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Summary of Hybrid Generative Ai For De Novo Design Of Co-crystals with Enhanced Tabletability, by Nina Gubina et al.


Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability

by Nina Gubina, Andrei Dmitrenko, Gleb Solovev, Lyubov Yamshchikova, Oleg Petrov, Ivan Lebedev, Nikita Serov, Grigorii Kirgizov, Nikolay Nikitin, Vladimir Vinogradov

First submitted to arxiv on: 22 Oct 2024

Categories

  • Main: Artificial Intelligence (cs.AI)
  • Secondary: None

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GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
Medium Difficulty summary: This paper introduces Generative Method for Co-crystal Design (GEMCODE), a novel pipeline that combines deep generative models and evolutionary optimization to rapidly design co-crystals with target physicochemical properties. GEMCODE is particularly useful for developing pharmaceuticals, as it enables the creation of crystals with specific tabletability profiles. The authors demonstrate the effectiveness of GEMCODE through experimental studies, showcasing its potential in accelerating drug development. Furthermore, they explore the application of language models in generating co-crystals. The paper presents numerous previously unknown co-crystals predicted by GEMCODE, highlighting its capabilities in this area.
Low GrooveSquid.com (original content) Low Difficulty Summary
Low Difficulty summary: This research paper is about a new way to design crystals that are useful for making medicine. It uses a combination of artificial intelligence and mathematical optimization to quickly create crystals with specific properties. The goal is to make it easier and faster to develop new medicines. The scientists tested their method using real-world examples and found that it worked well. They also showed how language models can be used to generate new crystal structures. Overall, this research has the potential to speed up the process of creating new medicines.

Keywords

» Artificial intelligence  » Optimization