Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Authors

  • Watshara Shoombuatong Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
  • Nalini Schaduangrat Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
  • Chanin Nantasenamat Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand

DOI:

https://doi.org/10.17179/excli2018-1447

Keywords:

cancer, anticancer, antitumor, anticancer peptides, host defense peptides, bioactivity, machine learning, QSAR

Abstract

Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. In spite of advancements in cancer therapy, the low success rate and recurrence of tumor has necessitated the ongoing search for new therapeutic agents. Aside from drugs based on small molecules and protein-based biopharmaceuticals, there has been an intense effort geared towards the development of peptide-based therapeutics owing to its favorable and intrinsic properties of being relatively small, highly selective, potent, safe and low in production costs. In spite of these advantages, there are several inherent weaknesses that are in need of attention in the design and development of therapeutic peptides. An abundance of data on bioactive and therapeutic peptides have been accumulated over the years and the burgeoning area of artificial intelligence has set the stage for the lucrative utilization of machine learning to make sense of these large and high-dimensional data. This review summarizes the current state-of-the-art on the application of machine learning for studying the bioactivity of anticancer peptides along with future outlook of the field. Data and R codes used in the analysis herein are available on GitHub at https://github.com/Shoombuatong2527/anticancer-peptides-review.

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Published

2018-07-25

How to Cite

Shoombuatong, W., Schaduangrat, N., & Nantasenamat, C. (2018). Unraveling the bioactivity of anticancer peptides as deduced from machine learning. EXCLI Journal, 17, 734–752. https://doi.org/10.17179/excli2018-1447

Issue

Vol. 17 (2018)

Section

Review articles

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