- author = "Nina Gubina and Andrei Dmitrenko and Gleb Solovev and Lyubov Yamshchikova and Oleg Petrov and Ivan Lebedev and Nikita Serov and Grigorii Kirgizov and Nikolay Nikitin and Vladimir Vinogradov",
- title = "Hybrid Generative {AI} for De Novo Design of Co-Crystals with Enhanced Tabletability",
- booktitle = "NeurIPS 2024",
- year = "2024",
- editor = "A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang",
- volume = "37",
- pages = "84606--84644",
- address = "Vancouver",
- publisher = "Curran Associates, Inc.",
- note = "Winner Bronze HUMIES",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
https://proceedings.neurips.cc/paper_files/paper/2024/file/99d27a4939a1dfcdde8394b9137bd885-Paper-Conference.pdf",
-
URL = "
https://neurips.cc/virtual/2024/poster/95931",
-
URL = "https://arxiv.org/abs/2410.17005",
-
URL = "https://www.human-competitive.org/sites/default/files/gubina_humies25.txt",
-
URL = "https://www.human-competitive.org/sites/default/files/hybrid-generative-ai-for-de-novo-design-of-co-crystals-with-enhanced-tabletability-neurips-2024.pdf",
-
slides_url = "https://www.human-competitive.org/sites/default/files/gubina_slides.pptx",
-
video_url = "https://mediaspace.msu.edu/media/Presentation+7+Tabletability/1_ji0tsi28",
-
code_url = "https://github.com/ai-chem/GEMCODE",
- size = "39 pages",
- abstract = "Co-crystallization is an accessible way to control physicochemical characteristics of organic crystals, which finds many biomedical applications. we present Generative Method for Co-crystal Design (GEMCODE) 1, 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. Finally, we present numerous previously unknown co-crystals predicted by GEM-CODE and discuss its potential in accelerating drug development",
-
notes = "Also known as
\cite{NEURIPS2024_99d27a49}
\cite{gubina2024hybridgenerativeainovo} arXiv 2410.17005
https://neurips.cc/virtual/2024/index.html
https://mediaspace.msu.edu/media/Presentation+7+Tabletability/1_ji0tsi28 contains the spoken text.
ITMO University, St. Petersburg, Russia",
