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Abbreviations
ML – machine learning method
SVM – support vector machine
MLR – multiple linear regression
RF – random forest
NN – neural network
DBN – deep belief network
meta-predictor – consolidates several predictors in a consensus prediction
ML/NN facilitated – ML or NN was used, for example, to derive weights for energy function
| Predictor | Input | Algorithm | Multiple-point mutations | Anti-symmetry | Setting T | Setting pH | Year |
|---|---|---|---|---|---|---|---|
| Eris (doi, server) | 3D | energy function (knowledge-based) | ● | ○ | ○ | ○ | 2007 |
| I-Mutant 3 (doi, server) | 1D | ML (SVM) | ○ | ● | ● | ● | 2008 |
| AUTO-MUTE (doi, server) | 3D | energy function (knowledge-based), ML facilitated (RF, SVM REPTree, SVM Regression) |
○ | ○ | ● | ● | 2008 |
| iSTABLE (doi, server) | 1D | ML (SVM), meta-predictor | ○ | ○ | ● | ● | 2013 |
| NeEMO (doi, server) | 3D | NN | ○ | ○ | ● | ● | 2014 |
| INPS (doi, server) | 1D | ML (SVM) | ○ | ● | ○ | ○ | 2015 |
| EASE-MM (doi, server) | 1D | ML (SVM) | ○ | ○ | ○ | ○ | 2016 |
| MAESTRO (doi, server) | 3D | energy function (knowledge-based), ML facilitated (MLR, NN, SVM) | ● | ○ | ○ | ● | 2016 |
| DNpro (paper, server) | 1D | NN (DBN) | ○ | ○ | ○ | ○ | 2016 |
| DynaMut (doi, server) | 3D | ML (RF), meta-predictor | ○ | ● | ○ | ○ | 2018 |
| HoTMuSiC (doi, server) | 3D | energy function (knowledge-based), NN facilitated | ● | ○ | ○ | ○ | 2019 |
| DeepDDG (doi, server) | 3D | NN meta-predictor available |
○ | ● | ○ | ○ | 2019 |
| BoostDDG (doi, server) | 1D | ML (XGBoost) | ○ | ● | ○ | ○ | 2020 |
| PremPS (doi, server) | 3D | ML (RF regression) | ○ | ● | ○ | ○ | 2020 |
| SAAFEC-SEQ (doi, server) | 1D | ML (gradient boosting decision tree) | ○ | ○ | ○ | ○ | 2021 |