A Review on Fragment-based De Novo 2D Molecule Generation
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909196115509248 |
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| author | Voloboev, Sergei |
| author_facet | Voloboev, Sergei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05293 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Review on Fragment-based De Novo 2D Molecule Generation Voloboev, Sergei Biomolecules Machine Learning 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. |
| title | A Review on Fragment-based De Novo 2D Molecule Generation |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2405.05293 |