{"id":11150,"date":"2026-07-06T14:28:30","date_gmt":"2026-07-06T12:28:30","guid":{"rendered":"https:\/\/www.iqac.csic.es\/proyecto\/deep-learning-as-a-novel-tool-for-the-identification-of-active-odorant-compounds-for-pest-control-the-moroccan-locust-as-a-case-study\/"},"modified":"2026-07-06T14:28:30","modified_gmt":"2026-07-06T12:28:30","slug":"deep-learning-as-a-novel-tool-for-the-identification-of-active-odorant-compounds-for-pest-control-the-moroccan-locust-as-a-case-study","status":"publish","type":"proyecto","link":"https:\/\/www.iqac.csic.es\/en\/project\/deep-learning-as-a-novel-tool-for-the-identification-of-active-odorant-compounds-for-pest-control-the-moroccan-locust-as-a-case-study\/","title":{"rendered":"Deep learning as a novel tool for the identification of active odorant compounds for pest control: the Moroccan locust as a case study"},"content":{"rendered":"<p>DEEPPEST aims to develop an innovative, fast, and cost-effective approach to identify environmentally friendly compounds for pest control, replacing conventional pesticides with species-specific semiochemicals (natural chemical signals that modify insect behavior). The project integrates <em>deep learning<\/em>, computational chemistry, transcriptomics, chemical analysis, and behavioral assays to predict the structure of receptors and odorant-binding proteins, screen compounds produced by insects, and design new active molecules <em>de novo<\/em>. The Moroccan locust (<em>Dociostaurus maroccanus<\/em>), a major agricultural pest causing recurrent outbreaks in Spain and other Mediterranean regions, is used as a case study. Candidate compounds will be validated through laboratory and semi-field trials. Beyond this case, DEEPPEST seeks to establish a general protocol rapidly applicable to emerging or invasive pests.    <\/p>\n","protected":false},"excerpt":{"rendered":"<p>The DEEPPEST project develops a faster and more economical way to find naturally occurring chemical compounds to control insect pests, without having to resort to conventional pesticides, by combining artificial intelligence and computational chemistry with insect biology. The approach is tested on the Moroccan locust as a proof of concept that could subsequently be applied to other invasive species. <\/p>\n","protected":false},"featured_media":11151,"parent":0,"template":"","meta":{"_acf_changed":false},"tags":[165,196],"enfermedad":[],"framework":[131],"linea-de-investigacion":[164,163],"class_list":["post-11150","proyecto","type-proyecto","status-publish","has-post-thumbnail","hentry","tag-chemical-ecology","tag-theoretical-and-computational-chemistry","framework-plan-estatal-peicti","linea-de-investigacion-prevent","linea-de-investigacion-understand","entry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Deep learning as a novel tool for the identification of active odorant compounds for pest control: the Moroccan locust as a case study | IQAC - Institut de Qu\u00edmica Avan\u00e7ada de Catalunya - CSIC<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.iqac.csic.es\/en\/project\/deep-learning-as-a-novel-tool-for-the-identification-of-active-odorant-compounds-for-pest-control-the-moroccan-locust-as-a-case-study\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep learning as a novel tool for the identification of active odorant compounds for pest control: the Moroccan locust as a case study | IQAC - Institut de Qu\u00edmica Avan\u00e7ada de Catalunya - CSIC\" \/>\n<meta property=\"og:description\" content=\"The DEEPPEST project develops a faster and more economical way to find naturally occurring chemical compounds to control insect pests, without having to resort to conventional pesticides, by combining artificial intelligence and computational chemistry with insect biology. 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