化学造型生物是否梦见有机物质? 评估一个变压器模型用于多步复合复合
Annie M Westerlund1, Siva Manohar Koki1,2, Supriya Kancharla1,2
1Department of Molecular AI, Discovery Sciences, R&D, AstraZeneca, 43183 Mölndal, Sweden.
Journal of chemical information and modeling
|April 11, 2024
概括
变压器模型Chemformer通过在单步和多步规划中优于基于模板的方法,更有效地识别可行的合成路径,显著改善了药物发现的逆合成预测.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 化学合成规划 化学合成规划
背景情况:
- 复合成规划是制药开发中的一个关键瓶.
- 无模板变压器模型为回复合成预测提供了一种新的方法.
- 评估变压器模型对药物发现的准确性和效率至关重要.
研究的目的:
- 训练和评估一个变压器模型,Chemformer,用于回归合成预测.
- 将Chemformer的性能与基于模板的单步和多步合成模型进行比较.
- 评估Chemformer识别新型合成断电和商业上可行的原材料的能力.
主要方法:
- 在大约1800万个反应的大数据集上训练变压器模型 (Chemformer).
- 评估单步和多步逆合成预测的准确性.
- 将往返准确度和目标化合物与商用原材料的百分比与基于模板的模型进行比较.
- 分析Chemformer建议的新型断开连接.
主要成果:
- 对于常见的药物发现反应类,Chemformer表现出高的单步预测准确度 (前10个往返准确度>0.97).
- 化学变压器实现了比基于模板的模型更高的往返准确性.
- 对于95%的目标化合物,Chemformer成功地确定了合成路径到商业原材料,比基于模板的方法提高了20%以上.
- 在基于模板的模型中不存在的Chemformer提出的新型断开.
结论:
- 像Chemformer这样的无模板变压器模型对于药物发现中的逆合成预测非常有效.
- 在识别高效的合成路径方面,Chemformer比传统的基于模板的方法具有显著的优势.
- 未来的合成规划工具可以整合基于模板和无模板的模型,以优化回复合成建议.
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