有机质谱人工智能解释—Z值二级族分类及在环境污染物质谱解释中…

本文叙述了环境污染物质谱人工智能解释计算机程序“ＤＣＰ－ＡＩＭＳ－ＥＰ”，程序不仅基于质谱规则，而且基于依Ｚｉ／Ｚｒ值二级族分类构成的新型质谱知识库框架，程序在ＩＢＭ４８６计算机上调试通过，并经２２５个环境污染物质谱和８１个“非环境污染物”质谱验证评价，输出正确率达９９．３％，置信度达９５．３％。     

多功能基化合物质谱人工智能解释—氨基酸质谱人工智能解释研究

本文提出了多功能基化合物、蛋白质降解产物——氨基酸LR-EI谱人工智能(AI)解释程序(AIMS-MFC-AAC)。输出置信度达90%。文中指出了多功能基化合物质谱中同时存在“可加性”关系和“取代基效应”,及其对McLafferty重排的影响。     

有机质谱人工智能解释研究——烷基苯质谱解释程序及其应用

本文提出的烷基苯质谱人工智能解释计算机程序(DCP-MSAB),是基于有机质谱碎裂、重排规律,采用CoboL语言编制成的。经过对50多个化合物质谱的计算机程序解释验证,给出96％的首解正确率。该程序已经用于实际样品质谱的解释。     

基于质谱成像和组学分析的环境毒理研究

生物体多器官的空间异质性导致环境污染物在生物体内的毒性分子机制错综复杂.基于传统化学和生物分析的环境毒理学研究,通常将研究对象看作“均一”整体,无法从空间上准确定位污染物及其代谢.以质谱成像和组学分析为基础的技术,同时对污染物、污染物代谢活化途径及其诱导的生物分子进行定性、定量和空间分析,从而确定污染物迁移、生物学效应...     

新型污染物的环境质谱技术研究进展

随着科学技术的飞速发展,质谱检测及其联用技术方法正以前所未有的速度、广度和深度全面渗透到环境分析化学中,其在环境监测中的使用已经或正在日常化.近年来,一些高分辨质谱及其与色谱等的联用技术在目标污染物和非目标污染物的同时甄别鉴定和分析中发挥了重要作用,其对于阐明污染物在环境的归趋具有重要意义.本文对质谱技术及其与气相色谱和液相色谱的联用技术在污染物尤其是新型污染物分析中的进展进行了总结,并对高分辨质谱技术的环境应用研究给于关注,对环境质谱技术的发展方向进行了展望.     

基于纳米材料的质谱成像研究进展

纳米材料种类的多样性以及性能的可调性为质谱分析方法的开发提供了多种可能。近年来,随着质谱分析技术的不断革新与发展,研究者逐步尝试将纳米材料辅助的质谱分析技术推进到质谱成像研究领域。利用纳米材料作为辅助电离介质和信号分子载体,质谱成像技术在实施未知物鉴别的同时可实现目标分子的精确定位,进而呈现生物分子、药物、环境污染物等在组织与细胞中的空间分布信息,为基于组织样本的病理生理学研究提供更直观的技术手段。本文总结了基于纳米材料的质谱成像技术的主要原理和研究进展,并对其未来发展及应用进行了展望。     

有机质谱人工智能解释研究——烷基苯质谱解释程序的评价

本文提出了有机质谱人工智能计算机解释程序的三参数评价方法。采用输出正确率(CRI)、鉴别率(DIS)和置信度(Conf.)来表征解释程序对未知化合物(程序类化合物和非程序类化合物质)谱进行化学结构解释和化合物类型鉴别功能的优劣。CRI值不仅考虑到输出的化学结构是否正确,还考虑到了正确结构(答案)在输出结果中所处的序号。采用上述三参数可以对各不同实验室的同类程序进行相互评价比较。此外,CRI值也可以单独用于各不同来源的质谱数据库的评价比较。     

气相色谱/质谱-选择离子检测法同时测定大米中的25种持久性有机污染物

利用超声波提取、固相萃取净化对样品进行前处理,然后采用气相色谱/质谱-选择离子检测模式对大米中的25种持久性有机污染物进行了分析。色谱条件:DB-35MS毛细管色谱柱(30 m×0.25 mm i.d.×0.25 μm);载气为氦气,流速1 mL/min;进样口温度300 ℃;不分流进样,进样量1 μL;柱温为程序升温模式。质谱条件:电子轰击电离源,70 eV;采集方式为选择离子方式,扫描质量范围50~450 u。实验采用保留时间以及定性、定量特征离子的丰度比定性,采用峰面积外标法定量,制作了25种持久性有机污染物的标准工作曲线。不同浓度水平的添加回收率试验表明,25种持久性有机污染物的添加回收率为81.99%～100.60%,相对标准偏差为2.37%～18.48%,除异狄氏剂、反式氯丹和顺式氯丹的检测限分别为20,30和20 ng/g外,其他有机污染物的检测限为0.1～5 ng/g。该方法的灵敏度、准确度和精密度均符合农药多残留测定技术的要求。     

同位素质谱和无机质谱

本文是《分析试验室》定期评述中“同位素质谱和无机质谱”的第三次评述，评述的范围是１９９２年１１月至１９９４年１０月我国同位素质谱和无机质谱的进展。内容包括同位素质谱，同位素稀释质谱，二次离子质谱，离子探针，电感耦合等离子体质谱，激光电离质谱，加速器质谱、火花源质谱和它们在同位素地质年代学、同位素地球化学、核科学、农业、医学、环境学、计量学等学科中的应用。引用文献２５９篇．     

同位素质谱和无机质谱

本文是《分析试验室》定期评述中“同位素质谱和无机质谱”的第四篇评述，评述的范围是１９９４年１１月至１９９６年１０月我国气体同位素质谱，热电离同位素质谱，加速器质谱，火花源质谱，电感耦合等离子体质谱，辉光放电质谱，同位素稀释质谱，二次离子质谱，激光共振电离子飞行时间质谱，电子探针，质子探针，激光探针和它们在地学，核科学，环境科学，材料学，计理学，医学和生命科学中的应用，引用文献１４９篇。     

Generation of substructure identification rules using feature-combinations from tandem mass spectra

Software to interpret tandem mass spectra, entitled Method for Analyzing Patterns in Spectra (MAPS), has been developed to provide substructure information for an automated compound identification system, This software consists of several program modules which manipulate databases of tandem mass spectra and substructure information, generate substructure identification rules, and apply these rules to the tandem mass spectra of unknown compounds to identify components of their structure. The MAPS rule generation program has been modified to generate rules based on specific combinations of spectral features that occur concertedly. False positives are drastically reduced by searching for “feature-combinations” that have 100% uniqueness with respect to a reference database of compounds. Recall is increased by the determination of multiple feature-combinations indicative of the presence of a given substructure. Strategies were developed in the algorithm for the discovery of feature-combinations that avoid the computation “explosion” that occurs when working with a large number of spectral features. The rules developed have the form: “IF feature-eombination a (FC a) or FC b,..., or FC x, THEN substructure SSn is present.”     

Applications of artificial intelligence for chemical inference—X : Intsum. A data interpretation and summary program applied to the collected mass spectra of estrogenic steroids

A method for systematic interpretation and summary of evidence found for all possible mass spectral fragmentations of a molecule or set of related molecules is described. The method is embodied in a computer program (INTSUM) which interprets, in terms of fragmentation processes, mass spectral data collected on known compounds. Utilizing high resolution mass spectra from 47 estrogenic steroids, the method is verified and new findings are discussed. Finally, the method is used to explore the fragmentations of equilenins and several acetate and benzoate ester derivatives.     

Similarity of High-Resolution Tandem Mass Spectrometry Spectra of Structurally Related Micropollutants and Transformation Products

High-resolution tandem mass spectrometry (HRMS2) with electrospray ionization is frequently applied to study polar organic molecules such as micropollutants. Fragmentation provides structural information to confirm structures of known compounds or propose structures of unknown compounds. Similarity of HRMS2 spectra between structurally related compounds has been suggested to facilitate identification of unknown compounds. To test this hypothesis, the similarity of reference standard HRMS2 spectra was calculated for 243 pairs of micropollutants and their structurally related transformation products (TPs); for comparison, spectral similarity was also calculated for 219 pairs of unrelated compounds. Spectra were measured on Orbitrap and QTOF mass spectrometers and similarity was calculated with the dot product. The influence of different factors on spectral similarity [e.g., normalized collision energy (NCE), merging fragments from all NCEs, and shifting fragments by the mass difference of the pair] was considered. Spectral similarity increased at higher NCEs and highest similarity scores for related pairs were obtained with merged spectra including measured fragments and shifted fragments. Removal of the monoisotopic peak was critical to reduce false positives. Using a spectral similarity score threshold of 0.52, 40% of related pairs and 0% of unrelated pairs were above this value. Structural similarity was estimated with the Tanimoto coefficient and pairs with higher structural similarity generally had higher spectral similarity. Pairs where one or both compounds contained heteroatoms such as sulfur often resulted in dissimilar spectra. This work demonstrates that HRMS2 spectral similarity may indicate structural similarity and that spectral similarity can be used in the future to screen complex samples for related compounds such as micropollutants and TPs, assisting in the prioritization of non-target compounds.

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Computer aided prediction of biological activity spectra: evaluating versus known and predicting of new activities for thiazole derivatives

Computer aided prediction of biological activity spectra by the computer program PASS was applied to a set of 89 new thiazole derivatives. Experimentally tested activities (NSAID, local anaesthetic and antioxidant) coincide with the experiment in 70.8% cases, that exceeds significantly the random guess-work (approximately 0.1%). Therefore, computer aided prediction using the Prediction of Activity Spectra for Substances (PASS) system (http://www.ibmh.msk.su/PASS) provides a reliable basis for planning of synthesis and experimental study for new compounds. New psychotropic activities are predicted for some compounds from the series under study. In particular, 7, 44 and 55 compounds likely have anxiolytic, anticonvulsant and cognition enhancer effects, respectively. Most of these compounds have the estimated values of probability to be active (Pa) less than 60%. Therefore, if their activity will be confirmed by the experiment, they might occur to be New Chemical Entities.     

Sulfur Analogs of Pyrimidine Bases: Synthesis of 2‐Alkylthio‐ and 4‐Alkylthio‐5‐bromouracils and In Silico Evaluation of Their Biological Activity

The general reactivity of alkylthiouracils under bromination conditions has been examined. Twenty 2‐alkylthio‐ and 4‐alkylthio‐5‐bromouracils of potential biological activity have been prepared. The structures of these compounds were confirmed by elemental and spectral (UV/vis, IR, and ¹H‐NMR) analyses. The pharmacotherapeutical potential of the synthesized compounds has been predicted using Prediction of Activity Spectra for Substances (i.e., PASS) program.     

Computer aided prediction of biological activity spectra: Evaluating versus known and predicting of new activities for thiazole derivatives

Computer aided prediction of biological activity spectra by the computer program PASS was applied to a set of 89 new thiazole derivatives. Experimentally tested activities (NSAID, local anaesthetic and antioxidant) coincide with the experiment in 70.8% cases, that exceeds significantly the random guess-work (～0.1%). Therefore, computer aided prediction using the Prediction of Activity Spectra for Substances (PASS) system (http://www.ibmh.msk.su/PASS) provides a reliable basis for planning of synthesis and experimental study for new compounds. New psychotropic activities are predicted for some compounds from the series under study. In particular, 7, 44 and 55 compounds likely have anxiolytic, anticonvulsant and cognition enhancer effects, respectively. Most of these compounds have the estimated values of probability to be active ( P a ) less than 60%. Therefore, if their activity will be confirmed by the experiment, they might occur to be New Chemical Entities.     

Evaluation of automatically generated substructure identification rules from tandem mass spectra

Substructure identification rules for phenothiazine and barbiturate substructures were generated by using a new version of the Method for Analyzing Patterns in Spectra (MAPS) software. This software uses tandem mass spectra and known substructure content of reference compounds to provide “feature-combination“ rules. A feature-combination is a series of tandem mass spectral features which are completely unique to compounds containing a specified substructure. The current reference databases contain over 11,000 daughter spectra of 100 compounds acquired at two different collision gas pressures (i.e., single- and multiple-collision conditions). The results of rule evaluation procedures are presented and include a comparison of the spectral features developed in rule generation to those identified in documented fragmentation pathways of the indicated substructure. Two potential sources of error due to spectral feature and substructure “cross-correlation“ were identified. If errors occur, they can be detected by calculating cross-correlation coefficients and edited from the rules. A beneficial cross-correlation involving feature-combinations was also discovered. The rules obtained by using single- and multiple-collision data were further evaluated by applying them to tandem mass spectra of 20 test compounds (compounds not in the reference database). The results of these evaluations give a good indication of the utility of the rules for use in an automated structure elucidation system for tandem mass spectrometry data.     

Prediction of biological activity profiles of cyanobacterial secondary metabolites

Over the past decade cyanobacteria have become an interesting source of new classes of pharmacologically active natural products. Some cyanobacterial secondary metabolites (CSMs) are also well known for their toxic effects on living species. The PASS (Prediction of Activity Spectra for Substances) computer program, which is able to simultaneously predict more than one thousand biological and toxicological activities from only the structural formulas of the chemicals, was used to predict the biological activity profile of 681 CSMs. Multivariate methods were employed to structure and analyse this wealth of biological and chemical information. PASS predictions were successfully compared to the available information on the pharmacological and toxicological activity of these compounds.     

A general approach to calculating isotopic distributions for mass spectrometry

Fundamental principles for obtaining mass spectral isotopic distributions are applied to a general computer program that can be used to calculate and present in tabular and graphic form the isotopic contributions for any molecular formula. A unique feature is the retention of the isotopic distribution, exact mass, and absolute abundance for all individual peaks at each mass. Special considerations have been made for the large number of isotopic combinations that occur for many higher mass compounds. The computer program accepts the input of a molecular formula followed by interactive input of a number of parameters that affect the final presentation of the theoretical distribution profile.