[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124901-en":3,"doc-seo-124901-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124901,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning Modeling and Insights into the Structural Foundations of Polymyxin-like Antimicrobials","Antimicrobial resistance is presented as an urgent, escalating threat to human health, especially among Gram-negative pathogens. With polymyxins revived as last-line therapy due to failures of other antibiotics, the document addresses the need for faster, more reliable analog design approaches than mostly empirical strategies. It applies machine learning to QSAR modeling using 408 molecule–microorganism pairs from PubChem, comparing four algorithms and ten descriptor families. The AdaBoost model using CKP descriptors shows strong accuracy with very low false predictions and reveals structural change trends that may guide improved activity while targeting toxicity reduction.","Machine Learning Modeling and Insights into the Structural Foundations of Polymyxin-like  \nAntimicrobials  \nInˆes Machado,† Jo˜ao In´acio,‡,¶ , § Paula Jorge,‡,¶ , § and Filipe Teixeira ∗ ,‡  \n†Institute for Polymers and Composites, University of Minho, 4800-058 Guimar˜aes,  \nPortugal  \n‡Centre of Chemistry, University of Minho, 4710-057 Braga, Portugal ¶Centre of Biological Engineering, University of Minho, 4710-057 Braga, Portugal  \n§LABBELS – Associated Laboratory, Braga/Guimar˜aes, Portugal  \nE-mail: [fteixeira@quimica.uminho.pt](fteixeira@quimica.uminho.pt)  \n1 Abstract  \n2 Antimicrobial resistance (AMR) is a silent pandemic that represents an urgent  \n3 threat to human health. Unfortunately, the antibiotic development pipeline is slow  \n4 even though AMR has been escalating uncontrollably fast, namely amongst Gram- 5 negative pathogens. Although out of use until recently due to their toxic side effects, 6 polymyxins have been revived as a last-line therapeutic option since all other antibiotics  \n7 are currently failing. In an attempt to ameliorate their toxicity and improve antimicro- 8 bial activity, many studies have been generating polymyxin analogues through different  \n9 strategies, mostly empirical. As such, there is still a lack of faster and more reliable  \n10 approaches to make analog design efficient in order to tackle AMR in a timely fash- 11 ion. The solution to accelerate the discovery of new drugs probably lies in the use of  \n12 in silico approaches, such as machine learning, due to their faster pace and time and  \n13 cost efficiency. In this work, machine learning was applied to Quantitative Structure- 14 Activity Relationship (QSAR) modeling with the objective of providing a working  \n15 semi-quantitative model capable of predicting the activity of polymyxin-like molecules  \n16 for a given species. For this, we applied four different learning algorithms and ten dif- 17 ferent families of molecular descriptors to our dataset of 408 molecule/microorganism  \n18 pairs retrieved from PubChem. The AdaBoost model devised using the CKP set of  \n19 descriptors was the best performer, with good accuracies and very low false negative  \n20 and positive predictions. Preliminary exploration of the model’s response to systematic  \n21 changes in the structure of polymyxin B reveals a trend towards increased antimicro- 22 bial activity when exchanging some of its constituent amino acids for more lipophilic  \n23 ones. Experimental studies are already underway based on this model’s application  \n24 and we believe it will become a crucial tool for drug development.  \n25 1 Introduction  \n26 Antimicrobial resistance represents one of the current biggest health-threats worldwide, 27 whose impact has been heightened by the escalation of multidrug resistant (MDR) Gram- 28 negative bacteria. Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneu- 29 moniae head the WHO list of priority pathogens not responding to front-line antibiotics 1 , 30 and their ability to grow as biofilms further heightens their role as troublesome pathogens  \n31 highly associated with lower respiratory infections and with high mortality/morbidity rates 2 .  \n32 Polymyxins (PMs) B and E are the two most studied and utilized variants of the an- 33 timicrobial peptide PM group and are currently used in last resort treatments for Gram- 34 negative bacterial infections 3 . PMs were first put into clinical use in the 1950s, but were  \n35 subsequently replaced by other drugs due to their nephrotoxic and neurotoxic side effects.  \n36 Nevertheless, improved dosing regimens and the rise of Gram-negative MDR strains led to a  \n37 renaissance of their clinical use when everything else was failing 4 . Sadly, PM resistance has  \n38 also emerged, mostly due to outer membrane lower permeability 7 , but also through nonspe- 39 cific mechanisms (e.g. capsules, efflux pumps) 6 . This, along with PM’s poor bioavailability, 40 nephro-/neuro-toxicity, and narro","cbCaitsLbntd6tny","https://ap.wps.com/l/cbCaitsLbntd6tny","pdf",438483,1,27,"English","en",105,"# Abstract\n# Introduction\n## Antimicrobial resistance and priority pathogens\n## Polymyxins: clinical use and resistance\n## Structural domains and mechanisms of action\n## Need for improved analog design\n## Study approach: QSAR modeling","[{\"question\":\"Why are polymyxin-like antimicrobials considered important in the face of AMR?\",\"answer\":\"Polymyxins have been revived as a last-line option because other antibiotics are failing, and they can act against Gram-negative bacteria through multiple structural and functional interactions. However, toxicity and emerging resistance motivate the search for improved analogs.\"},{\"question\":\"What modeling approach is used to predict polymyxin-like activity?\",\"answer\":\"Machine learning is applied to Quantitative Structure-Activity Relationship (QSAR) modeling. The work compares four learning algorithms and ten families of molecular descriptors using a dataset of 408 molecule–microorganism pairs retrieved from PubChem.\"},{\"question\":\"Which model performed best and what did it reveal?\",\"answer\":\"The AdaBoost model using CKP set descriptors performed best, achieving good accuracies with very low false positive and false negative predictions. Preliminary analysis of systematic structural changes in polymyxin B suggests increased antimicrobial activity when some constituent amino acids are replaced with more lipophilic ones.\"}]","Machine Learning Modeling and Insights into the Structural Foundations of Polymyxin-like Antimicrobials | PDF",1785895300,68,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-modeling-and-insights-into-the-structural-foundations-of-polymyxin-like-antimicrobials","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-modeling-and-insights-into-the-structural-foundations-of-polymyxin-like-antimicrobials/124901/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are polymyxin-like antimicrobials considered important in the face of AMR?","Question",{"text":75,"@type":76},"Polymyxins have been revived as a last-line option because other antibiotics are failing, and they can act against Gram-negative bacteria through multiple structural and functional interactions. However, toxicity and emerging resistance motivate the search for improved analogs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What modeling approach is used to predict polymyxin-like activity?",{"text":80,"@type":76},"Machine learning is applied to Quantitative Structure-Activity Relationship (QSAR) modeling. The work compares four learning algorithms and ten families of molecular descriptors using a dataset of 408 molecule–microorganism pairs retrieved from PubChem.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what did it reveal?",{"text":84,"@type":76},"The AdaBoost model using CKP set descriptors performed best, achieving good accuracies with very low false positive and false negative predictions. Preliminary analysis of systematic structural changes in polymyxin B suggests increased antimicrobial activity when some constituent amino acids are replaced with more lipophilic ones.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]