ArticleJournal of molecular modeling2019
De novo design of anticancer peptides by ensemble artificial neural networks.
Article in Journal of molecular modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 65 citations in OpenAlex.
- Discovering Plastic-Binding Peptides with Favorable Affinity, Water Solubility, and Binding Specificity Through Deep Learning and Biophysical Modeling.bioRxiv : the preprint server for biology · 2026Article
- AI-driven rational design of promiscuous and selective plastic-binding peptides.Chemical science · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- peptidy: a light-weight Python library for peptide representation in machine learning.Bioinformatics advances · 2025Article
- Integrating biophysical modeling, quantum computing, and AI to discover plastic-binding peptides that combat microplastic pollution.PNAS nexus · 2025Article
- Designing microplastic-binding peptides with a variational quantum circuit-based hybrid quantum-classical approach.Science advances · 2024Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Deriving general structure-activity/selectivity relationship patterns for different subfamilies of cyclin-dependent kinase inhibitors using machine learning methods.Scientific reports · 2024Article
- AACFlow: an end-to-end model based on attention augmented convolutional neural network and flow-attention mechanism for identification of anticancer peptides.Bioinformatics (Oxford, England) · 2024Article
- Recent advances and applications of peptide-agent conjugates for targeting tumor cells.Journal of cancer research and clinical oncology · 2023Review
- Article
- Nanoparticle-mediated cancer cell therapy: basic science to clinical applications.Cancer metastasis reviews · 2023Review
- ALA-A2 Is a Novel Anticancer Peptide Inspired by Alpha-Lactalbumin: A Discovery from a Computational Peptide Library, In Silico Anticancer Peptide Screening and In Vitro Experimental Validation.Global challenges (Hoboken, NJ) · 2023Article
- Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data.Neural computing & applications · 2023Article
- Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics.Pharmaceutics · 2022Review
- A novel kernel based approach to arbitrary length symbolic data with application to type 2 diabetes risk.Scientific reports · 2022Article
- Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks.Interdisciplinary sciences, computational life sciences · 2022Article
- Design of Protein Segments and Peptides for Binding to Protein Targets.Biodesign research · 2022Review
- GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics.Journal of cheminformatics · 2021Article
- Accelerating antibiotic discovery through artificial intelligence.Communications biology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. In this work, we present an ensemble machine learning model to design potent ACPs. Four counter-propagation artificial neural-networks were trained to identify peptides that kill breast and/or lung cancer cells. For prospective application of the ensemble model, we selected 14 peptides from a total of 1000 de novo designs, for synthesis and testing in vitro on breast cancer (MCF7) and lung cancer (A549) cell lines. Six de novo designs showed anticancer activity in vitro, five of which against both MCF7 and A549 cell lines. The novel active peptides populate uncharted regions of ACP sequence space.
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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.