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Privileged substructures for anti-sickling activity via cheminformatic analysis

机译:通过化学信息分析的抗病活动特权子结构

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Sickle cell disease (SCD), an autosomal recessive genetic disorder, has been recognized by the World Health Organization (WHO) as a major public health problem as it affects 300 000 individuals worldwide. Complications arising from SCD include anemia, microvascular occlusion, severe pain, stokes, renal dysfunction and infections. A lucrative therapeutic strategy is to employ anti-sickling agents that can disrupt the formation of the HbS polymer. This study therefore employed cheminformatic approaches, encompassing classification structure-activity relationship (CSAR) modeling, to deduce the privileged substructures giving rise to the anti-sickling activity of an investigated set of 115 compounds, followed by substructure analysis. Briefly, the compiled compounds were described by fingerprint descriptors and used in the construction of CSAR models via several machine learning algorithms. The modelability of the data set, as exemplified by the MODI index, was determined to be in the range of 0.70-0.84. The predictive performance was deduced by the accuracy, sensitivity, specificity and Matthews correlation coefficient, which was found to be statistically robust, whereby the former three parameters afforded values in excess of 0.7 while the latter statistical parameter provided a value greater than 0.5. An analysis of the top 20 important substructure descriptors for anti-sickling activity revealed that 10 important features were significant in the differentiation of actives from inactives, as illustrated by aromaticity/conjugation (e.g. SubFPC287, SubFPC171 and SubFPC5), carbonyl groups (e.g. SubFPC137, SubFPC139, SubFPC49 and SubFPC135) and miscellaneous groups (e.g. SubFPC303, SubFPC302 and SubFPC275). Furthermore, an analysis of the structure-activity relationship revealed that the length of alkyl chains, choice of functional moiety and position of substitution on the benzene ring may affect the anti-sickling activity of these compounds. Thus, this knowledge is anticipated to be useful for guiding the design of robust compounds against the gelling activity of HbS, as preliminarily demonstrated in the data-driven compound design presented herein.
机译:镰状细胞病(SCD),为常染色体隐性遗传性疾病,已被世界卫生组织(WHO)列为一个重要的公共卫生问题,因为它影响到全世界300 000人的认可。 SCD产生的并发症包括贫血,微血管闭塞,严重的疼痛,斯托克斯,肾功能不全和感染。利润丰厚的治疗策略是使用可能破坏HBS聚合物的形成的抗病药。因此,该研究采用化学综合管理方法,包括分类结构 - 活性关系(CSAR)建模,推导出一种特征的亚结构,从而引起所研究的115种化合物的抗病活性,然后进行次结构分析。简而言之,通过指纹描述符描述编译的化合物,并通过多种机器学习算法来构造CSAR模型。如Modi索引所示的数据集的可用性被确定为0.70-0.84的范围。通过精度,敏感度,特异性和马修的相关系数推导出预测性能,该相关系数被发现是统计稳健的,由此前三个参数提供超过0.7的值,而后者统计参数提供大于0.5的值。对抗病活性的前20个重要的次结构描述符的分析表明,由于芳香性/缀合(例如子谱287,SubFPC171和SubFPC5),羰基(例如Subfpc137, Subfpc139,subfpc49和subfpc135)和杂项组(例如子级别组(例如子杂交303,subfpc302和subfpc275)。此外,结构 - 活性关系的分析表明,烷基链的长度,官能部分和在苯环上取代的位置的选择可能会影响这些化合物的抗镰状化活性。因此,预计该知识将用于引导抗HBS的胶凝活性的鲁棒化合物的设计是有用的,如本文所示的数据驱动的化合物设计中初步证明。

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  • 来源
    《RSC Advances 》 |2018年第11期| 共16页
  • 作者单位

    Mahidol Univ Fac Med Technol Ctr Data Min &

    Biomed Informat Bangkok 10700 Thailand;

    Mahidol Univ Fac Med Technol Ctr Data Min &

    Biomed Informat Bangkok 10700 Thailand;

    Mahidol Univ Fac Med Technol Ctr Data Min &

    Biomed Informat Bangkok 10700 Thailand;

    Mahidol Univ Fac Med Technol Ctr Data Min &

    Biomed Informat Bangkok 10700 Thailand;

    Mahidol Univ Fac Med Technol Ctr Data Min &

    Biomed Informat Bangkok 10700 Thailand;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 化学 ;
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