[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127761-en":3,"doc-seo-127761-105":31,"detail-sidebar-cat-0-en-105":96},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127761,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine Learning Approaches Identify Chemical Features for StageSpecific Antimalarial Compounds","Efficacy data from chemical libraries screened across malaria parasite Plasmodium falciparum life-cycle stages, including asexual blood stage (ABS) parasites and transmissible gametocytes, provide insight into the chemical space of compounds with stage-specific activity. The study mines these data to derive chemical features linked to sole ABS activity and to additional lifecycle profiles such as gametocytocidal activity. Machine learning models were trained on molecular fingerprints and predict active and inactive compounds, identifying enriched features for streamlining hit-to-lead optimization and prioritizing phenotypic screening and medicinal chemistry programs.","This article is licensed under CC-BY-NC-ND 4.0   \n[http://pubs.acs.org/journal/acsodf](http://pubs.acs.org/journal/acsodf)  Article   \nMachine Learning Approaches Identify Chemical Features for StageSpecific Antimalarial Compounds  \nAshleigh van Heerden, Gemma Turon, Miquel Duran-Frigola, Nelishia Pillay, and Lyn-Marié Birkholtz *  \n Cite This: ACS Omega 2023, 8, 43813−43826  \nRead Online  \nACCESS  \n Metrics & More  \n Article Recommendations  \n*sı   \nSupporting Information  \nDownloaded via UNIV OF PRETORIA on February 29, 2024 at 06:09:44 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nABSTRACT: Efficacy data from diverse chemical libraries, screened against the various stages of the malaria parasite Plasmodium falciparum, including asexual blood stage (ABS) parasites and transmissible gametocytes, serve as a valuable reservoir of information on the chemical space of compounds that are either active (or not) against the parasite. We postulated that this data can be mined to define chemical features associated with the sole ABS activity and/or those that provide additional lifecycle activity profiles like gametocytocidal activity. Additionally, this information could provide chemical features associated with  \ninactive compounds, which could eliminate any future unnecessary screening of similar chemical analogs. Therefore, we aimed to use machine learning to identify the chemical space associated with stage-specific antimalarial activity. We collected data from various chemical libraries that were screened against the asexual (126 374 compounds) and sexual (gametocyte) stages of the parasite (93 941 compounds), calculated the compounds’ molecular fingerprints, and trained machine learning models to recognize stage-specific active and inactive compounds. We were able to build several models that predict compound activity against ABS and dual activity against ABS and gametocytes, with Support Vector Machines (SVM) showing superior abilities with high recall (90 and 66%) and low false-positive predictions (15 and 1%). This allowed the identification of chemical features enriched in active and inactive populations, an important outcome that could be mined for essential chemical features to streamline hit-to-lead optimization strategies of antimalarial candidates. The predictive capabilities of the models held true in diverse chemical spaces, indicating that the ML models are therefore robust and can serve as a prioritization tool to drive and guide phenotypic screening and medicinal chemistry programs.  \n1. INTRODUCTION  \nFrom 2000 to 2019, malaria-associated deaths showed a steady decline, but these gains were stalled in 2020, with a 12% increase in malaria mortality reported globally.1 Compounding factors include the COVID-19 pandemic, which hindered control efforts and the continued emergence of drug-resistant malaria parasites.2 Therefore, efforts toward discovering and developing potent antimalarials with novel modes of action must be sustained, and such compounds should target multiple life cycle stages of Plasmodium parasites.3,4 Importantly, compounds with the ability to target the transmission of the parasite are sought after as they could be employed to limit the spread of the parasite, and hence disease, and support malariaelimination strategies.5 Aside from the required need for compounds with prophylactic activity (blocking exoerythrocytic development of parasites during liver schizogony), transmission-blocking (TrB) activity can also be ascribed to compounds able to block sexual gametocyte development and subsequent human-to-mosquito transmission of the parasite.  \nThe discovery of compounds with TrB activity through phenotypic whole-cell screening is fraught with challenges associated with the unique biology inherent to the sexual gametocyte stages of the human malaria parasite, Plasmodium  \nfalciparum. In this","cbCaipingZc25QwF","https://ap.wps.com/l/cbCaipingZc25QwF","pdf",9030353,2,1,14,"English","en",105,"# Introduction\n## Stage-specific antimalarial discovery background\n## Challenges of transmission-blocking and gametocyte biology\n# Abstract\n## Data mining and modeling approach","[{\"question\":\"What problem does the study address in antimalarial research?\",\"answer\":\"It targets the need to discover potent antimalarials that act on specific Plasmodium falciparum life-cycle stages, including both asexual blood stages and transmissible gametocytes.\"},{\"question\":\"How were chemical features used to predict stage-specific activity?\",\"answer\":\"The work collected screened efficacy data, calculated molecular fingerprints for each compound, and trained machine learning models to classify active versus inactive compounds for the relevant parasite stages.\"},{\"question\":\"What performance aspects were reported for the best models?\",\"answer\":\"Support Vector Machines (SVM) achieved superior abilities with high recall (90% for ABS and 66% for dual ABS-gametocyte activity) and low false-positive predictions (15% and 1%, respectively).\"},{\"question\":\"How do the models support downstream drug development?\",\"answer\":\"By identifying chemical features enriched in active and inactive populations, the models can streamline hit-to-lead optimization and help prioritize compounds for phenotypic screening and medicinal chemistry programs.\"}]","Machine Learning Approaches Identify Chemical Features for StageSpecific Antimalarial Compounds | 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problem does the study address in antimalarial research?","Question",{"text":76,"@type":77},"It targets the need to discover potent antimalarials that act on specific Plasmodium falciparum life-cycle stages, including both asexual blood stages and transmissible gametocytes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were chemical features used to predict stage-specific activity?",{"text":81,"@type":77},"The work collected screened efficacy data, calculated molecular fingerprints for each compound, and trained machine learning models to classify active versus inactive compounds for the relevant parasite stages.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance aspects were reported for the best models?",{"text":85,"@type":77},"Support Vector Machines (SVM) achieved superior abilities with high recall (90% for ABS and 66% for dual ABS-gametocyte activity) and low false-positive predictions (15% and 1%, respectively).",{"name":87,"@type":74,"acceptedAnswer":88},"How do the models support downstream drug development?",{"text":89,"@type":77},"By identifying chemical features enriched in active and inactive populations, the models can streamline hit-to-lead optimization and help prioritize compounds for phenotypic screening and medicinal chemistry programs.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,125,128,133,136,140],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Story & 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