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Review and comparative analysis of machine learning-based predictors for predicting and analyzing of anti-angiogenic peptides


Phasit Charoenkwan, Wararat Chiangjong, Md. Mehedi Hasan, Chanin Nantasenamat and Watshara Shoombuatong*  


Cancer is one of the leading causes of death worldwide and underlying this is angiogenesis that represents one of the hallmarks of cancer. Ongoing effort is already under way in the discovery of anti-angiogenic peptides (AAPs) as a promising therapeutic route by tackling the formation of new blood vessels. As such, the identification of AAPs constitutes a viable path for understanding their mechanistic properties pertinent for the discovery of new anti-cancer drugs. In spite of the abundance of peptide sequences in public databases, experimental efforts in the identification of anti-angiogenic peptides have progressed very slowly owing to its high expenditures and laborious nature. Owing to its inherent ability to make sense of large volumes of data, machine learning (ML) represents a lucrative technique that can be harnessed for peptide-based drug discovery. In this review, we conducted a comprehensive and comparative analysis of ML-based AAP predictors in terms of their employed feature descriptors, ML algorithms, cross-validation methods and prediction performance. Moreover, the common framework of these AAP predictors and their inherent weaknesses are also discussed. Particularly, we explore future perspectives for improving the prediction accuracy and model interpretability, which represents an interesting avenue for overcoming some of the inherent weaknesses of existing AAP predictors. We anticipate that this review would assist researchers in the rapid screening and identification of promising AAPs for clinical use.


anti-angiogenic peptides, therapeutic peptides, classification, machine learning, feature representation, feature selection


Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Pediatric Translational Research Unit, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok

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