QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs
Abstract
:1. Introduction
2. Results
2.1. 2D-QSAR
2.2. Pharmacophore Model
2.3. Shape-Based Screening
2.4. Virtual Screening Workflow and Experimental Assay
3. Discussion and Conclusions
4. Materials and Methods
4.1. Data Preparation
4.1.1. QSAR Data
4.1.2. Pharmacophore Data Collection
4.2. Data Splitting
4.2.1. Descriptor Selection
4.2.2. Descriptor Screening
4.2.3. Advanced Descriptor Selection: Lasso, Stepwise, and Lars
4.3. Genetic Algorithm (GA)
4.4. GreedGene
4.5. QSAR Model Generation and Validation
4.5.1. Training
4.5.2. Validation
4.5.3. Testing
4.6. Pharmacophore Hypothesis Generation and Evaluation
4.7. Shape-Based Screening
4.8. Virtual Screening Protocol
4.9. Radioligand Binding Assay
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Sample Availability
Appendix A
References
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ID | Reference | pKi Range | Number of Compounds | Structure |
---|---|---|---|---|
1 | Ferorelli, Abate [26] | 5.48–7.71 | 9 | |
2 | Mach, Huang [27] | 6.14–8.09 | 8 | |
3 | Huang, Luedtke [28] | 6.39–6.95 | 4 | |
4 | Mach, Huang [29] | 6.29–7.59 | 9 | |
5 | Yarim, Koksal [30] | 6.18–8.00 | 6 | |
6 | Abate, Ferorelli [31] | 6.51–8.79 | 14 | |
7 | Niso, Abate [32] | 7.54–10.40 | 9 | |
8 | Abate, Ferorelli [33] | 7.29–8.58 | 8 | |
9 | Berardi, Ferorelli [34] | 7.52–9.24 | 4 | |
10 | Bai, Li [35] | 5.99–8.82 | 22 | |
11 | Xie, Bergmann [36] | 6.28–7.64 | 16 | |
12 | Berardi, Ferorelli [37] | 6.62–7.75 | 15 | |
13 | Ferorelli, Abate [38] | 6.17–8.08 | 8 | |
14 | Abate, Niso [39] | 7.63–9.31 | 7 | |
15 | Xie, Kniess [40] | 7.17–8.52 | 10 | |
16 | Schininà, Martorana [41] | 5.33–7.25 | 10 |
Lasso | b_Single | Chi0v_C | Chi1v_C | b_max1len | QRPC + |
Stepwise | b_single | chi1_C | SMR_VSA2 | BCUT_PEOE_3 | SlogP_VSA9 |
GA | balabanJ | b_max1len | SMR_VSA0 | Q_VSA_FPNEG | SMR_VSA3 |
GreedGene | balabanJ | b_max1len | Q_VSA_PNEG | vsa_acc | SlogP_VSA1 |
Statistical Parameters | Lasso | Stepwise | GA | GreedGene |
---|---|---|---|---|
Training R2 | 0.43–0.58 | 0.48–0.60 | 0.58–0.68 | 0.62–0.69 |
Training Q2 | 0.36–0.52 | 0.42–0.56 | 0.52–0.63 | 0.57–0.64 |
Validation R2 | 0.27–0.68 | 0.37–0.71 | 0.50–0.73 | 0.53–0.78 |
% met criteria | 38% | 68% | 100% | 100% |
Modeling R2 | 0.5 | 0.55 | 0.63 | 0.65 |
Modeling Q2 | 0.45 | 0.50 | 0.59 | 0.62 |
Testing R2 | 0.51 | 0.51 | 0.51 | 0.56 |
Criteria met | Yes | Yes | Yes | Yes |
Hypo 1 | HHHPRR | 15.2 * |
Hypo 2 | HHPRD | 7.8 |
Hypo 3 | HDPRR | 6.4 |
Hypo 4 | HDPRR | 4.5 |
Hypo 5 | HAPRR | 3.2 |
Hypo 6 | HHPRDH | 5.2 |
Hypo 7 | HAPRR | 3.7 |
Hypo 8 | AHPRR | 2.1 |
Hypo 9 | AHPRR | 4.1 |
Hypo 10 | HHPRR | 4.3 |
Generic Name | Structure | Inh% at 1 μM |
---|---|---|
Ranolazine | 13 | |
Flibanserin | 13 | |
Nefazodone | 76 | |
Cinacalcet | 50 | |
Pimozide | 55 | |
Vilazodone | 26 |
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Yu, Y.; Dong, H.; Peng, Y.; Welsh, W.J. QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs. Molecules 2021, 26, 5270. https://doi.org/10.3390/molecules26175270
Yu Y, Dong H, Peng Y, Welsh WJ. QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs. Molecules. 2021; 26(17):5270. https://doi.org/10.3390/molecules26175270
Chicago/Turabian StyleYu, Yangxi, Hiep Dong, Youyi Peng, and William J. Welsh. 2021. "QSAR-Based Computational Approaches to Accelerate the Discovery of Sigma-2 Receptor (S2R) Ligands as Therapeutic Drugs" Molecules 26, no. 17: 5270. https://doi.org/10.3390/molecules26175270