Table 2.
List of deep learning‐based MS/MS spectrum prediction tools. The fragment ion type supported by each tool is summarized based on its original publication and available trained models
| No. | Software | Framework | Core network model | Fragment ion type | Usability a) | Year | Reference |
|---|---|---|---|---|---|---|---|
| 1 | pDeep/pDeep2 | Keras/TensorFlow | RNN | b/y; c/z | O,C,P,T | 2017/2019 | [57, 69] |
| 2 | Prosit | Keras/TensorFlow | RNN | b/y | O,C,W,P,T | 2019 | [29] |
| 3 | DeepMass:Prism | Keras/TensorFlow | RNN | b/y | W | 2019 | [45] |
| 4 | Guan et al. | Keras/TensorFlow | RNN | b/y | O,C,P,T | 2019 | [46] |
| 5 | MS2CNN | Keras/TensorFlow | CNN | b/y | O,C,P | 2019 | [70] |
| 6 | DeepDIA | Keras/TensorFlow | CNN+RNN | b/y | O,C,P,T | 2020 | [30] |
| 7 | Predfull | TensorFlow | CNN | All possible ions at all m/z axises | O,C,W,P,T | 2020 | [56] |
a)
O, open‐source; C, command line; P, provide trained model for prediction; W, web interface; T, provide option for model training. The link of each tool could be found at https://github.com/bzhanglab/deep_learning_in_proteomics.