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 deep learning, 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 de novo. The Moroccan locust (Dociostaurus maroccanus), 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.