Prediction of PKCθ Inhibitory Activity Using the Random Forest Algorithm
Abstract
:1. Introduction
2. Results and Discussion
2.1. Performance of RF, PLS and SVM
2.2. Comparison of Different Statistical Approaches
2.3. Focus on the RF Model
3. Material and Experimental Methods
3.1. Data Sets and Descriptors
3.2. Statistical Methods
3.3. Evaluation of the Prediction Performance
4. Conclusions
Acknowledgements
References
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Para.a | RF | SVM | PLS | |||
---|---|---|---|---|---|---|
Training | Test | Training | Test | Training | Test | |
Size | 157 | 51 | 157 | 51 | 157 | 51 |
R2 | 0.96 | 0.76 | 0.99 | 0.61 | 0.57 | 0.42 |
Q2 | 0.54 | - | 0.57 | - | 0.36 | - |
R2pred | - | 0.72 | - | 0.59 | - | 0.39 |
SEE | 0.25 | - | 0.08 | - | 0.59 | - |
SEP | - | 0.45 | - | 0.55 | - | 0.67 |
Paraa | RF | SVM | PLS | |||
---|---|---|---|---|---|---|
Training | Test | Training | Test | Training | Test | |
Size | 157 | 51 | 157 | 51 | 157 | 51 |
R2 | 0.95 ± 0.003 | 0.58 ± 0.09 | 0.82 ± 0.01 | 0.49 ± 0.10 | 0.64 ± 0.13 | 0.41 ± 0.13 |
Q2 | 0.57 ± 0.03 | - | 0.59 ± 0.02 | - | 0.39 ± 0.11 | - |
R2pred | - | 0.56 ± 0.09 | - | 0.45 ± 0.10 | - | 0.10 ± 0.84 |
SEE | 0.24 ± 0.01 | - | 0.39 ± 0.01 | - | 0.53 ± 0.09 | - |
SEP | - | 0.59 ± 0.06 | - | 0.63 ± 0.05 | - | 0.79 ± 0.25 |
No. | Scaffold | Substituent | pIC50 | Refa | ||
---|---|---|---|---|---|---|
R1 | R2 | R3 | ||||
1* | A | OMe | OMe | 3-Bromophenyl | 5.337 | [8] |
2 | A | OMe | OMe | Phenyl | 5.796 | [8] |
3* | A | OMe | OMe | 3-Chlorophenyl | 5.409 | [8] |
X | ||||||
17 | B | Pyrrolidine | 8.420 | [9] | ||
23 | B | H2N | 7.921 | [9] | ||
27 | B | PhNH | 6.959 | [9] | ||
Ar | R | |||||
77 | C | Phenyl | 4-CH2-NMe2 | 7.854 | [11] | |
80 | C | 3-Pyridine | 5-CH2-NMe2 | 7.076 | [11] | |
85* | C | Phenyl | 2-OMe,3-CH2-NMe2 | 7.921 | [11] | |
X | n | |||||
37 | D | 1 | 7.456 | [10] | ||
41* | D | 2 | 7.469 | [10] | ||
NR’R | ||||||
137 | E | Morpholine | 8.108 | [14] | ||
140 | E | Pyrrolidine | 7.456 | [14] | ||
NR’R | ||||||
153* | F | Morpholine | 7.886 | [15] | ||
157* | F | NHCH2CH(OH)CH2OH | 8.824 | [15] |
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Hao, M.; Li, Y.; Wang, Y.; Zhang, S. Prediction of PKCθ Inhibitory Activity Using the Random Forest Algorithm. Int. J. Mol. Sci. 2010, 11, 3413-3433. https://doi.org/10.3390/ijms11093413
Hao M, Li Y, Wang Y, Zhang S. Prediction of PKCθ Inhibitory Activity Using the Random Forest Algorithm. International Journal of Molecular Sciences. 2010; 11(9):3413-3433. https://doi.org/10.3390/ijms11093413
Chicago/Turabian StyleHao, Ming, Yan Li, Yonghua Wang, and Shuwei Zhang. 2010. "Prediction of PKCθ Inhibitory Activity Using the Random Forest Algorithm" International Journal of Molecular Sciences 11, no. 9: 3413-3433. https://doi.org/10.3390/ijms11093413