Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors
Abstract
:1. Introduction
2. Results and Discussion
Group | Number of Compounds | R2 | SD | F | p | Stability |
---|---|---|---|---|---|---|
1 (ARRR) | 83 | 0.81 | 0.41 | 114.50 | 7.35 × 10−29 | 0.51 |
2 (ADRRR) | 29 | 0.89 | 0.32 | 75.30 | 3.10 × 10−13 | 0.61 |
3 (AAAARR) | 62 | 0.86 | 0.32 | 121.70 | 5.14 × 10−25 | 0.74 |
4 (DDRRR) | 99 | 0.81 | 0.38 | 134.70 | 2.82 × 10−34 | 0.53 |
5 (ADHRR) | 18 | 0.99 | 0.09 | 362.60 | 1.67 × 10−13 | 0.53 |
6 (ADDH) | 25 | 0.96 | 0.14 | 170.90 | 6.58 × 10−15 | 0.89 |
7 (ADHR) | 20 | 0.98 | 0.15 | 237.80 | 1.78 × 10−13 | 0.56 |
8 (DRRRR) | 39 | 0.98 | 0.21 | 649.80 | 9.81 × 10−31 | 0.85 |
Group | Test Set Grouped Based on Similarity | Total Test Set (232) | ||||
---|---|---|---|---|---|---|
Matched Hits | R2 | SD | Matched Hits | R2 | SD | |
1 (ARRR) | 51 | 0.53 | 0.64 | 229 | 0.00 * | 1.12 |
2 (ADRRR) | 16 | 0.37 | 0.71 | 231 | 0.00 * | 1.16 |
3 (AAAARR) | 29 | 0.41 | 0.68 | 227 | 0.01 | 1.09 |
4 (DDRRR) | 43 | 0.17 | 0.87 | 227 | 0.00 * | 1.12 |
5 (ADHRR) | 9 | 0.66 | 0.79 | 232 | 0.00 * | 1.09 |
6 (ADDH) | 16 | 0.54 | 0.68 | 184 | 0.00 * | 1.00 |
7 (ADHR) | 23 | 0.37 | 0.90 | 216 | 0.03 | 1.10 |
8 (DRRRR) | 42 | 0.87 | 0.33 | 229 | 0.12 | 1.01 |
Combinatorial QSAR Model | – | – | – | 229 | 0.53 | 0.75 |
Group | Number of Hits/Size of Dataset | EF | |||||
---|---|---|---|---|---|---|---|
Decoy Set | Decoys | Inhibitors | 1% | 2% | 5% | 10% | |
1 | 2063/2804 | 2012/2750 | 51/54 | 11.56 | 7.89 | 5.50 | 3.34 |
2 | 836/915 | 820/899 | 16/16 | 26.13 | 18.44 | 11.20 | 6.84 |
3 | 1048/1121 | 1019/1092 | 29/29 | 14.46 | 10.33 | 8.34 | 6.54 |
4 | 629/716 | 586/673 | 43/43 | 14.63 | 14.63 | 12.74 | 8.59 |
5 | 737/773 | 728/764 | 9/9 | 0.00 | 10.92 | 6.64 | 3.32 |
6 | 93/109 | 77/93 | 16/16 | 5.81 | 5.81 | 4.65 | 3.49 |
7 | 542/593 | 519/570 | 23/23 | 23.57 | 17.14 | 9.60 | 4.80 |
8 | 585/866 | 543/824 | 42/42 | 9.29 | 4.64 | 2.40 | 2.60 |
Total | 6533/7897 | 6304/7665 | 229/232 | 10.09 | 7.84 | 5.06 | 3.15 |
Compound ID | Hit Compound Structure | Inhibition (%) a | Similar Structure in BindingDB | Similarity b | |
---|---|---|---|---|---|
50 μM | 10 μM | ||||
6 | 59.20 | 19.65 | 0.25 | ||
BindingDB4812 | |||||
42 | 54.20 | 28.25 | 0.27 | ||
BindingDB50307880 | |||||
47 | 51.80 | 33.20 | 0.23 | ||
BindingDB50421018 | |||||
53 | 104.30 | 24.75 | 0.37 | ||
BindingDB50234144 | |||||
56 | 80.60 | 54.00 | 0.32 | ||
BindingDB50420994 | |||||
75 | 85.40 | 48.00 | 0.26 | ||
BindingDB50279238 | |||||
76 | 56.80 | 29.15 | 0.29 | ||
BindingDB13533 | |||||
82 | 63.40 | 27.30 | 0.35 | ||
BindingDB50121980 | |||||
83 | 55.40 | 21.80 | 0.32 | ||
BindingDB3855 | |||||
88 | 74.50 | 50.00 | 0.35 | ||
BindingDB3933 | |||||
89 | 53.00 | 24.95 | 0.30 | ||
BindingDB50431812 | |||||
91 | 53.70 | 45.00 | 0.30 | ||
BindingDB50420968 | |||||
92 | 57.10 | 15.75 | 0.25 | ||
BindingDB50185172 | |||||
93 | 54.30 | 37.35 | 0.38 | ||
BindingDB50185180 | |||||
95 | 86.40 | 18.30 | 0.28 | ||
BindingDB3051 | |||||
96 | 83.70 | 2.80 | 0.28 | ||
BindingDB50279045 | |||||
97 | 77.10 | 45.00 | 0.28 | ||
BindingDB11242 | |||||
98 | 64.00 | 23.85 | 0.28 | ||
BindingDB50345445 | |||||
99 | 52.30 | 27.45 | 0.27 | ||
BindingDB6619 |
3. Experimental Section
3.1. Dataset
Group | Skeleton | Size of Training Set | Size of Test Set |
---|---|---|---|
1 | 83 | 54 | |
2 | 29 | 16 | |
3 | 62 | 29 | |
4 | 99 | 43 | |
5 | 18 | 9 | |
6 | 25 | 16 | |
7 | 20 | 23 | |
8 | 39 | 42 |
3.2. Pharmacophore Hypothesis Generation
3.2.1. Conformation Analysis
3.2.2. Generate Common Pharmacophore Hypothesis (CPH)
3.2.3. Score Hypotheses
3.3. Build QSAR Models
3.4. Construct Combinatorial 3D-QSAR Model
3.5. Model Validation
3.6. Pharmacophore-Based Virtual Screening and 3D-QSAR Analysis
3.7. Enzyme Assay
4. Conclusions
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Zhou, N.; Xu, Y.; Liu, X.; Wang, Y.; Peng, J.; Luo, X.; Zheng, M.; Chen, K.; Jiang, H. Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors. Int. J. Mol. Sci. 2015, 16, 13407-13426. https://doi.org/10.3390/ijms160613407
Zhou N, Xu Y, Liu X, Wang Y, Peng J, Luo X, Zheng M, Chen K, Jiang H. Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors. International Journal of Molecular Sciences. 2015; 16(6):13407-13426. https://doi.org/10.3390/ijms160613407
Chicago/Turabian StyleZhou, Nannan, Yuan Xu, Xian Liu, Yulan Wang, Jianlong Peng, Xiaomin Luo, Mingyue Zheng, Kaixian Chen, and Hualiang Jiang. 2015. "Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors" International Journal of Molecular Sciences 16, no. 6: 13407-13426. https://doi.org/10.3390/ijms160613407