ArticleChemMedChem2022
Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides.
Article in ChemMedChem, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 31 citations in OpenAlex.
- PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction.Journal of cheminformatics · 2026Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- ELECTRUM: an electron configuration-based universal metal fingerprint for transition metal compounds.Digital discovery · 2025Article
- Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis.BMC bioinformatics · 2024Article
- An active machine learning discovery platform for membrane-disrupting and pore-forming peptides.Physical chemistry chemical physics : PCCP · 2024Article
- Can large language models predict antimicrobial peptide activity and toxicity?RSC medicinal chemistry · 2024Article
- Structure-aware machine learning strategies for antimicrobial peptide discovery.Scientific reports · 2024Article
- Machine learning for antimicrobial peptide identification and design.Nature reviews bioengineering · 2024Article
- Article
- Extracellular vesicles for developing targeted hearing loss therapy.Journal of controlled release : official journal of the Controlled Release Society · 2024Review
- Antimicrobial Peptide-Peptoid Hybrids with and without Membrane Disruption.ACS infectious diseases · 2023Article
- To Fold or Not to Fold: Diastereomeric Optimization of an α-Helical Antimicrobial Peptide.Journal of medicinal chemistry · 2023Article
- Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides.ChemMedChem · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most antimicrobial peptides (AMPs) and anticancer peptides (ACPs) fold into membrane disruptive cationic amphiphilic α-helices, many of which are however also unpredictably hemolytic and toxic. Here we exploited the ability of recurrent neural networks (RNN) to distinguish active from inactive and non-hemolytic from hemolytic AMPs and ACPs to discover new non-hemolytic ACPs. Our discovery pipeline involved: 1) sequence generation using either a generative RNN or a genetic algorithm, 2) RNN classification for activity and hemolysis, 3) selection for sequence novelty, helicity and amphiphilicity, and 4) synthesis and testing. Experimental evaluation of thirty-three peptides resulted in eleven active ACPs, four of which were non-hemolytic, with properties resembling those of the natural ACP lasioglossin III. These experiments show the first example of direct machine learning guided discovery of non-hemolytic ACPs.
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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.