A Review on Fragment-based De Novo 2D Molecule Generation

• URL: http://arxiv.org/abs/2405.05293v1
• Date: Wed, 8 May 2024 09:38:38 GMT
• Title: A Review on Fragment-based De Novo 2D Molecule Generation
• Authors: Sergei Voloboev,
• Abstract summary: fragment-based deep generative models consistently achieve state-of-the-art results in molecular design benchmarks as of 2023. This review includes comparisons of output quality, generation speed, and the current limitations of specific models. We highlight promising avenues for future research that could bridge fragment-based models to real-world applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the field of computational molecule generation, an essential task in the discovery of new chemical compounds, fragment-based deep generative models are a leading approach, consistently achieving state-of-the-art results in molecular design benchmarks as of 2023. We present a detailed comparative assessment of their architectures, highlighting their unique approaches to molecular fragmentation and generative modeling. This review also includes comparisons of output quality, generation speed, and the current limitations of specific models. We also highlight promising avenues for future research that could bridge fragment-based models to real-world applications.

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