Generating diversity and securing completeness in algorithmic retrosynthesis
Abstract Chemical synthesis planning has considerably benefited from advances in the field of machine learning. Neural networks can reliably and accurately predict reactions leading to a given, possibly complex, molecule. In this work we focus on algorithms for assembling such predictions to a full...
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| Principais autores: | , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BMC
2025-05-01
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| coleção: | Journal of Cheminformatics |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1186/s13321-025-00981-x |
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