A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Preparation
2.2. Molecular Representation
2.2.1. Fingerprint
- (1)
- Morgan fingerprint [28]: A circular fingerprint encoding structural information by considering substructures at different radii around each atom.
- (2)
- PubChem fingerprint [29]: A binary fingerprint derived from the PubChem Compound database, representing molecular structural features based on predefined chemical substructures.
- (3)
- Daylight fingerprint: A descriptor developed by Daylight Chemical Information Systems, encoding chemical features by identifying fragments and substructures within a molecule.
- (4)
- RDKit fingerprint: A fingerprinting method integrated by the RDKit package. It is a dictionary with one entry per bit set in the fingerprint; the keys are the bit IDs; the values are tuples of tuples containing bond indices.
- (5)
- ESPF fingerprint [18]: An explainable substructure partition fingerprint capturing extended connectivity patterns within a molecule, representing the presence of specific atom types and their surrounding environments.
- (6)
- ErG fingerprint [30]: A novel fingerprinting method presented that uses pharmacophore-type node descriptions to encode the relevant molecular properties.
2.2.2. Convolutional Neural Network
- (1)
- Simple CNN [31]:
- (2)
- CNN-LSTM [33]:
- (3)
- CNN-GRU [33]:
2.2.3. Graph Neural Networks
- (1)
- GCN [35]:
- (2)
- NeuralFP [36]:
- (3)
- GIN-AttrMasking [37]:
- (4)
- GIN-ContextPred [37]:
- (5)
- AttentiveFP [38]:
2.3. Predictor
3. Results
3.1. Evaluation Metrics
3.2. Comparison of Model Performance
3.3. Voting/Consensus Model Performance
- (1)
- Consensus FP: The ensemble score is obtained by voting from six molecular fingerprint methods.
- (2)
- Consensus CNN: The ensemble score is obtained by voting from three CNN methods.
- (3)
- Consensus GNN: The ensemble score is obtained by voting from five GNN methods.
- (4)
- FP + CNN: This approach combines the top two molecular fingerprint methods and the top two CNN methods based on their best F1 scores.
- (5)
- FP + GNN: This approach combines the top two molecular fingerprint methods and the top two GNN methods based on their best F1 scores.
- (6)
- CNN + GNN: This approach combines the top two CNN methods and the top two GNN methods based on their best F1 scores.
- (7)
- FP + CNN + GNN: This approach combines the top two molecular fingerprint methods, the top two CNN methods, and the top two GNN methods based on their best F1 scores.
3.4. In Silico Compound Taste Database
4. Discussion
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Category | Training | Validation | Test | Total | |||
---|---|---|---|---|---|---|---|
Number | Positive Rate | Number | Positive Rate | Number | Positive Rate | ||
Sweet | 637 | 0.350 | 91 | 0.350 | 178 | 0.342 | 906 |
Nonsweet | 1184 | 169 | 342 | 1695 | |||
Bitter | 769 | 0.422 | 118 | 0.454 | 239 | 0.460 | 1126 |
Non-bitter | 1052 | 142 | 281 | 1475 | |||
Umami | 71 | 0.039 | 8 | 0.031 | 19 | 0.037 | 98 |
Non-umami | 1750 | 252 | 501 | 2503 |
Model | TP | FP | TN | FN | Acc. | Prec. | Sens. | Spec. | F1 |
---|---|---|---|---|---|---|---|---|---|
Morgan | 151 | 57 | 285 | 27 | 0.838 | 0.726 | 0.848 | 0.833 | 0.782 |
Pubchem | 152 | 61 | 281 | 26 | 0.833 | 0.714 | 0.854 | 0.822 | 0.777 |
Daylight | 120 | 33 | 309 | 58 | 0.825 | 0.784 | 0.674 | 0.904 | 0.725 |
RDKit | 127 | 35 | 307 | 51 | 0.835 | 0.784 | 0.713 | 0.898 | 0.747 |
ESPF | 123 | 49 | 293 | 55 | 0.800 | 0.715 | 0.691 | 0.857 | 0.703 |
ErG | 140 | 45 | 297 | 38 | 0.840 | 0.757 | 0.787 | 0.868 | 0.771 |
CNN | 141 | 60 | 282 | 37 | 0.813 | 0.701 | 0.792 | 0.825 | 0.744 |
CNN_GRU | 134 | 43 | 299 | 44 | 0.833 | 0.757 | 0.753 | 0.874 | 0.755 |
CNN_LSTM | 114 | 29 | 313 | 64 | 0.821 | 0.797 | 0.640 | 0.915 | 0.710 |
GCN | 148 | 38 | 304 | 30 | 0.869 | 0.796 | 0.831 | 0.889 | 0.813 |
NeuralFP | 147 | 37 | 305 | 31 | 0.869 | 0.799 | 0.826 | 0.892 | 0.812 |
GIN_AttrMasking | 154 | 52 | 290 | 24 | 0.854 | 0.748 | 0.865 | 0.848 | 0.802 |
GIN_ContextPred | 152 | 52 | 290 | 26 | 0.850 | 0.745 | 0.854 | 0.848 | 0.796 |
AttentiveFP | 98 | 19 | 323 | 80 | 0.810 | 0.838 | 0.550 | 0.944 | 0.664 |
Model | TP | FP | TN | FN | Acc. | Prec. | Sens. | Spec. | F1 |
---|---|---|---|---|---|---|---|---|---|
Morgan | 197 | 30 | 251 | 42 | 0.862 | 0.868 | 0.824 | 0.893 | 0.845 |
Pubchem | 202 | 26 | 255 | 37 | 0.879 | 0.886 | 0.845 | 0.907 | 0.865 |
Daylight | 197 | 43 | 238 | 42 | 0.837 | 0.821 | 0.824 | 0.847 | 0.823 |
RDKit | 203 | 32 | 249 | 36 | 0.869 | 0.864 | 0.849 | 0.886 | 0.857 |
ESPF | 196 | 53 | 228 | 43 | 0.815 | 0.787 | 0.820 | 0.811 | 0.803 |
ErG | 190 | 25 | 256 | 49 | 0.858 | 0.884 | 0.795 | 0.911 | 0.837 |
CNN | 163 | 16 | 265 | 76 | 0.823 | 0.911 | 0.682 | 0.943 | 0.780 |
CNN_GRU | 167 | 19 | 262 | 72 | 0.825 | 0.898 | 0.699 | 0.932 | 0.786 |
CNN_LSTM | 173 | 25 | 256 | 66 | 0.825 | 0.874 | 0.724 | 0.911 | 0.792 |
GCN | 193 | 27 | 254 | 46 | 0.860 | 0.877 | 0.808 | 0.904 | 0.841 |
NeuralFP | 207 | 22 | 259 | 32 | 0.896 | 0.904 | 0.866 | 0.922 | 0.885 |
GIN_AttrMasking | 174 | 23 | 258 | 65 | 0.831 | 0.883 | 0.728 | 0.918 | 0.798 |
GIN_ContextPred | 169 | 14 | 267 | 70 | 0.838 | 0.923 | 0.707 | 0.950 | 0.801 |
AttentiveFP | 170 | 19 | 262 | 69 | 0.831 | 0.899 | 0.711 | 0.932 | 0.794 |
Model | TP | FP | TN | FN | Acc. | Prec. | Sens. | Spec. | F1 |
---|---|---|---|---|---|---|---|---|---|
Morgan | 15 | 0 | 501 | 4 | 0.992 | 1.000 | 0.789 | 1.000 | 0.882 |
Pubchem | 17 | 1 | 500 | 2 | 0.994 | 0.944 | 0.895 | 0.998 | 0.919 |
Daylight | 16 | 5 | 496 | 3 | 0.985 | 0.762 | 0.842 | 0.990 | 0.800 |
RDKit | 19 | 14 | 487 | 0 | 0.973 | 0.576 | 1.000 | 0.972 | 0.731 |
ESPF | 15 | 4 | 497 | 4 | 0.985 | 0.789 | 0.789 | 0.992 | 0.789 |
ErG | 16 | 1 | 500 | 3 | 0.992 | 0.941 | 0.842 | 0.998 | 0.889 |
CNN | 13 | 3 | 498 | 6 | 0.983 | 0.813 | 0.684 | 0.994 | 0.743 |
CNN_GRU | 15 | 0 | 501 | 4 | 0.992 | 1.000 | 0.789 | 1.000 | 0.882 |
CNN_LSTM | 14 | 1 | 500 | 5 | 0.988 | 0.933 | 0.737 | 0.998 | 0.824 |
GCN | 15 | 0 | 501 | 4 | 0.992 | 1.000 | 0.789 | 1.000 | 0.882 |
NeuralFP | 17 | 7 | 494 | 2 | 0.982 | 0.708 | 0.895 | 0.986 | 0.791 |
GIN_AttrMasking | 16 | 10 | 491 | 3 | 0.975 | 0.615 | 0.842 | 0.980 | 0.711 |
GIN_ContextPred | 16 | 6 | 495 | 3 | 0.983 | 0.727 | 0.842 | 0.988 | 0.780 |
AttentiveFP | 19 | 0 | 501 | 3 | 0.994 | 1.000 | 0.842 | 1.000 | 0.914 |
Model | TP | FP | TN | FN | Acc. | Prec. | Sens. | Spec. | F1 |
---|---|---|---|---|---|---|---|---|---|
Consensus FP | 141 | 24 | 318 | 37 | 0.883 | 0.855 | 0.792 | 0.930 | 0.822 |
Consensus CNN | 138 | 41 | 301 | 40 | 0.844 | 0.771 | 0.775 | 0.880 | 0.773 |
Consensus GNN | 142 | 23 | 319 | 36 | 0.887 | 0.861 | 0.798 | 0.933 | 0.828 |
FP + CNN | 156 | 40 | 302 | 22 | 0.881 | 0.796 | 0.876 | 0.883 | 0.834 |
FP + GNN | 153 | 28 | 314 | 25 | 0.898 | 0.845 | 0.860 | 0.918 | 0.852 |
CNN + GNN | 141 | 26 | 316 | 37 | 0.879 | 0.844 | 0.792 | 0.924 | 0.817 |
FP + CNN + GNN | 153 | 29 | 313 | 25 | 0.896 | 0.841 | 0.860 | 0.915 | 0.850 |
Model | TP | FP | TN | FN | Acc. | Prec. | Sens. | Spec. | F1 |
---|---|---|---|---|---|---|---|---|---|
Consensus FP | 205 | 27 | 254 | 34 | 0.883 | 0.884 | 0.858 | 0.904 | 0.870 |
Consensus CNN | 181 | 19 | 262 | 58 | 0.852 | 0.905 | 0.757 | 0.932 | 0.825 |
Consensus GNN | 188 | 13 | 268 | 51 | 0.877 | 0.935 | 0.787 | 0.954 | 0.855 |
FP + CNN | 192 | 16 | 265 | 47 | 0.879 | 0.923 | 0.805 | 0.943 | 0.859 |
FP + GNN | 202 | 17 | 264 | 37 | 0.896 | 0.922 | 0.845 | 0.940 | 0.882 |
CNN + GNN | 189 | 13 | 268 | 50 | 0.879 | 0.956 | 0.791 | 0.954 | 0.857 |
FP + CNN + GNN | 197 | 15 | 266 | 42 | 0.890 | 0.929 | 0.824 | 0.947 | 0.874 |
Model | TP | FP | TN | FN | Acc. | Prec. | Sens. | Spec. | F1 |
---|---|---|---|---|---|---|---|---|---|
Consensus FP | 16 | 1 | 500 | 3 | 0.992 | 0.941 | 0.842 | 0.998 | 0.889 |
Consensus CNN | 16 | 0 | 501 | 3 | 0.994 | 1.000 | 0.842 | 1.000 | 0.914 |
Consensus GNN | 17 | 1 | 500 | 2 | 0.994 | 0.944 | 0.895 | 0.998 | 0.919 |
FP + CNN | 15 | 0 | 501 | 4 | 0.992 | 1.000 | 0.789 | 1.000 | 0.882 |
FP + GNN | 15 | 0 | 501 | 4 | 0.992 | 1.000 | 0.789 | 1.000 | 0.882 |
CNN + GNN | 16 | 0 | 501 | 3 | 0.994 | 1.000 | 0.842 | 1.000 | 0.914 |
FP + CNN + GNN | 15 | 0 | 501 | 4 | 0.992 | 1.000 | 0.789 | 1.000 | 0.882 |
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Song, Y.; Chang, S.; Tian, J.; Pan, W.; Feng, L.; Ji, H. A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction. Foods 2023, 12, 3386. https://doi.org/10.3390/foods12183386
Song Y, Chang S, Tian J, Pan W, Feng L, Ji H. A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction. Foods. 2023; 12(18):3386. https://doi.org/10.3390/foods12183386
Chicago/Turabian StyleSong, Yu, Sihao Chang, Jing Tian, Weihua Pan, Lu Feng, and Hongchao Ji. 2023. "A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction" Foods 12, no. 18: 3386. https://doi.org/10.3390/foods12183386