在自然产品的无模板新生生物合成路径设计中的深度学习
Xueying Xie1,2, Lin Gui3, Baixue Qiao1,2
1Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), No. 26 Hexing Road, Xiangfang District, Harbin 150001, China.
Briefings in bioinformatics
|October 7, 2024
概括
深度学习推进了对自然产品的无模板新生生物合成路径设计,克服了生产限制. 这种方法提高了对感染,癌症和神经退行性疾病的药物发现.
科学领域:
- 生物技术是生物技术.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现 药物发现
背景情况:
- 自然产品 (NPs) 对于开发抗感染,癌症和神经退行性疾病的药物至关重要.
- 由于NP的有限可用性,阻碍了它们的治疗应用.
- 无模板的de novo生物合成路径设计为NP生产提供了一个有前途的解决方案.
研究的目的:
- 审查自然产品生物合成路径设计的最先进的深度学习算法.
- 讨论生物和化学数据库的整合,用于模型培训和转移学习.
- 评估深度学习模型用于单步和多步路径预测,酶功能预测和酶工程.
主要方法:
- 审查深度学习算法,包括序列对序列和图对图模型.
- 数据库的分析:KEGG,Reactome,UniProt,Reaxys,SciFinder和PubChem. 这些数据库的分析包括:
- 探索搜索算法和深度学习,用于酶功能预测和工程.
- 在路径设计和酶发现中检查大型语言模型.
主要成果:
- 深度学习模型显示了准确的单步和多步NP生物合成路径预测的潜力.
- 深度学习有助于预测酶功能,并通过酶工程提高催化效率.
- 大型语言模型为路径设计和酶发现提供了新的途径.
结论:
- 使用深度学习的无模板de novo路径设计是克服自然产品稀缺性的强大策略.
- 深度学习的进步,包括大型语言模型,即将彻底改变自然产品生物合成和药物开发.
- 对深度学习应用的进一步研究对增强自然产品生产和治疗应用具有重大前景.
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