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 orde...
| Main Authors: | , , |
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| 格式: | 文件 |
| 语言: | English |
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Springer International Publishing
2022
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| 在线阅读: | https://hdl.handle.net/1721.1/141316 |