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Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Watshara Shoombuatong¹, Nalini Schaduangrat¹, Chanin Nantasenamat¹

¹Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.

|September 8, 2018

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.

Insights

Machine learning is advancing the study of anticancer peptides, offering new therapeutic strategies to combat cancer

Area of Science:

Biotechnology
Computational Biology
Oncology

Background:

Cancer is a major global health challenge with significant morbidity, mortality, and economic impact.
Current cancer therapies face limitations in success rates and tumor recurrence, driving the need for novel treatments.
Peptide-based therapeutics offer advantages like selectivity, potency, safety, and cost-effectiveness, but require careful design.

Purpose of the Study:

To review the current applications of machine learning in analyzing anticancer peptide bioactivity.
To highlight the potential of artificial intelligence in peptide drug discovery.
To discuss future directions for machine learning in anticancer peptide research.

Main Methods:

Literature review of machine learning applications in anticancer peptide studies.
Analysis of accumulated data on bioactive and therapeutic peptides.
Exploration of artificial intelligence techniques for data interpretation.

Main Results:

Machine learning is effectively utilized to analyze large datasets of anticancer peptides.
AI facilitates a deeper understanding of peptide bioactivity and therapeutic potential.
The review consolidates current knowledge and identifies trends in the field.

Conclusions:

Machine learning holds significant promise for accelerating the discovery and development of novel anticancer peptide therapeutics.
Integrating AI with peptide research can overcome existing challenges in drug design.
Further research in this interdisciplinary area is crucial for advancing cancer treatment.

Read full analysis

Keywords:

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

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