biostudies-literatureZakharova ESwiss National Science FoundationEuropean Research Councile202200291https://www.ebi.ac.uk/biostudies/studies/S-EPMC9541320biostudies-literatureUnknown17(17)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.ChemMedChemMachine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides.PMC9541320200020885076200020_178998178998407240_167048Reymond JLCapecchi AZakharova EOrsi MfalseMachine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides.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.2022-01-01T00:00:00Z2022 Sep2025-04-19T04:49:44.849Z2025-04-19T04:49:44.849ZS-EPMC95413203588081010.1002/cmdc.202200291