Table 3.
Machine learning prediction of antimicrobial peptides
| Tool name | URL | Algorithms | Features | Year | Ref |
|---|---|---|---|---|---|
| AntiBP | http://crdd.osdd.net/raghava/antibp2 | SVM,QM,ANN | Single label | 2007 | [17] |
| CAMP | http://www.bicnirrh.res.in/antimicrobial | SVM, RF, DA | Single label | 2010 | [18, 53] |
| http://amp.biosino.org/ | BLASTP, NNA | Single label | 2011 | [54] | |
| AMPA | http://tcoffee.crg.cat/apps/ampa | AMP region scan | 2012 | [55] | |
| ANFIS | ANFIS | Single label | 2012 | [56] | |
| Peptide Locator | http://bioware.ucd.ie/ | BRNN | Single label | 2013 | [57] |
| iAMP-2L | http://www.jci-bioinfo.cn/iAMP-2L | FKNN | Two-level, Multi-label | 2013 | [52] |
| DBAASP | https://dbaasp.org/prediction/general | thresholds | 2014 | [33] | |
| SVM-LZ | NG (BioMed Research International) | SVM | Single label | 2015 | [58] |
| ADAM | http://bioinformatics.cs.ntou.edu.tw/ADAM/ | SVM, HMM | Single label | 2015 | [59] |
| MLAMP | http://www.jci-bioinfo.cn/MLAMP | RF – ML-SMOTE | Multi-label | 2016 | [60] |
| iAMPpred | http://cabgrid.res.in:8080/amppred/ | SVM | Single label | 2017 | [61] |
| AmPEP | http://cbbio.cis.umac.mo/software/AmPEP/ | RF | Single label | 2018 | [62] |
| AMP scanner | www.ampscanner.com | DNN | Single label, Large scale | 2018 | [63] |
| AntiMPmod | https://webs.iiitd.edu.in/raghava/antimpmod/ | SVM | Single label, PTM/3D | 2018 | [64] |
| dbAMP | http://csb.cse.yzu.edu.tw/dbAMP/ | RF | Single label | 2019 | [65] |
| AMAP | http://faculty.pieas.edu.pk/fayyaz/software.html#AMAP | SVM, XGBoost | Multi-label | 2019 | [66] |
| NA | IDQD | Single label | 2019 | [67] | |
| AMPfun | http://fdblab.csie.ncu.edu.tw/AMPfun/index.html | CART | Multi-label | 2020 | [68] |
| AMP0 | http://ampzero.pythonanywhere.com | ZSL, FSL | Single label, Species-specific | 2020 | [69] |
| MIV-RF | NA | RF | Single label, Sequence | 2020 | [70] |
| Deep-AmPEP30 | https://cbbio.cis.um.edu.mo/AxPEP | CNN | Genome search | 2020 | [71] |
| ACEP | https://github.com/Fuhaoyi/ACEP | DNN | high-throughput predictions | 2020 | [72] |
| IAMPE | http://cbb1.ut.ac.ir/ | KNN, SVM, RF | Single label | 2020 | [73] |
| Macrel | https://big-data-biology.org/software/macrel. | RF | Genome search | 2020 | [74] |
| https://github.com/mtyoumans/lstm_peptides | LSTM RNN | Single label | 2020 | [75] | |
| ampir | https://github.com/legana/ampir | SVM | Genome wide | 2020 | [76] |
| amPEPpy | https://github.com/tlawrence3/amPEPpy | RF | Genome wide | 2020 | [77] |
| Ensemble-AMPPred | Ensemble model | Single label | 2021 | [78] |