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The approach focuses especially on short AMPs (\u003C35 amino acids) to help address global multi-drug resistance. CalcAMP is trained on a new dataset built from publicly available AMP data and corresponding experimental activity results, enabling prediction against both Gram-positive and Gram-negative bacteria. It evaluates features including physicochemical properties and sequence composition to improve accuracy, supporting rapid identification of promising short peptides.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/calcamp-a-new-machine-learning-model-for-the-accurate-prediction-of-antimicrobial-activity-of-peptides-research-article/290938/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/calcamp-a-new-machine-learning-model-for-the-accurate-prediction-of-antimicrobial-activity-of-peptides-research-article/290938.png","ImageObject",300,407,{"name":92,"@type":93},"Eden","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does CalcAMP aim to address?","Question",{"text":112,"@type":113},"CalcAMP targets the need for faster identification of antimicrobial peptides as bacterial resistance to conventional antibiotics continues to rise, contributing to a post-antibiotic era.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does CalcAMP predict antimicrobial activity?",{"text":117,"@type":113},"It uses a machine learning model trained on a newly constructed dataset from public AMP information and experimental antimicrobial activity measurements.",{"name":119,"@type":110,"acceptedAnswer":120},"What types of AMPs and bacteria can CalcAMP work with?",{"text":121,"@type":113},"CalcAMP is designed for short AMPs (\u003C35 amino acids) and can predict activity against both Gram-positive and Gram-negative bacteria.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290938,1789642997,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},1374391974468,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","CalcAMP: a new machine learning model for the accurate prediction of antimicrobial activity of peptides  \nBournez, C.; Riool, M.; Boer, [L. de](L. de); Cordfunke, R.A.; Best, [L. de](L. de); Leeuwen, R. van;   ; Westen, G.J.P. van  \nCitation  \nBournez, C., Riool, M., Boer, [L. de](L. de), Cordfunke, R. A., Best, [L. de](L. de), Leeuwen, R. van,   Westen, G. J. P. van. (2023) . CalcAMP: a new machine learning model for the accurate prediction of antimicrobial activity of peptides. Antibiotics, 12(4) .  \ndoi:10.3390/antibiotics12040725  \nVersion: Publisher's Version  \nLicense:  Creative Commons CC BY 4.0 license  \nDownloaded from:  [https://hdl.handle.net/1887/3618249](https://hdl.handle.net/1887/3618249)  \nNote: To cite this publication please use the final published version (if applicable) .  \n antibiotics   \nArticle  \nCalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides  \nColin Bournez 1, Martijn Riool 2,†, Leonie de Boer 2, Robert A. Cordfunke 3, Leonie de Best 4,  \nRemko van Leeuwen 4, Jan Wouter Drijfhout 3, Sebastian A. J. Zaat 2 and Gerard J. P. van Westen 1, *  \nCitation: Bournez, C.; Riool, M.; de Boer, L.; Cordfunke, R.A.; de Best, L.; van Leeuwen, R.; Drijfhout, J.W.; Zaat, S.A.J.; van Westen, G.J.P. CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides. Antibiotics 2023, 12, 725. [https://](https://)[ ](https://)[doi.org/10.3390/antibiotics12040725](doi.org/10.3390/antibiotics12040725)  \nAcademic Editor: Fernando Albericio  \nReceived: 26 February 2023  \nRevised: 24 March 2023  \nAccepted: 31 March 2023  \nPublished: 7 April 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Computational Drug Discovery, Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands  \n2 Department of Medical Microbiology and Infection Prevention, Amsterdam Institute for Infection and Immunity, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands  \n3 Department Immunology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands  \n4 Madam Therapeutics B.V., Pivot Park Life Sciences Community, Kloosterstraat 9, 5349 AB Oss, The Netherlands  \n* Correspondence: [gerard@lacdr.leidenuniv.nl](gerard@lacdr.leidenuniv.nl)  \n† Current address: Laboratory for Experimental Trauma Surgery, Department of Trauma Surgery, University Hospital Regensburg, Am Biopark 9, 93053 Regensburg, Germany.  \nAbstract: To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones (\u003C35 amino acids), can become an effective solution to face the multi-drug resistance issue arising worldwide. Whereas finding potent AMPs through classical wet-lab techniques is still a long and expensive process, a machine learning model can be useful to help researchers to rapidly identify whether peptides present potential or not. Our prediction model is based on a new data set constructed from the available public data on AMPs and experimental antimicrobial activities. CalcAMP can predict activity against both Gram-positive and Gram-negative bacteria. Different features either concerning general physicochemical properties or sequence composition have been assessed to retrieve higher prediction accuracy. CalcAM","cbCair2h65Bt7z32","https://ap.wps.com/l/cbCair2h65Bt7z32","pdf",5339983,22,"English","# Introduction\n## Antimicrobial resistance and need for new drugs\n# Abstract\n## Development of CalcAMP\n## Prediction scope and data basis","[{\"question\":\"What problem does CalcAMP aim to address?\",\"answer\":\"CalcAMP targets the need for faster identification of antimicrobial peptides as bacterial resistance to conventional antibiotics continues to rise, contributing to a post-antibiotic era.\"},{\"question\":\"How does CalcAMP predict antimicrobial activity?\",\"answer\":\"It uses a machine learning model trained on a newly constructed dataset from public AMP information and experimental antimicrobial activity measurements.\"},{\"question\":\"What types of AMPs and bacteria can CalcAMP work with?\",\"answer\":\"CalcAMP is designed for short AMPs (\\u003c35 amino acids) and can predict activity against both Gram-positive and Gram-negative bacteria.\"}]","CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides - research article | PDF",55]