole moment | Edge adjacency indices | |
C-043 | X | X--CR.X | Atom-centred fragments |
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Daghighi, A.; Casanola-Martin, G.M.; Timmerman, T.; Milenković, D.; Lučić, B.; Rasulev, B. In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach. Toxics 2022, 10, 746. https://doi.org/10.3390/toxics10120746
Daghighi A, Casanola-Martin GM, Timmerman T, Milenković D, Lučić B, Rasulev B. In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach. Toxics. 2022; 10(12):746. https://doi.org/10.3390/toxics10120746
Chicago/Turabian StyleDaghighi, Amirreza, Gerardo M. Casanola-Martin, Troy Timmerman, Dejan Milenković, Bono Lučić, and Bakhtiyor Rasulev. 2022. "In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach" Toxics 10, no. 12: 746. https://doi.org/10.3390/toxics10120746