Research Pipelines

Research Projects

We focus on three research areas involving the predictive and de novo design of potential drug candidates:

1. Stacking Ensemble Learning, where we train a model to learn from its previous knowledge.

2. Active Learning mimics the human learning paradigm, which requires active and constructive feedback.

3. A generative advisory network that can generate new information based on the previous experimental data.

Our pipeline focuses on Chemical, Peptide, and Drug formulation.

Research Papers

1. Zetta, D. N., & Srisongkram, T. (2026). Data-efficient learning for accurate identification of MAPK1 inhibitors using an active meta-deep learning framework. Journal of Cheminformatics.

2. Huynh, A. D., Khampasri, P., Janthanet, P., Pattiyamongkhonkul, P., & Srisongkram, T. (2026). Accurate prediction of anticancer peptides using a stacking ensemble of convolutional and transformer models with conjoint sequence representations. Computers in Biology and Medicine, 202, 111463. 

3. Zetta, D. N., Shoombuatong, W., & Srisongkram, T. (2025). Active Stacking-Deep Learning with Strategic Sampling for Small and Imbalanced Chemical Toxicity Prediction. ACS omega, 10(45), 53907-53926.

4. Duy, H. A., & Srisongkram, T. (2025). Accurate structure-activity relationship prediction of antioxidant peptides using a multimodal deep learning framework: HA Duy, T. Srisongkram. Journal of Cheminformatics, 17(1), 166.

5. Intan, A. E. K., Zetta, D. N., Jarukamjorn, K., & Srisongkram, T. (2025). MetaAMPK: Accurate Prediction of Adenosine Monophosphate-Activated Protein Kinase Activators Using a Meta-Learner Neural Network. ACS Omega.

6. Duy, H. A., & Srisongkram, T. (2025). Toward Explainable Carcinogenicity Prediction: An Integrated Cheminformatics Approach and Consensus Framework for Possibly Carcinogenic Chemicals. Journal of Chemical Information and Modeling.

7. Duy, H. A., & Srisongkram, T. (2025). Multimodal Deep Learning for Generating Potential Anti-Dengue Peptides. ACS Omega, 10 (34) 38653–38674

8. Intan, A. E. K., Jarukamjorn, K., & Srisongkram, T. (2025). StackNAFLD: An Accurate Stacking Ensemble Learning Targeting NAFLD Treatment. ACS Omega, 10 (33), 37096–37114

9. Piyasawetkul, T., Tiyaworanant, S., & Srisongkram, T. (2025). AppHerb: Language Model for Recommending Traditional Thai Medicine. AI, 6(8), 170.

10. Duy, H. A., & Srisongkram, T. (2025). A hybrid framework of generative deep learning for antiviral peptide discovery. Scientific Reports, 15(1), 25554.

11. Zetta, D. N., & Srisongkram, T. (2025). Stacking Ensemble Neural Network for Chemical Safety Assessment: A Case Study of Thyroid Peroxidase and Natural Product Screening. ACS omega.

12. Duy, H. A., & Srisongkram, T. (2025). Deep Generative Models for the Discovery of Antiviral Peptides Targeting Dengue Virus: A Systematic Review. International Journal of Molecular Sciences, 26(13), 6159.

13. Duy, H. A., & Srisongkram, T. (2025). Protecting your skin: a highly accurate LSTM network integrating conjoint features for predicting chemical-induced skin irritation. Journal of Cheminformatics, 17(1), 1-19.

14. Duy, H. A., & Srisongkram, T. (2025). Bidirectional Long Short-Term Memory (BiLSTM) Neural Networks with Conjoint Fingerprints: Application in Predicting Skin-Sensitizing Agents in Natural Compounds. Journal of Chemical Information and Modeling.

15. Duy, H. A., & Srisongkram, T. (2025). Comparative Analysis of Recurrent Neural Networks with Conjoint Fingerprints for Skin Corrosion Prediction. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.4c02062

16. Boonsom, S., Chamnansil, P., Boonseng, S., & Srisongkram, T. (2025). ToxSTK: A multi-target toxicity assessment utilizing molecular structure and stacking ensemble learning. Computers in Biology and Medicine, 185, 109480.

17. Kaimuangpak, K., Srisongkram, T., Lehtonen, M., Rautio, J., & Weerapreeyakul, N. (2024). The metabolic response of HepG2 cells to extracellular vesicles derived from Raphanus sativus L. var. caudatus Alef microgreens probed by chemometrics-assisted LC-MS/MS analysis. Food Chemistry, 140833.

18. Rosalina, R., Kamwilaisak, K., Sutthanut, K., Srisongkram, T., & Weerapreeyakul, N. (2024). Probing the stability and quality of the cellulose-based Pickering emulsion containing sesamolin-enriched sesame oil by chemometrics-assisted ATR-FTIR spectroscopy. Food Chemistry, 452, 139555.

19. Srisongkram, T. (2024). DeepRA: A novel deep learning-read-across framework and its application in non-sugar sweeteners mutagenicity prediction. Computers in Biology and Medicine, 178, 108731. 

20. Pocasap, P., Tamprasit, K., Rungsri, T., Kaimuangpak, K., Srisongkram, T., Katekaew, S., Kamwilaisak, K., Puthongking, P., & Weerapreeyakul, N. (2024). Pickering Emulsion of Oleoresin from Dipterocarpus alatus Roxb. Ex G. Don and Its Antiproliferation in Colon (HCT116) and Liver (HepG2) Cancer Cells. Molecules, 29(11), 2695. 

21. Srisongkram, T., & Tookkane, D. (2024). Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein. Biophysical Chemistry, 307, 107179.

22. Srisongkram, T., Syahid, N. F., Piyasawetkul, T., Thirawatthanasak, P., Khamtang, P., Sawasnopparat, N., Tookkane, D., Weerapreeyakul, N., & Puthongking, P. (2023). Prediction of Spheroid Cell Death Using Fluorescence Staining and Convolutional Neural Networks. Chemical Research in Toxicology, 36(12), 1980–1989.

23. Srisongkram, T. (2023). Ensemble Quantitative Read-Across Structure–Activity Relationship Algorithm for Predicting Skin Cytotoxicity. Chemical Research in Toxicology, 36(12), 1961–1972.

24. Srisongkram T., Syahid N. F., Tookkane D., Weerapreeyakul N., & Puthongking P. Stacked ensemble learning on HaCaT cytotoxicity for skin irritation prediction: A case study on dipterocarpol. Food and Chemical Toxicology, 2023 Nov 1; 181, 114115.

25. Tiranakwit T, Puangpun W, Tamprasit K, Wichai N, Siriamornpun S, Srisongkram T, Weerapreeyakul N. (2023).  Phytochemical Screening on Phenolic, Flavonoid Contents, and Antioxidant Activities of Six Indigenous Plants Used in Traditional Thai Medicine. International Journal of Molecular Sciences. 2023; 24(17):13425. 

26. Syahid, N. F., Weerapreeyakul, N., & Srisongkram, T. (2023). StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction. ACS Omega, 8, 23, 20881–20891. 

27. Srisongkram, T., Khamtang, P., & Weerapreeyakul, N. (2023). Prediction of KRASG12C inhibitors using conjoint fingerprint and machine learning-based QSAR models. Journal of Molecular Graphics and Modelling, 122, 108466. 

28. So, V., Poul, P., Oeung, S., Srey, P., Mao, K., Ung, H., Eng, P., Heim, M., Srun, M., Chheng, C., Chea, S., Srisongkram, T., & Weerapreeyakul, N. (2023). Bioactive Compounds, Antioxidant Activities, and HPLC Analysis of Nine Edible Sprouts in Cambodia. Molecules, 28(6), Article 6. 

29. Ratha J, Yongram C, Panyatip P, Powijitkul P, Siriparu P, Datham S, Priprem A, Srisongkram T, Puthongking P. Polyphenol and Tryptophan Contents of Purple Corn (Zea mays L.) Variety KND and Butterfly Pea (Clitoria ternatea) Aqueous Extracts: Insights into Phytochemical Profiles with Antioxidant Activities and PCA Analysis. Plants. 2023; 12(3):603. 

30. 16. Srisongkram T, Weerapreeyakul N. Drug Repurposing against KRAS Mutant G12C: A Machine Learning, Molecular Docking, and Molecular Dynamics Study. International Journal of Molecular Sciences. 2023; 24(1):669

31. Siriparu P, Panyatip P, Pota T, Ratha J, Yongram C, Srisongkram T, Sungthong B, Puthongking P. Effect of Germination and Illumination on Melatonin and Its Metabolites, Phenolic Content, and Antioxidant Activity in Mung Bean Sprouts. Plants. 2022 Nov 6;11(21):2990.

32. Juengsanguanpornsuk W, Kitisripanya T, Boonsnongcheep P, Yusakul G, Srisongkram T, Sakamoto S, Putalun W. Improvement in the binding specificity of anti-isomiroestrol antibodies by expression as fragments under oxidizing conditions inside the SHuffle T7 E. coli cytoplasm. Bioscience, Biotechnology, and Biochemistry. 2022 Oct;86(10):1368-77.

33. Srisongkram T, Bahrami K, Järvinen J, Timonen J, Rautio J, Weerapreeyakul N. Development of Sesamol Carbamate-L-Phenylalanine Prodrug Targeting L-Type Amino Acid Transporter1 (LAT1) as a Potential Antiproliferative Agent against Melanoma. International Journal of Molecular Sciences. 2022 Jul 30;23(15):8446.

34. Srisongkram T, Weerapreeyakul N. Route of intracellular uptake and cytotoxicity of sesamol, sesamin, and sesamolin in human melanoma SK-MEL-2 cells. Biomedicine & Pharmacotherapy. 2022 Feb 1; 146:112528.

35. Srisongkram T, Waithong S, Thitimetharoch T, Weerapreeyakul N. Machine learning and in vitro chemical screening of potential α-amylase and α-glucosidase inhibitors from Thai indigenous plants. Nutrients. 2022 Jan 9;14(2):267.

36. Srisongkram, T., Weerapreeyakul, N., & Thumanu, K. (2020). Evaluation of melanoma (SK-MEL-2) cell growth between three-dimensional (3D) and two-dimensional (2D) cell cultures with fourier transform infrared (FTIR) microspectroscopy. International Journal of Molecular Sciences, 21(11), 4141.

37. Srisongkram, T., Weerapreeyakul, N., Kärkkäinen, J., & Rautio, J. (2019). Role of L-type amino acid transporter 1 (LAT1) for the selective cytotoxicity of sesamol in human melanoma cells. Molecules, 24(21), 3869.

38. Srisongkram, T., & Weerapreeyakul, N. (2019). Validation of cell-based assay for quantification of sesamol uptake and its application for measuring target exposure. Molecules, 24(19), 3522.

List of Tentative Journals

1. Journal of Chemical Information and Modeling  (T1)

2. ACS Central Sciences (T1)

3. Chemical Science  (T1)

4. Nature Computational Science (T1)

5. Nature Machine Intelligence (T1)

6. Journal of Cheminformatics (T1)

7. International Journal of Biological Macromolecules (T1)

8. Neurocomputing (T1)

9. Sustainable Chemistry and Pharmacy (Q1)

10. Computers in Biology and Medicine (T1)

11. European Journal of Pharmaceutical Sciences (Q1)

12. Asian Journal of Pharmaceutical Sciences (T1)

13. International Journal of Pharmaceutics (Q1)

14. Molecular Pharmaceutics (Q1)

15. Journal of Pharmaceutical Analysis (Q1)

16. Scandinavian Journal of Medicine & Science in Sports

17. Sports Medicine-Open

18. American Journal of Sports Medicine

19. Journal of Science and Medicine in Sport

20. Medicine & Science in Sports & Exercise

21. Computer methods in biomechanics and biomedical engineering

22. Journal of Sports Science

23. Journal of Sports Science and Medicine

24. International journal of performance analysis in sport