The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
In Silico Models for Designing and Discovering Novel Anticancer Peptides
Open accessAbstract
Use of therapeutic peptides in cancer therapy has been receiving considerable attention in the recent years. Present study describes the development of computational models for predicting and discovering novel anticancer peptides. Preliminary analysis revealed that Cys, Gly, Ile, Lys, and Trp are dominated at various positions in anticancer peptides. Support vector machine models were developed using amino acid composition and binary profiles as input features on main dataset that contains experimentally validated anticancer peptides and random peptides derived from SwissProt database. In addition, models were developed on alternate dataset that contains antimicrobial peptides instead of random peptides. Binary profiles-based model achieved maximum accuracy 91.44% with MCC 0.83. We have developed a webserver, which would be helpful in: (i) predicting minimum mutations required for improving anticancer potency; (ii) virtual screening of peptides for discovering novel anticancer peptides, and (iii) scanning natural proteins for identification of anticancer peptides (http://crdd.osdd.net/raghava/anticp/).
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Studies on anticancer activities of antimicrobial peptides
Hoskin, D. W., & Ramamoorthy, A. (2008) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2007.11.008 · Cited by 1,296
APD2: the updated antimicrobial peptide database and its application in peptide design.
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Anti-cancer activity of targeted pro-apoptotic peptides
Ellerby, H. M., Arap, W., Ellerby, L. M., Kain, R., Andrusiak, R., Rio, G. D., Krajewski, S., Lombardo, C. R., Rao, R., Ruoslahti, E., et al. (1999) · Nature Medicine · doi:10.1038/12469 · Cited by 936
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Cited by
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
“Likewise, Tyagi et al. 2013 developed an SVM-based platform for identifying new anti-cancer peptides [115].”
Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
Basith, S., Manavalan, B., Hwan Shin, T., & Lee, G. (2020) · Medicinal Research Reviews · doi:10.1002/med.21658 · Cited by 312
Bridging machine learning and peptide design for cancer treatment: a comprehensive review
Rezaee, K., & Eslami, H. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11148-3 · Cited by 34
Metadata
- Authors
- Tyagi, A., Kapoor, P., Kumar, R., Chaudhary, K., Gautam, A., & Raghava, G. P. S.
- Venue
- Scientific Reports
- Published
- 2013-10-18
- Volume
- 3
- Issue
- 1
- Pages
- 2984-2984
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2118911320
- Cited by
- 320
- References
- 21
- FWCI
- 6.02
- Type
- journal article
- Language
- en
- Open access
- gold
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- Tyagi, A., Kapoor, P., Kumar, R., Chaudhary, K., Gautam, A., & Raghava, G. P. S. (2013). In Silico Models for Designing and Discovering Novel Anticancer Peptides. Scientific Reports. https://doi.org/10.1038/srep02984
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