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An efficient computer-aided structural elucidation strategy for mixtures using an iterative dynamic programming algorithm

机译:使用迭代动态规划算法的有效的计算机辅助混合物结构解析策略

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摘要

The identification of chemical structures in natural product mixtures is an important task in drug discovery but is still a challenging problem, as structural elucidation is a time-consuming process and is limited by the available mass spectra of known natural products. Computer-aided structure elucidation (CASE) strategies seek to automatically propose a list of possible chemical structures in mixtures by utilizing chromatographic and spectroscopic methods. However, current CASE tools still cannot automatically solve structures for experienced natural product chemists. Here, we formulated the structural elucidation of natural products in a mixture as a computational problem by extending a list of scaffolds using a weighted side chain list after analyzing a collection of 243,130 natural products and designed an efficient algorithm to precisely identify the chemical structures. The complexity of such a problem is NP-complete. A dynamic programming (DP) algorithm can solve this NP-complete problem in pseudo-polynomial time after converting floating point molecular weights into integers. However, the running time of the DP algorithm degrades exponentially as the precision of the mass spectrometry experiment grows. To ideally solve in polynomial time, we proposed a novel iterative DP algorithm that can quickly recognize the chemical structures of natural products. By utilizing this algorithm to elucidate the structures of four natural products that were experimentally and structurally determined, the algorithm can search the exact solutions, and the time performance was shown to be in polynomial time for average cases. The proposed method improved the speed of the structural elucidation of natural products and helped broaden the spectrum of available compounds that could be applied as new drug candidates. A web service built for structural elucidation studies is freely accessible via the following link ().Electronic supplementary materialThe online version of this article (10.1186/s13321-017-0244-9) contains supplementary material, which is available to authorized users.
机译:天然产物混合物中化学结构的鉴定是药物开发中的重要任务,但仍然是一个具有挑战性的问题,因为结构阐明是一个耗时的过程,并且受到已知天然产物的可用质谱的限制。计算机辅助结构解析(CASE)策略试图通过利用色谱和光谱方法自动提出混合物中可能存在的化学结构的列表。但是,当前的CASE工具仍然无法自动为经验丰富的天然产物化学家解决结构。在这里,我们在分析了243,130种天然产物的集合后,通过使用加权侧链列表扩展了脚手架的列表,从而将混合物中天然产物的结构阐明作为计算问题,并设计了一种有效的算法来精确识别化学结构。这个问题的复杂性是NP完全的。将浮点分子量转换为整数后,动态规划(DP)算法可以在伪多项式时间内解决该NP完全问题。但是,随着质谱实验精度的提高,DP算法的运行时间呈指数下降。为了理想地在多项式时间内求解,我们提出了一种新颖的迭代DP算法,该算法可以快速识别天然产物的化学结构。通过使用该算法阐明通过实验和结构确定的四种天然产物的结构,该算法可以搜索精确解,并且对于平均情况,时间性能显示为多项式时间。所提出的方法提高了天然产物的结构解析速度,并有助于拓宽了可用作新药候选物的可用化合物的范围。可通过以下链接()免费访问为结构解析研究而构建的Web服务。电子补充材料本文的在线版本(10.1186 / s13321-017-0244-9)包含补充材料,授权用户可以使用。

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