| HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation MM Hasan, N Schaduangrat, S Basith, G Lee, W Shoombuatong, ... Bioinformatics 36 (11), 3350-3356, 2020 | 176 | 2020 |
| ACPred: a computational tool for the prediction and analysis of anticancer peptides N Schaduangrat, C Nantasenamat, V Prachayasittikul, W Shoombuatong Molecules 24 (10), 1973, 2019 | 175 | 2019 |
| Towards reproducible computational drug discovery N Schaduangrat, S Lampa, S Simeon, MP Gleeson, O Spjuth, ... Journal of cheminformatics 12, 1-30, 2020 | 166 | 2020 |
| Unraveling the bioactivity of anticancer peptides as deduced from machine learning W Shoombuatong, N Schaduangrat, C Nantasenamat EXCLI journal 17, 734, 2018 | 123 | 2018 |
| Meta-iAVP: a sequence-based meta-predictor for improving the prediction of antiviral peptides using effective feature representation N Schaduangrat, C Nantasenamat, V Prachayasittikul, W Shoombuatong International journal of molecular sciences 20 (22), 5743, 2019 | 107 | 2019 |
| iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides P Charoenkwan, J Yana, N Schaduangrat, C Nantasenamat, MM Hasan, ... Genomics 112 (4), 2813-2822, 2020 | 97 | 2020 |
| Exploring the epigenetic drug discovery landscape V Prachayasittikul, P Prathipati, R Pratiwi, C Phanus-Umporn, AA Malik, ... Expert opinion on drug discovery 12 (4), 345-362, 2017 | 89 | 2017 |
| Tackling the Antibiotic Resistance Caused by Class A β-Lactamases through the Use of β-Lactamase Inhibitory Protein W Eiamphungporn, N Schaduangrat, AA Malik, C Nantasenamat International journal of molecular sciences 19 (8), 2222, 2018 | 80 | 2018 |
| THPep: A machine learning-based approach for predicting tumor homing peptides W Shoombuatong, N Schaduangrat, R Pratiwi, C Nantasenamat Computational biology and chemistry 80, 441-451, 2019 | 72 | 2019 |
| iQSP: a sequence-based tool for the prediction and analysis of quorum sensing peptides via Chou’s 5-steps rule and informative physicochemical properties P Charoenkwan, N Schaduangrat, C Nantasenamat, T Piacham, ... International Journal of Molecular Sciences 21 (1), 2020 | 62 | 2020 |
| PAAP: A web server for predicting antihypertensive activity of peptides TS Win, N Schaduangrat, V Prachayasittikul, C Nantasenamat, ... Future medicinal chemistry 10 (15), 1749-1767, 2018 | 55 | 2018 |
| CryoProtect: a web server for classifying antifreeze proteins from nonantifreeze proteins R Pratiwi, AA Malik, N Schaduangrat, V Prachayasittikul, JES Wikberg, ... Journal of Chemistry 2017 (1), 9861752, 2017 | 54 | 2017 |
| PVPred-SCM: improved prediction and analysis of phage virion proteins using a scoring card method P Charoenkwan, S Kanthawong, N Schaduangrat, J Yana, ... Cells 9 (2), 353, 2020 | 50 | 2020 |
| TargetAntiAngio: a sequence-based tool for the prediction and analysis of anti-angiogenic peptides V Laengsri, C Nantasenamat, N Schaduangrat, P Nuchnoi, ... International journal of molecular sciences 20 (12), 2950, 2019 | 48 | 2019 |
| SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins P Charoenkwan, N Schaduangrat, MA Moni, B Manavalan, ... Computers in Biology and Medicine 146, 105704, 2022 | 39 | 2022 |
| Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study N Suvannang, L Preeyanon, AA Malik, N Schaduangrat, ... RSC advances 8 (21), 11344-11356, 2018 | 36 | 2018 |
| Towards predicting the cytochrome P450 modulation: from QSAR to proteochemometric modeling W Shoombuatong, P Prathipati, V Prachayasittikul, N Schaduangrat, ... Current drug metabolism 18 (6), 540-555, 2017 | 36 | 2017 |
| Towards understanding aromatase inhibitory activity via QSAR modeling W Shoombuatong, N Schaduangrat, C Nantasenamat EXCLI journal 17, 688, 2018 | 35 | 2018 |
| ABCpred: a webserver for the discovery of acetyl-and butyryl-cholinesterase inhibitors AA Malik, SC Ojha, N Schaduangrat, C Nantasenamat Molecular Diversity, 1-21, 2022 | 34 | 2022 |
| Data mining for the identification of metabolic syndrome status A Worachartcheewan, N Schaduangrat, V Prachayasittikul, ... EXCLI journal 17, 72, 2018 | 26 | 2018 |
