N. Schaduangrat

发表

Multidisciplinary approaches for targeting the secretase protein family as a therapeutic route for Alzheimer's disease

B. Vincent, C. Nantasenamat, A. Malik, 2019, Medicinal research reviews.

TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides

Virapong Prachayasittikul, Chanin Nantasenamat, Watshara Shoombuatong, 2019, International journal of molecular sciences.

Data mining for the identification of metabolic syndrome status

C. Nantasenamat, V. Prachayasittikul, N. Schaduangrat, 2018, EXCLI journal.

CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins

Virapong Prachayasittikul, Chanin Nantasenamat, Watshara Shoombuatong, 2017 .

The significance of naturally occurring neuraminidase quasispecies of H5N1 avian influenza virus on resistance to oseltamivir: a point of concern.

T. Rungrotmongkol, P. Puthavathana, Hatairat Lerdsamran, 2016, The Journal of general virology.

Tackling the Antibiotic Resistance Caused by Class A β-Lactamases through the Use of β-Lactamase Inhibitory Protein

C. Nantasenamat, A. Malik, N. Schaduangrat, 2018, International journal of molecular sciences.

SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins

Balachandran Manavalan, Phasit Charoenkwan, M. Moni, 2022, Comput. Biol. Medicine.

NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides

P. Lio’, Balachandran Manavalan, Phasit Charoenkwan, 2022, Comput. Biol. Medicine.

Proteomic and bioinformatic discovery of biomarkers for diabetic nephropathy

C. Nantasenamat, N. Schaduangrat, Chadinee Thippakorn, 2018, EXCLI journal.

Exploring the origin of phosphodiesterase inhibition via proteochemometric modeling

C. Nantasenamat, B. Rasti, N. Schaduangrat, 2017 .

Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework

Balachandran Manavalan, Phasit Charoenkwan, M. Moni, 2022, iScience.

Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides

Phasit Charoenkwan, Pramote Chumnanpuen, M. Moni, 2022, Journal of Computer-Aided Molecular Design.

iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides

Phasit Charoenkwan, M. Moni, W. Shoombuatong, 2022, ACS omega.

Oxidative responses and defense mechanism of hyperpigmented P. aeruginosa as characterized by proteomics and metabolomics

C. Nantasenamat, C. Isarankura-Na-Ayudhya, V. Prachayasittikul, 2018, EXCLI journal.

StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy

Phasit Charoenkwan, M. Moni, W. Shoombuatong, 2022, Scientific Reports.

ABCpred: a webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors

C. Nantasenamat, A. Malik, N. Schaduangrat, 2021, Molecular Diversity.

ABCpred: a webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors

C. Nantasenamat, A. Malik, N. Schaduangrat, 2021, Molecular diversity.

Towards reproducible computational drug discovery

Ola Spjuth, Nalini Schaduangrat, Samuel Lampa, 2020, Journal of Cheminformatics.

SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides

Phasit Charoenkwan, M. Moni, W. Shoombuatong, 2022, ACS omega.

Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Chanin Nantasenamat, Watshara Shoombuatong, Nalini Schaduangrat, 2018, EXCLI journal.

Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation

Virapong Prachayasittikul, Nalini Schaduangrat, Chanin Nantasenamat, 2019, International journal of molecular sciences.

THPep: A machine learning-based approach for predicting tumor homing peptides

Nalini Schaduangrat, Chanin Nantasenamat, Watshara Shoombuatong, 2019, Comput. Biol. Chem..

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

Virapong Prachayasittikul, Chanin Nantasenamat, Watshara Shoombuatong, 2019, Molecules.

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation

Nalini Schaduangrat, Watshara Shoombuatong, Md Mehedi Hasan, 2020, Bioinform..

iBitter-SCM: Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides.

Chanin Nantasenamat, Phasit Charoenkwan, Watshara Shoombuatong, 2020, Genomics.

Exploring the epigenetic drug discovery landscape

Virapong Prachayasittikul, Chanin Nantasenamat, Veda Prachayasittikul, 2017, Expert opinion on drug discovery.

PAAP: a web server for predicting antihypertensive activity of peptides.

Virapong Prachayasittikul, Chanin Nantasenamat, Watshara Shoombuatong, 2018, Future medicinal chemistry.

Large-scale classification of P-glycoprotein inhibitors using SMILES-based descriptors

A P Toropova, V Prachayasittikul, A Worachartcheewan, 2017, SAR and QSAR in environmental research.

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

P. Lio’, Phasit Charoenkwan, M. Hasan, 2022, EXCLI journal.

Proteochemometric Modeling for Drug Repositioning

Nalini Schaduangrat, Chanin Nantasenamat, Nuttapat Anuwongcharoen, 2019, In Silico Drug Design.

Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study

Apilak Worachartcheewan, Chanin Nantasenamat, Watshara Shoombuatong, 2018, RSC advances.

Computational study on the origin of the cancer immunotherapeutic potential of B and T cell epitope peptides.

Chanin Nantasenamat, Hao Li, Nalini Schaduangrat, 2017, Molecular bioSystems.

PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning

Balachandran Manavalan, Phasit Charoenkwan, W. Shoombuatong, 2023, Comput. Biol. Medicine.

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties

Nalini Schaduangrat, Chanin Nantasenamat, Watshara Shoombuatong, 2019, International journal of molecular sciences.

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Nalini Schaduangrat, Sakawrat Kanthawong, Watshara Shoombuatong, 2020, Cells.

HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors

Chartchalerm Isarankura-Na-Ayudhya, Nalini Schaduangrat, Chanin Nantasenamat, 2020, J. Comput. Chem..

Recent development of machine learning-based methods for the prediction of defensin family and subfamily

Phasit Charoenkwan, W. Shoombuatong, N. Schaduangrat, 2022, EXCLI journal.

DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists

Phasit Charoenkwan, W. Shoombuatong, N. Schaduangrat, 2023, Journal of Cheminformatics.

Towards understanding aromatase inhibitory activity via QSAR modeling

Chanin Nantasenamat, Watshara Shoombuatong, Nalini Schaduangrat, 2018, EXCLI journal.

TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus.

Phasit Charoenkwan, W. Shoombuatong, N. Schaduangrat, 2023, PloS one.

ERpred: a web server for the prediction of subtype-specific estrogen receptor antagonists

C. Nantasenamat, A. Malik, N. Schaduangrat, 2021, PeerJ.

Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction

N. Schaduangrat, Nutta Homdee, P. Chumnanpuen, 2024, Scientific reports.

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

N. Schaduangrat, Nutta Homdee, Watshara Shoombuatong, 2023, Scientific reports.

The role of ncRNA regulatory mechanisms in diseases—case on gestational diabetes

N. Schaduangrat, Shi-Shi Yuan, Dong Gao, 2023, Briefings in bioinformatics.

Stack-HDAC3i: A high-precision identification of HDAC3 inhibitors by exploiting a stacked ensemble-learning framework.

N. Schaduangrat, Lawankorn Mookdarsanit, Ittipat Meewan, 2024, Methods.

MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information

Phasit Charoenkwan, N. Schaduangrat, Phisit Khemawoot, 2024, Scientific reports.

StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens

Phasit Charoenkwan, N. Schaduangrat, Watshara Shoombuatong, 2023, BMC Bioinformatics.