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  1. Article: Molecular symmetry perception.

    Ivanov, Julian

    Journal of chemical information and computer sciences

    2004  Volume 44, Issue 2, Page(s) 596–600

    Abstract: An algorithm for molecular symmetry perception is presented. The method identifies the full set of molecular symmetry elements (proper and improper) and determines their coordinates. The algorithm eliminates the necessity to explore the entire graph ... ...

    Abstract An algorithm for molecular symmetry perception is presented. The method identifies the full set of molecular symmetry elements (proper and improper) and determines their coordinates. The algorithm eliminates the necessity to explore the entire graph automorphism group; as a result its computer application is extremely effective. Application to several dendrimers and fullerenes with high topological symmetry is presented.
    Language English
    Publishing date 2004-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 0095-2338
    ISSN 0095-2338
    DOI 10.1021/ci0341868
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  2. Article ; Online: Quantitative Structure-Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections.

    Ivanov, Julian / Polshakov, Dmitrii / Kato-Weinstein, Junko / Zhou, Qiongqiong / Li, Yingzhu / Granet, Roger / Garner, Linda / Deng, Yi / Liu, Cynthia / Albaiu, Dana / Wilson, Jeffrey / Aultman, Christopher

    ACS omega

    2020  Volume 5, Issue 42, Page(s) 27344–27358

    Abstract: In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics. Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates ...

    Abstract In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics. Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates for the viral targets 3 chymotrypsin-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp). Chemist-curated training sets of substances were assembled from CAS data collections and integrated with curated bioassay data. The best-performing classification models were applied to screen a set of FDA-approved drugs and CAS REGISTRY substances that are similar to, or associated with, antiviral agents. Numerous substances with potential activity against 3CLpro or RdRp were found, and some were validated by published bioassay studies and/or by their inclusion in upcoming or ongoing COVID-19 clinical trials. This study further supports that machine learning-based predictive models may be used to assist the drug discovery process for COVID-19 and other diseases.
    Keywords covid19
    Language English
    Publishing date 2020-10-14
    Publishing country United States
    Document type Journal Article
    ISSN 2470-1343
    ISSN (online) 2470-1343
    DOI 10.1021/acsomega.0c03682
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Quantitative Structure–Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections

    Ivanov, Julian / Polshakov, Dmitrii / Kato-Weinstein, Junko / Zhou, Qiongqiong / Li, Yingzhu / Granet, Roger / Garner, Linda / Deng, Yi / Liu, Cynthia / Albaiu, Dana / Wilson, Jeffrey / Aultman, Christopher

    ACS Omega

    Abstract: In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates ... ...

    Abstract In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates for the viral targets 3 chymotrypsin-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp) Chemist-curated training sets of substances were assembled from CAS data collections and integrated with curated bioassay data The best-performing classification models were applied to screen a set of FDA-approved drugs and CAS REGISTRY substances that are similar to, or associated with, antiviral agents Numerous substances with potential activity against 3CLpro or RdRp were found, and some were validated by published bioassay studies and/or by their inclusion in upcoming or ongoing COVID-19 clinical trials This study further supports that machine learning-based predictive models may be used to assist the drug discovery process for COVID-19 and other diseases
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #872642
    Database COVID19

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  4. Article ; Online: Quantitative Structure–Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections

    Ivanov, Julian / Polshakov, Dmitrii / Kato-Weinstein, Junko / Zhou, Qiongqiong / Li, Yingzhu / Granet, Roger / Garner, Linda / Deng, Yi / Liu, Cynthia / Albaiu, Dana / Wilson, Jeffrey / Aultman, Christopher

    ACS Omega

    2020  Volume 5, Issue 42, Page(s) 27344–27358

    Keywords covid19
    Language English
    Publisher American Chemical Society (ACS)
    Publishing country us
    Document type Article ; Online
    ISSN 2470-1343
    DOI 10.1021/acsomega.0c03682
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: ESP: a method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases.

    Klopman, Gilles / Chakravarti, Suman K / Zhu, Hao / Ivanov, Julian M / Saiakhov, Roustem D

    Journal of chemical information and computer sciences

    2004  Volume 44, Issue 2, Page(s) 704–715

    Abstract: We describe here the development of a computer program which uses a new method called Expert System Prediction (ESP), to predict toxic end points and pharmacological properties of chemicals based on multiple modules created by the MCASE artificial ... ...

    Abstract We describe here the development of a computer program which uses a new method called Expert System Prediction (ESP), to predict toxic end points and pharmacological properties of chemicals based on multiple modules created by the MCASE artificial intelligence system. The modules are generally based on different biological models measuring related end points. The purpose is to improve the decision making process regarding the overall activity or inactivity of the chemicals and also to enable rapid in silico screening. ESP evaluates the significance of the biophores from a different viewpoint and uses this information for predicting the activity of new chemicals. We have used a unique encoding system to represent relevant features of a chemical in the form of a pattern vector and a genetic artificial neural network (GA-ANN) to gain knowledge of the relationship between these patterns and the overall pharmacological property. The effectiveness of ESP is illustrated in the prediction of general carcinogenicity of a diverse set of chemicals using MCASE male/female rats and mice carcinogenicity modules.
    MeSH term(s) Algorithms ; Animals ; Carcinogens/chemistry ; Carcinogens/toxicity ; Databases, Genetic ; Expert Systems ; Female ; Hydrazines/chemistry ; Hydrazines/toxicity ; Male ; Mice ; Nitrosamines/chemistry ; Nitrosamines/toxicity ; Nitroso Compounds/chemistry ; Nitroso Compounds/toxicity ; Pharmacology ; Predictive Value of Tests ; Quantitative Structure-Activity Relationship ; Rats ; Rodentia ; Software ; Toxicity Tests
    Chemical Substances Carcinogens ; Hydrazines ; Nitrosamines ; Nitroso Compounds ; nitrosamides
    Language English
    Publishing date 2004-03
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, P.H.S.
    ZDB-ID 190019-5
    ISSN 0095-2338
    ISSN 0095-2338
    DOI 10.1021/ci030298n
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1230: Show issues Location:
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  6. Article: A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals.

    Matthews, Edwin J / Kruhlak, Naomi L / Daniel Benz, R / Ivanov, Julian / Klopman, Gilles / Contrera, Joseph F

    Regulatory toxicology and pharmacology : RTP

    2007  Volume 47, Issue 2, Page(s) 136–155

    Abstract: This report describes the construction, optimization and validation of a battery of quantitative structure-activity relationship (QSAR) models to predict reproductive and developmental (reprotox) hazards of untested chemicals. These models run with MC4PC ...

    Abstract This report describes the construction, optimization and validation of a battery of quantitative structure-activity relationship (QSAR) models to predict reproductive and developmental (reprotox) hazards of untested chemicals. These models run with MC4PC software to predict seven general reprotox classes: male and female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity. The reprotox QSARs incorporate a weight of evidence paradigm using rats, mice, and rabbit reprotox study data and are designed to identify trans-species reprotoxicants. The majority of the reprotox QSARs exhibit good predictive performance properties: high specificity (>80%), low false positives (<20%), significant receiver operating characteristic (ROC) values (>2.00), and high coverage (>80%) in 10% leave-many-out validation experiments. The QSARs are based on 627-2023 chemicals and exhibited a wide applicability domain for FDA regulated organic chemicals for which they were designed. Experiments were also performed using the MC4PC multiple module prediction technology, and ROC statistics, and adjustments to the ratio of active to inactive (A/I ratio) chemicals in training data sets were made to optimize the predictive performance of QSAR models. Results revealed that an A/I ratio of approximately 40% was optimal for MC4PC. We discuss specific recommendations for the application of the reprotox QSAR battery.
    MeSH term(s) Abnormalities, Drug-Induced ; Animals ; Computer Simulation ; Databases, Factual ; Embryonic Development/drug effects ; Female ; Humans ; Male ; Mice ; Models, Theoretical ; Predictive Value of Tests ; Quantitative Structure-Activity Relationship ; Rabbits ; Rats ; Reproduction/drug effects ; Species Specificity ; Teratogens/classification ; Terminology as Topic ; Toxicity Tests
    Chemical Substances Teratogens
    Language English
    Publishing date 2007-03
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 604672-1
    ISSN 0273-2300
    ISSN 0273-2300
    DOI 10.1016/j.yrtph.2006.10.001
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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