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一种基于卷积神经网络的方法,用于快速注释分子多样化的自然产品

Raphael Reher¹, Hyun Woo Kim¹, Chen Zhang^1,2

¹Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States.

Journal of the American Chemical Society

|February 12, 2020

概括

一个新的机器学习工具,小分子精确识别技术 (SMART 2.0),加速了从海洋蓝藻中发现天然产品. 这种方法迅速发现了一种新型的宏化物Simplocolide A,有助于药物发现工作.

科学领域:

自然产品化学
化学中的机器学习
海洋生物技术

背景情况:

海洋蓝藻是细胞毒性天然产物的丰富来源.
发现和描述新的天然产品带来了重大挑战.
先进的分析技术对于天然产品药物发现至关重要.

研究的目的:

展示SMART 2.0工具用于分析复杂的天然产品混合物.
加速从海洋蓝藻细菌中发现和描述新的天然产品.
展示机器学习与药物发现传统分析方法的整合.

主要方法:

小分子精确识别技术 (SMART 2.0) 的应用,这是一个基于NMR的新型机器学习工具.
使用癌细胞细胞毒性,NMR和基于MS2的分子网络的优先级和指导.
使用1D/2D NMR和LC-MS2分析进行隔离和表征.
用"原子排序"方法对SMART 2.0进行循环的比较分析.

主要成果:

成功分离并快速识别出一种新型的模拟分子宏,即symplocolide A.
已知化合物的注释,包括swinholide A,samholides A-I和新的衍生物.
确认symplocolide A的结构是swinholide A和luminaolide B之间的混合物.
证明SMART 2.0在表征新型循环中的有效性.

结论:

结合传统和深度学习的方法, 彻底改变了天然产品药物发现.
显著加快了复杂自然产品的识别和表征.
这项研究强调了先进的分析工具的潜力,以克服长期存在的挑战.

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