{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hybrid-generative-ai-for-de-novo-design-of-co","title":"Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability","arxiv_id":"2410.17005","date":"2024-10-22","proceeding":null,"authors":["Nina Gubina","Andrei Dmitrenko","Gleb Solovev","Lyubov Yamshchikova","Oleg Petrov","Ivan Lebedev","Nikita Serov","Grigorii Kirgizov","Nikolay Nikitin","Vladimir Vinogradov"],"abstract":"Co-crystallization is an accessible way to control physicochemical characteristics of organic crystals, which finds many biomedical applications. In this work, we present Generative Method for Co-crystal Design (GEMCODE), a novel pipeline for automated co-crystal screening based on the hybridization of deep generative models and evolutionary optimization for broader exploration of the target chemical space. GEMCODE enables fast de novo co-crystal design with target tabletability profiles, which is crucial for the development of pharmaceuticals. With a series of experimental studies highlighting validation and discovery cases, we show that GEMCODE is effective even under realistic computational constraints. Furthermore, we explore the potential of language models in generating co-crystals. 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