ArticleInternational journal of molecular sciences2021
Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Anticancer Potential of Antimicrobial Peptides: Mechanisms, Clinical Application, Challenges, and Future Prospects.Drug development research · 2026Review
- Prediction of Chemically Modified Antimicrobial Peptides and Their Sub-functional Activities Using Hybrid Features.Probiotics and antimicrobial proteins · 2026Article
- Accurate prediction of toxicity peptide and its function using multi-view tensor learning and latent semantic learning framework.Bioinformatics (Oxford, England) · 2025Article
- PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial peptide identification with remarkable accuracy.Bioinformatics (Oxford, England) · 2024Article
- LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets.Molecular diversity · 2024Article
- Article
- Role of Anti-Cancer Peptides as Immunomodulatory Agents: Potential and Design Strategy.Pharmaceutics · 2022Review
- Emerging Computational Approaches for Antimicrobial Peptide Discovery.Antibiotics (Basel, Switzerland) · 2022Review
- Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics.Pharmaceutics · 2022Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop accurate machine learning models for ACP prediction. In this study, features were extracted from the three-dimensional (3D) structure of peptides to develop the model, compared to most of the previous computational models, which are based on sequence information. In order to develop ACPs with more potency, more selectivity and less toxicity, the model for predicting ACPs, hemolytic peptides and toxic peptides were established by peptides 3D structure separately. Multiple datasets were collected according to whether the peptide sequence was chemically modified. After feature extraction and screening, diverse algorithms were used to build the model. Twelve models with excellent performance (Acc > 90%) in the ACPs mixed datasets were used to form a hybrid model to predict the candidate ACPs, and then the optimal model of hemolytic peptides (Acc = 73.68%) and toxic peptides (Acc = 85.5%) was used for safety prediction. Novel ACPs were found by using those models, and five peptides were randomly selected to determine their anticancer activity and toxic side effects in vitro experiments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.