{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zakharova E"],"funding":["Swiss National Science Foundation","European Research Council"],"pagination":["e202200291"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9541320"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(17)"],"pubmed_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."],"journal":["ChemMedChem"],"pubmed_title":["Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides."],"pmcid":["PMC9541320"],"funding_grant_id":["200020","885076","200020_178998","178998","407240_167048"],"pubmed_authors":["Reymond JL","Capecchi A","Zakharova E","Orsi M"],"additional_accession":[]},"is_claimable":false,"name":"Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides.","description":"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.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Sep","modification":"2025-04-19T04:49:44.849Z","creation":"2025-04-19T04:49:44.849Z"},"accession":"S-EPMC9541320","cross_references":{"pubmed":["35880810"],"doi":["10.1002/cmdc.202200291"]}}