Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition
Abstract
:1. Introduction
2. Results
2.1. Performance Evaluation on DTIs
2.2. Comparison with State-of-the-Art Methods
3. Discussion
3.1. Effectiveness of Negative Generation
3.2. Discussion of E-State and APAAC
3.3. Parameters Adjustment of Algorithm
4. Materials and Methods
4.1. Benchmark Datasets
4.2. Descriptors of Drugs and Targets
4.3. Construction of Datasets and Algorithm
4.4. The Flowchart
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Enzyme | GPCR | Ion Channel | Nuclear Receptor | |
---|---|---|---|---|
Prec. (%) | 100.00 ± 0.00 # | 100.0 ± 0.00 | 100.0 ± 0.00 | 100.0 ± 0.00 |
Rec. (%) | 97.85 ± 0.01 | 94.38 ± 0.28 | 95.46 ± 0.03 | 91.50 ± 0.68 |
Acc. (%) | 98.92 ± 0.01 | 97.19 ± 0.14 | 97.73 ± 0.02 | 95.75 ± 0.34 |
F1. (%) | 98.91 ± 0.01 | 97.11 ± 0.15 | 97.68 ± 0.02 | 95.56 ± 0.37 |
MCC (%) | 97.87 ± 0.01 | 94.53 ± 0.27 | 95.56 ± 0.03 | 91.83 ± 0.63 |
AUC (%) | 99.58 ± 0.02 | 98.66 ± 0.09 | 98.57 ± 0.07 | 98.51 ± 0.30 |
AUC | Enzyme | GPCR | Ion Channel | Nuclear Receptor | Dimension of Features | |
---|---|---|---|---|---|---|
Similarity-based | KBMF2K | 0.832 | 0.857 | 0.799 | 0.824 | - |
NetCBP | 0.825 | 0.823 | 0.803 | 0.839 | - | |
Bigram | 0.948 | 0.872 | 0.889 | 0.869 | - | |
PUDT | 0.884 | 0.878 | 0.831 | 0.885 | - | |
Feature vector-based | Cao et al. | 0.948 | 0.890 | 0.872 | 0.878 | 343 |
Wang et al. | 0.943 | 0.874 | 0.911 | 0.818 | 1281 | |
MFDR | 0.969 | 0.904 | 0.933 | 0.886 | 1448/2330 | |
FRnet-DTI | 0.976 | 0.948 | 0.951 | 0.924 | 4096 | |
Ran-proposed | 0.973 | 0.926 | 0.967 | 0.928 | 159 | |
Dis-proposed | 0.996 | 0.987 | 0.986 | 0.985 | 159 |
Methods | TPR (%) | TNR (%) | Acc. (%) | AUC (%) |
---|---|---|---|---|
DeepDTI | 82.27 ± 0.65 # | 89.53 ±1.30 | 85.88 ± 0.49 | 91.58 ± 0.59 |
Hu et al. of Random sampling | 91.94 ± 0.91 | 91.14 ± 1.96 | 88.14 ± 0.75 | 95.27 ± 0.43 |
Hu et al. of Distance-based sampling | 97.09 ± 0.67 | 96.86 ± 1.29 | 96.04 ± 0.32 | 99.47 ± 0.21 |
Ran-proposed | 81.67 ± 2.33 | 81.71 ± 2.51 | 81.69 ± 1.72 | 89.05 ± 1.30 |
Dis-proposed | 99.80 ± 0.30 | 99.97 ± 0.06 | 99.89 ± 0.14 | 99.98 ± 0.04 |
Methods | Prec. (%) | Rec. (%) | Acc. (%) | F1. (%) | MCC (%) | AUC (%) |
---|---|---|---|---|---|---|
Ran-ChEMBL | 72.48 ± 4.39 # | 90.14 ± 1.22 | 77.68 ± 3.77 | 80.23 ± 2.75 | 57.34 ± 6.68 | 92.05 ± 1.35 |
Dis-ChEMBL | 99.86 ± 0.24 | 98.99 ± 0.02 | 99.41 ± 0.13 | 99.42 ± 0.12 | 98.86 ± 0.25 | 99.83 ± 0.02 |
Enzyme | GPCR | Ion Channel | Nuclear Receptor | |
---|---|---|---|---|
Drugs | 445 | 223 | 210 | 54 |
Targets | 664 | 95 | 204 | 26 |
Positive Interactions | 2926 | 635 | 1476 | 90 |
Total DT-pairs | 295,480 | 21,180 | 42,840 | 1404 |
proportion of positive | 0.99% | 3.00% | 3.45% | 6.41% |
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Wang, C.; Wang, W.; Lu, K.; Zhang, J.; Chen, P.; Wang, B. Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition. Int. J. Mol. Sci. 2020, 21, 5694. https://doi.org/10.3390/ijms21165694
Wang C, Wang W, Lu K, Zhang J, Chen P, Wang B. Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition. International Journal of Molecular Sciences. 2020; 21(16):5694. https://doi.org/10.3390/ijms21165694
Chicago/Turabian StyleWang, Cheng, Wenyan Wang, Kun Lu, Jun Zhang, Peng Chen, and Bing Wang. 2020. "Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition" International Journal of Molecular Sciences 21, no. 16: 5694. https://doi.org/10.3390/ijms21165694