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From chemical documentation to chemoinformatics: 50 years of chemical information science

JOURNAL OF INFORMATION SCIENCE 2008年第4期34卷 477-499页

作者： Willett, Peter Univ Sheffield Dept Informat Studies Sheffield S1 4DP S Yorkshire England

This paper summarizes the historical development of the discipline that is now called ` chemoinformatics'. It shows how this has evolved, principally as a result of technological developments in chemistry and biology during the past decade, from long- established techniques for the modelling and searching of chemical molecules. A total of 30 papers, the earliest dating back to 1957, are briefly summarized to highlight some of the key publications and to show the development of the discipline.

关键词： chemical documentation chemical structures chemoinformatics drug discovery history informatics molecules pharmaceutical research

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Converging Interests: chemoinformatics, History, and Bibliometrics

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2020年第12期60卷 5870-5872页

作者： Zhao, Wenxi Korobskiy, Dmitriy Chandrasekharan, Shreya Merz, Kenneth M., Jr. Chacko, George NET ESolut Corp Netelabs Mclean VA 22102 USA Michigan State Univ Dept Chem E Lansing MI 48824 USA Univ Illinois Dept Comp Sci Urbana IL 61801 USA Univ Illinois Grainger Coll Engn Urbana IL 61801 USA

Modern scientometric techniques, applied at scale, can provide valuable information that complements qualitative investigation of the accumulation of knowledge in a field. We discuss a trio of articles from computatio... 详细信息

关键词： Organic chemistry chemoinformatics Order Computational chemistry Density functional theory

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Inner and Outer Recursive Neural Networks for chemoinformatics Applications

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2018年第2期58卷 207-211页

作者： Urban, Gregor Subrahmanya, Niranjan Badi, Pierre Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA ExxonMobil Res & Engn Co Annandale NJ 08801 USA

Deep learning methods applied to problems in chemoinformatics often require the use of recursive neural networks to handle data with graphical structure and variable size. We present a useful classification of recursive neural network approaches into two classes, the inner and outer approach. The inner approach uses recursion inside the underlying graph, to essentially "crawl" the edges of the graph, while the outer approach uses recursion outside the underlying graph, to aggregate information over progressively longer distances in an orthogonal direction. We illustrate the inner and outer approaches on several examples. More importantly, we provide open-source implementations [available at ***/chemoinformatics/InnerOuterRNN and ***] for both approaches in Tensorflow which can be used in combination with training data to produce efficient models for predicting the physical, chemical, and biological properties of small molecules.

关键词： Neural networks chemoinformatics Molecular modeling Layers Molecules

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A polynomial-time maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2013年第4期69卷 343-376页

作者： Schietgat, Leander Ramon, Jan Bruynooghe, Maurice Katholieke Univ Leuven Dept Comp Sci B-3001 Leuven Belgium

Metrics for structured data have received an increasing interest in the machine learning community. Graphs provide a natural representation for structured data, but a lot of operations on graphs are computationally intractable. In this article, we present a polynomial-time algorithm that computes a maximum common sub-graph of two outerplanar graphs. The algorithm makes use of the block-and-bridge preserving subgraph isomorphism, which has significant efficiency benefits and is also motivated from a chemical perspective. We focus on the application of learning structure-activity relationships, where the task is to predict the chemical activity of molecules. We show how the algorithm can be used to construct a metric for structured data and we evaluate this metric and more generally also the block-and-bridge preserving matching operator on 60 molecular datasets, obtaining state-of-the-art results in terms of predictive performance and efficiency.

关键词： Metrics for structured data Graph mining chemoinformatics Learning structure-activity relationships

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Combinatorial-computational-chemoinformatics (C³) approach to finding and analyzing low-energy tautomers

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JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 2010年第6-7期24卷 627-638页

作者： Haranczyk, Maciej Gutowski, Maciej Univ Calif Berkeley Lawrence Berkeley Lab Computat Res Div Berkeley CA 94720 USA Heriot Watt Univ Chem Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland

Finding the most stable tautomer or a set of low-energy tautomers of molecules is critical in many aspects of molecular modelling or virtual screening experiments. Enumeration of low-energy tautomers of neutral molecules in the gas-phase or typical solvents can be performed by applying available organic chemistry knowledge. This kind of enumeration is implemented in a number of software packages and it is relatively reliable. However, in esoteric cases such as charged molecules in uncommon, non-aqueous solvents there is simply not enough available knowledge to make reliable predictions of low energy tautomers. Over the last few years we have been developing an approach to address the latter problem and we successfully applied it to discover the most stable anionic tautomers of nucleic acid bases that might be involved in the process of DNA damage by low-energy electrons and in charge transfer through DNA. The approach involves three steps: (1) combinatorial generation of a library of tautomers, (2) energy-based screening of the library using electronic structure methods, and (3) analysis of the information generated in step (2). In steps 1-3 we employ combinatorial, computational and chemoinformatics techniques, respectively. Therefore, this hybrid approach is named "Combinatorial*Computational*chemoinformatics", or just abbreviated as C-3 (or C-cube) approach. This article summarizes our developments and most interesting methodological aspects of the C-3 approach. It can serve as an example how to identify the most stable tautomers of molecular systems for which common chemical knowledge had not been sufficient to make definite predictions.

关键词： Tautomer Combinatorial approach Electronic structure methods chemoinformatics

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A chemoinformatics analysis of hit lists obtained from high-throughput affinity-selection screening

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JOURNAL OF BIOMOLECULAR SCREENING 2006年第2期11卷 123-130页

作者： Brown, N Zehender, H Azzaoui, K Schuffenhauer, A Mayr, LM Jacoby, E Novartis Inst Biomed Res CH-4002 Basel Switzerland

The high-throughput affinity-selection screening platform SpeedScreen was recently reported by the Novartis Institutes for BioMedical Research as a homogeneous, label-free screening technology with mass-spectrometry readout. SpeedScreen relies on the screening of compound mixtures with various target proteins and uses fast size-exclusion chromatography to separate target-bound from unbound substances. After disintegration of the target-binder complex, the binder molecules are identified by their molecular masses using liquid chromatography/mass spectrometry. The authors report an analysis of the molecular properties of hits obtained with SpeedScreen on 26 targets screened within the past few years at Novartis using this technology. Affinity-based SpeedScreen is a robust high-throughput screening technology that does not accumulate frequent hitters or potential covalent binders. The hits are representative of the most commonly identified scaffold classes observed for known drugs. Validated SpeedScreen hits tend to be enriched on more lipophilic and larger-molecular-weight compounds compared to the whole library. The potential for a reduced SpeedScreen screening set to be used in case only limited protein quantities are available is evaluated. Such a reduced compound set should also maximize the coverage of the high-performing regions of the chemical property and class spaces;chemoinformatics methods including genetic algorithms and divisive K-means clustering are used for this aim.

关键词： affinity-based high-throughput screening SpeedScreen chemoinformatics chemoinformatics scaffolds genetic algorithms subset selection diversity

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***: An Open Source JavaScript chemoinformatics Toolkit

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2016年第6期56卷 1132-1138页

作者： Jiang, Chen Jin, Xi Dong, Ying Chen, Ming China Pharmaceut Univ Dept Organ Chem Nanjing 210009 Jiangsu Peoples R China China Pharmaceut Univ Dept Foreign Languages Nanjing 210009 Jiangsu Peoples R China

*** is an open-source, object-oriented JavaScript toolkit for chemoinformatics. It provides methods for many common tasks in molecular informatics, including chemical data input/output (I/O), two and three-dimensional (2D/3D) rendering of chemical structure, stereo identification, ring perception, structure comparison, and substructure search. Encapsulated widgets to display and edit chemical structures directly in web context are also supplied. Developed with web standards, the toolkit is ideal for building chemoinformatics applications over the Internet. Moreover, it is highly platform-independent and can also be used in desktop or mobile environments. Some initial applications, such as plugins for inputting chemical structures on the web and uses in chemistry education, have been developed based on the toolkit.

关键词： Chemical structure Algorithms Molecular structure chemoinformatics Molecules

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Exploring the mechanism of Shengmai Yin for coronary heart disease based on systematic pharmacology and chemoinformatics

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BIOSCIENCE REPORTS 2020年第6期40卷 1-24页

作者： Jiang, Yan He, Qi Zhang, Tianqing Xiang, Wang Long, Zhiyong Wu, Shiwei Univ South China Dept Gen Surg Affiliated Hosp 1 Hengyang Hunan Peoples R China Hunan Normal Univ Grad Coll Changsha Hunan Peoples R China Peoples Hosp Ningxiang City Intens Care Unit Ningxiang 410600 Hunan Peoples R China Univ South China Grad Coll Hengyang Hunan Peoples R China Univ South China Dept Cardiol Affiliated Hosp 1 Hengyang Hunan Peoples R China Guilin Med Univ Grad Coll Guilin Guangxi Provinc Peoples R China Guilin Med Univ Dept Rheumatol Affiliated Hosp Guilin Guangxi Provinc Peoples R China Shantou Univ Guangdong Gen Hosp Dept Phys Med & Rehabil Coll Med Shantou Guangdong Peoples R China Shantou Univ Grad Coll Coll Med Shantou Guangdong Peoples R China Sun Yat Sen Univ Affiliated Hosp 8 Dept Tradit Chinese Med Shenzhen Guangdong Peoples R China

Objective: To explore the mechanism of Shengmai Yin (SMY) for coronary heart disease (CHD) by systemic pharmacology and chemoinformatics. Methods: Traditional Chinese Medicine Systems Pharmacology Database (TCMSP), traditional Chinese medicine integrative database (TCMID) and the traditional Chinese medicine (TCM) Database@Taiwan were used to screen and predict the bioactive components of SMY. Pharmmapper were utilized to predict the potential targets of SMY, the TCMSP was utilized to obtain the known targets of SMY. The Genecards and OMIM database were utilized to collect CHD genes. Cytoscape was then used for network construction and analysis, and DAVID was used for Gene Ontology (GO) and pathway enrichment analysis. After that, animal experiments were then performed to further validate the results of systemic pharmacology and chemoinformatics. Results: Three major networks were constructed: (1) CHD genes? protein-protein interaction (PPI) network;(2) SMY-CHD PPI network;(3) SMY known target-CHD PPI network. The other networks are minor networks generated by analyzing the three major networks. Experimental results showed that compared with the model group, the Shengmai injection (SMI) can reduce the myocardial injury score and the activities of serum aspartate aminoconvertase (AST), CK and lactate dehydrogenase (LDH) in rats (P<0.05), and reduce serum lipid peroxide (LPO) content and increase serum superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities in myocardial infarction rats (P<0.05). SMI can also decrease the expression of MMP-9 mRNA and increase that of TIMP-1 mRNA (P<0.01). Conclusion: SMY may regulate the signaling pathways (such as PPAR, FoxO, VEGF signaling), biological processes (such as angiogenesis, blood pressure formation, inflammatory response) and targets (such as AKT1, EGFR, MAPK1) so as to play a therapeutic role in CHD.

关键词： chemoinformatics Chinese medicine Coronary heart disease Shengmai Injection Shengmai Yin Systematic pharmacology

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A Spanish chemoinformatics GitBook for Chemical Data Retrieval and Analysis Using Python Programming

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JOURNAL OF CHEMICAL EDUCATION 2024年第6期101卷 2549-2554页

作者： Saldivar-Gonzalez, Fernanda I. Prado-Romero, Diana L. Cedillo-Gonzalez, Raziel Chavez-Hernandez, Ana L. Avellaneda-Tamayo, Juan F. Gomez-Garcia, Alejandro Juarez-Rivera, Luis Medina-Franco, Jose L. Univ Nacl Autonoma Mexico Sch Chem DIFACQUIM Res Grp Dept Pharm Mexico City 04510 DF Mexico

Searching, retrieving, and analyzing chemical information are among the main tasks faced by students and professionals in chemistry-related scientific disciplines. Currently, freely available modules developed in programming languages, such as Python, allow efficient data management and facilitate the obtaining of information and knowledge from the data. This article describes an electronic handbook generated on the GitBook platform to introduce the Python programming language and the analysis, computational representation, and visualization of chemical data. This manual explores the most common molecular representations of low molecular weight organic compounds and their applications in various contexts. It also illustrates the acquisition of chemical data from large public molecular databases such as ChEMBL and PubChem and the analysis and visualization of chemical information using concepts such as chemical space. The GitBook is freely available (https://***/quimioinformatica/) and is expected to foster open science and facilitate learning for chemistry students at the undergraduate and graduate levels, as well as professionals interested in chemical data analysis and visualization.

关键词： chemoinformatics Scientific Education LatinAmerica Python Spanish-Speaking Community Open Science Handbook

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Prediction of CO2 absorption by physical solvents using a chemoinformatics-based machine learning model

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ENVIRONMENTAL CHEMISTRY LETTERS 2019年第3期17卷 1397-1404页

作者： Li, Hao Yang, Dan Zhang, Zhien Lichtfouse, Eric Univ Texas Austin Dept Chem 105 E 24th StStop A5300 Austin TX 78712 USA Univ Texas Austin Inst Computat & Engn Sci 105 E 24th StStop A5300 Austin TX 78712 USA Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Environm Sci & Technol Engn Lab Shenzhen 518055 Peoples R China Ohio State Univ William G Lowrie Dept Chem & Biomol Engn Columbus OH 43210 USA Aix Marseille Univ Coll France CEREGE CNRSINRAIRD Aix En Provence France

The rising atmospheric CO2 level is partly responsible for global warming. Despite numerous warnings from scientists during the past years, nations are reacting too slowly, and thus, we will probably reach a situation needing rapid and effective techniques to reduce atmospheric CO2. Therefore, advanced engineering methods are particularly important to decrease the greenhouse effect, for instance, by capturing CO2 using solvents. Experimental testing of many solvents under different conditions is necessary but time-consuming. Alternatively, modeling CO2 capture by solvents using a nonlinear fitting machine learning is a rapid way to select potential solvents, prior to experimentation. Previous predictive machine learning models were mainly designed for blended solutions in water using the solution concentration as the main input of the model, which was not able to predict CO2 solubility in different types of physical solvents. To address this issue, here, we developed a new descriptor-based chemoinformatics model for predicting CO2 solubility in physical solvents in the form of mole fraction. The input factors include organic structural and bond information, thermodynamic properties, and experimental conditions. We studied the solvents from 823 data, including methanol (165 data), ethanol (138), n-propanol (98), n-butanol (64), n-pentanol (59), ethylene glycol (52), propylene glycol (54), acetone (51), 2-butanone (49), ethylene glycol monomethyl ether (46 data), and ethylene glycol monoethyl ether (47), using artificial neural networks as the machine learning model. Results show that our descriptor-based model predicts the CO2 absorption in physical solvents with generally higher accuracy and low root-mean-squared errors. Our findings show that using a set of simple but effective chemoinformatics-based descriptors, intrinsic relationships between the general properties of physical solvents and their CO2 solubility can be precisely fitted with machine learning.

关键词： chemoinformatics Greenhouse gas CO2 Absorption Solubility Physical solvent Chemical descriptors Prediction Machine learning Artificial neural network (ANN)

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