THPep: A machine learning-based approach for predicting tumor homing peptides. - Watshara Shoombuatong; Nalini Schaduangrat; Reny Pratiwi; Chanin Nantasenamat (Comput. Biol. Chem., 2019)

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation. - Md. Mehedi Hasan 002; Nalini Schaduangrat; Shaherin Basith; Gwang Lee; Watshara Shoombuatong; Balachandran Manavalan (Bioinform., 2020)

HCVpred : A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors. - Aijaz Ahmad Malik; Chuleeporn Phanus-umporn; Nalini Schaduangrat; Watshara Shoombuatong; Chartchalerm Isarankura-Na-Ayudhya; Chanin Nantasenamat (J. Comput. Chem., 2020)

Towards reproducible computational drug discovery. - Nalini Schaduangrat; Samuel Lampa; Saw Simeon; Matthew Paul Gleeson; Ola Spjuth; Chanin Nantasenamat (J. Cheminformatics, 2020)

NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides. - Phasit Charoenkwan; Nalini Schaduangrat; Pietro Lio'; Mohammad Ali Moni; Balachandran Manavalan; Watshara Shoombuatong (Comput. Biol. Medicine, 2022)

SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins. - Phasit Charoenkwan; Nalini Schaduangrat; Mohammad Ali Moni; Pietro Lio'; Balachandran Manavalan; Watshara Shoombuatong (Comput. Biol. Medicine, 2022)

Improved prediction and characterization of blood-brain barrier penetrating peptides using estimated propensity scores of dipeptides. - Phasit Charoenkwan; Pramote Chumnanpuen; Nalini Schaduangrat; Pietro Lio'; Mohammad Ali Moni; Watshara Shoombuatong (J. Comput. Aided Mol. Des., 2022)

PSRQSP: An effective approach for the interpretable prediction of quorum sensing peptide using propensity score representation learning. - Phasit Charoenkwan; Pramote Chumnanpuen; Nalini Schaduangrat; Changmin Oh; Balachandran Manavalan; Watshara Shoombuatong (Comput. Biol. Medicine, 2023)

TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides. - Phasit Charoenkwan; Sasikarn Kongsompong; Nalini Schaduangrat; Pramote Chumnanpuen; Watshara Shoombuatong (BMC Bioinform., 2023)

StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens. - Phasit Charoenkwan; Nalini Schaduangrat; Watshara Shoombuatong (BMC Bioinform., 2023)

DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists. - Nalini Schaduangrat; Nuttapat Anuwongcharoen; Phasit Charoenkwan; Watshara Shoombuatong (J. Cheminformatics, 2023)

The role of ncRNA regulatory mechanisms in diseases - case on gestational diabetes. - Dong Gao; Liping Ren; Yu-Duo Hao; Nalini Schaduangrat; Xiao-Wei Liu; Shi-Shi Yuan; Yu-He Yang; Yan Wang; Watshara Shoombuatong; Hui Ding (Briefings Bioinform., 2024)

A comprehensive review and evaluation of machine learning-based approaches for identifying tumor T cell antigens. - Watshara Shoombuatong; Saeed Ahmed; S. M. Hasan Mahmud; Nalini Schaduangrat (Comput. Biol. Chem., 2025)

M3S-ALG: Improved and robust prediction of allergenicity of chemical compounds by using a novel multi-step stacking strategy. - Phasit Charoenkwan; Nalini Schaduangrat; Le Thi Phan; Balachandran Manavalan; Watshara Shoombuatong (Future Gener. Comput. Syst., 2025)

M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy. - Nalini Schaduangrat; Hathaichanok Chuntakaruk; Thanyada Rungrotmongkol; Pakpoom Mookdarsanit; Watshara Shoombuatong (BMC Bioinform., 2025)