图序增强变压器用于无模板预测自然产品生物合成
Shan Cong1,2, Meng Zhang2, Yu Song3
1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, P.R. China.
Patterns (New York, N.Y.)
|August 22, 2025
概括
我们开发了GSETransformer, 一种新型的人工智能模型, 这一突破通过改善天然产品药物候选者的预测来加强药物发现.
科学领域:
- 计算化学
- 药物发现
- 生物信息学
背景情况:
- 自然产品是FDA批准药物的重要来源.
- 自然产品的生物合成途径复杂且不太清楚.
- 传统的回归合成方法难以预测这些复杂的途径.
研究的目的:
- 解决自然产品生物合成途径的局限性.
- 开发一种先进的计算方法来预测复杂的生物合成反应.
- 提高基于天然产品的新疗法发现的效率.
主要方法:
- 提出了图序增强型变压器 (GSETransformer) 模型.
- 综合图形结构信息和顺序依赖关系用于路径分析.
- 对复合任务的基准数据集进行评估.
主要成果:
- 在单步和多步逆合成中实现了最先进的性能.
- 在处理复杂的生物合成数据方面表现出卓越的准确性.
- 在计算生物合成中展示了有效性.
结论:
- 显著地推动了计算生物合成的领域.
- 这种模式有可能加速基于天然产品的药物的设计.
- 在药物发现中应用机器学习的新方向.
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