QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency
Abstract
:1. Introduction
2. Materials and Methods
2.1. Compound Design and Synthesis
2.2. MGMT Activity Assay
2.3. Dataset Preparation
2.4. Descriptor Calculation and Dataset Splitting
2.5. Model Development and Validation
2.6. Best Model Selection by Multiple-Criteria Decision Making
2.7. Applicability Domain (AD) Analysis
2.8. Prediction Using a Similarity-Based Chemical Read-Across Technique
3. Results and Discussion
3.1. MGMT Inactivation
3.2. Chemical Space Distribution
3.3. QSAR Modeling of Potential MGMT Inactivators
3.3.1. Model Selection and Evaluation
3.3.2. Full Model
3.4. q-RASAR Analysis
3.5. Application of the 2D-QSAR-Based Full Model and q-RASAR-Full 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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Division | Fitting | Robustness | Chance Correlation | External Validation | Accuracy | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Scheme | Ntr | Npr | R2 | Q2LOO | Q2LMO | Q2Yscr | R2Yscr | R2pr | Q2F1 | Q2F2 | Q2F3 | CCCpr | RMSEtr | RMSEpr | MAEtr | MAEpr |
ORes | 278 | 92 | 0.5098 | 0.4968 | 0.4952 | −0.0186 | 0.0108 | 0.5319 | 0.5271 | 0.5266 | 0.5658 | 0.6891 | 0.8669 | 0.8151 | 0.6666 | 0.6440 |
pIC50 = 3.9243 + 0.6729F09[O-S] + 0.121SaaN + 1.005MDEN-12 (k = 1.0, |R20 − R′20| = 0.4582) | (1) | |||||||||||||||
OStr | 278 | 92 | 0.6826 | 0.6496 | 0.6451 | −0.0544 | 0.0432 | 0.5882 | 0.5721 | 0.5712 | 0.5814 | 0.7617 | 0.6917 | 0.7943 | 0.5514 | 0.5982 |
pIC50 = 7.1639 − 47.2151VE2sign_B(m) + 2.8662MATS6i − 1.5036GATS7p − 0.1826H-048 + 0.5367O-060 − 0.3698B08[N-O] + 0.3948F06[C-S] − 0.1856SsNH2 + 0.0537minHBint6 + 1.8451MDEN-12 + 0.1755MDEN-22 − 0.8906minaaCH (k = 1.0, |R20 − R′20| = 0.1428) | (2) | |||||||||||||||
Random (2D-QSAR) | 279 | 91 | 0.6086 | 0.5743 | 0.5648 | −0.0424 | 0.0324 | 0.7474 | 0.7377 | 0.7375 | 0.7437 | 0.8530 | 0.7682 | 0.6215 | 0.6114 | 0.5224 |
pIC50 = 4.5562 + 2.5829MATS6i − 0.191nCp + 0.3196O-060 + 0.6746B03[O-S] − 0.2499SsNH2 + 2.4853maxHBd − 2.3712hmin + 1.1784MDEN-12 − 0.6509minaaCH (k = 1.0, |R20 − R′20| = 0.2436) | (3) | |||||||||||||||
2D-QSAR-Full model | 370 | — | 0.6426 | 0.6202 | 0.6127 | −0.0309 | 0.0248 | — | — | — | — | — | 0.7320 | — | 0.5855 | — |
pIC50 = 4.7334 + 2.3826MATS6i − 0.2387nCp + 0.3401O-060 + 0.6301B03[O-S] − 0.248SsNH2 + 2.1364maxHBd − 2.3442hmin + 1.8332MDEN-12 − 0.6324minaaCH | (4) | |||||||||||||||
q-RASAR | 279 | 91 | 0.6059 | 0.5957 | 0.5926 | −0.0189 | 0.0103 | 0.7528 | 0.7389 | 0.7387 | 0.7449 | 0.8560 | 0.7708 | 0.6201 | 0.6144 | 0.4812 |
pIC50 = −1.1683 + 0.9192RA function (ED) + 0.0718CATS2D_07_AL + 1.2875LLS_02 | (5) | |||||||||||||||
q-RASAR-Full model | 370 | 0.6392 | 0.6322 | 0.6305 | −0.0136 | 0.0083 | — | — | — | — | — | 0.7354 | — | 0.5799 | — | |
pIC50 = −1.1089 + 0.9149RA function (ED) + 0.0667CATS2D_07_AL + 1.3166LLS_02 | (6) |
Descriptors | Std. Coefficient | Range | Definition | |
---|---|---|---|---|
(Full Model) | Min | Max | ||
MATS6i | 0.2152 (0.1946) | −0.212 | 0.371 | Moran autocorrelation of lag 6 weighted by ionization potential (DRAGON) |
nCp | −0.106 (−0.1331) | 0 | 6 | number of terminal primary C(sp3) (DRAGON) |
O-060 | 0.1908 (0.2012) | 0 | 4 | Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X (Atom-centered fragments, Basic descriptors) (DRAGON) |
B03[O-S] | 0.2623 (0.2452) | 0 | 1 | Presence/absence of O-S at topological distance 3 (DRAGON) |
SsNH2 | −0.4636 (−0.4582) | 0 | 11.662 | Sum of atom-type E-State: –NH2 (DRAGON) |
maxHBd | 0.2085 (0.185) | 0 | 0.764 | Maximum E-States for (strong) Hydrogen Bond donors (PaDEL) |
hmin | −0.1766 (−0.1792) | −0.447 | 0.425 | Minimum H E-State (PaDEL) |
MDEN-12 | 0.6452 (0.6567) | 0 | 2.515 | Molecular distance edge between all primary and secondary nitrogens (PaDEL) |
minaaCH | −0.1103 (−0.1063) | 1.075 | 2.329 | Minimum atom-type E-State: CH: (PaDEL) |
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Sun, G.; Bai, P.; Fan, T.; Zhao, L.; Zhong, R.; McElhinney, R.S.; McMurry, T.B.H.; Donnelly, D.J.; McCormick, J.E.; Kelly, J.; et al. QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency. Pharmaceutics 2023, 15, 2170. https://doi.org/10.3390/pharmaceutics15082170
Sun G, Bai P, Fan T, Zhao L, Zhong R, McElhinney RS, McMurry TBH, Donnelly DJ, McCormick JE, Kelly J, et al. QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency. Pharmaceutics. 2023; 15(8):2170. https://doi.org/10.3390/pharmaceutics15082170
Chicago/Turabian StyleSun, Guohui, Peiying Bai, Tengjiao Fan, Lijiao Zhao, Rugang Zhong, R. Stanley McElhinney, T. Brian H. McMurry, Dorothy J. Donnelly, Joan E. McCormick, Jane Kelly, and et al. 2023. "QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency" Pharmaceutics 15, no. 8: 2170. https://doi.org/10.3390/pharmaceutics15082170
APA StyleSun, G., Bai, P., Fan, T., Zhao, L., Zhong, R., McElhinney, R. S., McMurry, T. B. H., Donnelly, D. J., McCormick, J. E., Kelly, J., & Margison, G. P. (2023). QSAR and Chemical Read-Across Analysis of 370 Potential MGMT Inactivators to Identify the Structural Features Influencing Inactivation Potency. Pharmaceutics, 15(8), 2170. https://doi.org/10.3390/pharmaceutics15082170