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Improving the performance of models for one-step retrosynthesis through re-ranking
ABSTRACT: Retrosynthesis is at the core of organic chemistry. Recently, the rapid growth of artificial intelligence (AI) has spurred a variety of novel machine learning approaches for data-driven synthesis planning. These methods learn complex patterns from reaction databases in order to predict, fo...
Autores principales: | Lin, Min Htoo, Tu, Zhengkai, Coley, Connor W. |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8922884/ https://www.ncbi.nlm.nih.gov/pubmed/35292121 http://dx.doi.org/10.1186/s13321-022-00594-8 |
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