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QSPR designer – employ your own descriptors in the automated QSAR modeling process

Ondřej Skřehota, Radka Vařeková, Stanislav Geidl, Michal Kudera, David Sehnal, Crina-Maria Ionescu, Jan Žídek, Jaroslav Koča
2012 Journal of Cheminformatics  
The process of design, parameterization and evaluation of a QSAR/QSPR model is relatively complicated. Therefore, several software tools for its automation are currently under development [2, 3] .  ...  The prediction of physical and chemical properties of molecules is a very important step in the drug discovery process. QSAR and QSPR models are strong tools for predicting these properties.  ...  The process of design, parameterization and evaluation of a QSAR/QSPR model is relatively complicated. Therefore, several software tools for its automation are currently under development [2, 3] .  ... 
doi:10.1186/1758-2946-4-s1-p37 pmcid:PMC3341281 fatcat:4tfmy6dv3fagvbzvwg4fig64yq

QSAR DataBank repository: open and linked qualitative and quantitative structure–activity relationship models

V Ruusmann, S Sild, U Maran
2015 Journal of Cheminformatics  
Description: The QSAR DataBank (QsarDB) repository aims to make the processes and outcomes of in silico modelling work transparent, reproducible and accessible.  ...  Structure-activity relationship models have been used to gain insight into chemical and physical processes in biomedicine, toxicology, biotechnology, etc. for almost a century.  ...  Acknowledgements The authors acknowledge the European Union FP6 Chemomentum Project (grant: IST-5-033437), Estonian Science Foundation (grants: 5805, 7709), Estonian Ministry for Education and Research  ... 
doi:10.1186/s13321-015-0082-6 pmid:26110025 pmcid:PMC4479250 fatcat:gqnnig7ryfenddwd52grkzipr4

QSAR DataBank - an approach for the digital organization and archiving of QSAR model information

Villu Ruusmann, Sulev Sild, Uko Maran
2014 Journal of Cheminformatics  
The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide  ...  The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical  ...  OpenMolGRID employed an internal data exchange format for the development and use of QSAR models within the automated workflow system [7] .  ... 
doi:10.1186/1758-2946-6-25 pmid:24910716 pmcid:PMC4047268 fatcat:6l3fswbeq5f6jdnmqiryapv33a

What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery

Edward O. Pyzer-Knapp, Changwon Suh, Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre, Alán Aspuru-Guzik
2015 Annual review of materials research (Print)  
Additionally advice on the storing, analysis and visualization of data is given, based upon extensive experience in the group. • What is High Throughput Virtual Screening?  ...  an indepth discussion of the generation of molecular libraries.  ...  ACKNOWLEDGMENTS The authors wish to thank Martin Blood-Forsythe and Suleyman Er for helpful discussions. The PI also wishes to acknowledge Samsung Electronics Co., Ltd.  ... 
doi:10.1146/annurev-matsci-070214-020823 fatcat:3jkzwdz37fay3ibghbv6b2kju4

The 18th European Symposium on Quantitative Structure–Activity Relationships

Anna Tsantili-Kakoulidou, Dimitris K Agrafiotis
2011 Expert Opinion on Drug Discovery  
effective binding in the kinase.  ...  The authors are also thankful to Biobyte Corp. and Dr Hansch and Dr Leo for their support and free access to the C-QSAR program.  ...  SAR/QSAR/QSPR modeling approaches.  ... 
doi:10.1517/17460441.2011.560604 pmid:22646021 fatcat:tb4bhvtnpzahxm4xba7iw4afuy

Eu Us Roadmap Nanoinformatics 2030 [article]

Haase, Klaessig
2018 Zenodo  
The opinions expressed in this document are solely those of the authors.  ...  In particular it should be noted that some of the terms might be defined and used differently in the US versus the EU, also within different scientific disciplines and within different regulatory frameworks  ...  In both, after descriptors have been selected and values were set, the computational nanoEHS model can be solved (e.g., QSAR, QSPR, trend analysis etc.) and then be compared to either measured properties  ... 
doi:10.5281/zenodo.1486012 fatcat:yll2tsodjbgmlmyoyjfkcptxja

Review of bioinformatic tools used in Computer Aided Drug Design (CADD)

Namitha K N, V Velmurugan
2022 World Journal of Advanced Research and Reviews  
Molecular modelling is the process of designing a molecule with a computer-based collection of programmes (in-silico design) for deriving, representing, and manipulating the structures and reactions of  ...  All these tools are highly useful in the field of drug design and discovery. The article will be helpful for selecting a tool for computer aided drug design.  ...  al., 2014), and have implemented them in several SAR/QSAR/QSPR modelling approaches [45, 46] .  ... 
doi:10.30574/wjarr.2022.14.2.0394 fatcat:r5jfcism4fdntac7hl5vifyzba

Drug Design by Pharmacophore and Virtual Screening Approach

Deborah Giordano, Carmen Biancaniello, Maria Antonia Argenio, Angelo Facchiano
2022 Pharmaceuticals  
This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature  ...  Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies.  ...  The QSAR approaches are classified from the 1D to the 6D-QSAR according to the different dimensions of the descriptors implemented in the method. Figure 4 . 4 Figure 4. Virtual screening workflow.  ... 
doi:10.3390/ph15050646 fatcat:wzrl2d4yynhz3oq4gr5dzk3ek4

Opportunities and Challenges for Machine Learning in Materials Science

Dane Morgan, Ryan Jacobs
2020 Annual review of materials research (Print)  
detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models.  ...  Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities and the best practices for their use.  ...  gap models and machine learning infrastructure; and the University of Wisconsin-Madison Materials Research Science and Engineering Center (DMR-1720415), which supported projects related to QSAR/QSPR that  ... 
doi:10.1146/annurev-matsci-070218-010015 fatcat:7af5ywmnu5awljfz4szlrwwlyu

Food irradiation: current status and future prospects [chapter]

P. Loaharanu
1995 New Methods of Food Preservation  
In these tools, in vitro and/or in vivo ADME data are integrated with the results of QSAR/QSPR models (e.g. for percentage plasma protein binding or blood/brain barrier penetration) for organism-based  ...  Most of the models are based on non-linear relationships and utilise large numbers of molecular descriptors in order to capture the multiple features affecting the clearance process.  ...  QSAR) models and related computational methods.  ... 
doi:10.1007/978-1-4615-2105-1_5 fatcat:7jsivrenbrfhjk2parum6vwlam

Mechanistic understanding of in silico toxicity predictions

Diana Suarez, Carol A. Marchant, Mukesh L. Patel
2008 Toxicology Letters  
The authors are grateful to Sharon Munn (JRC) for reviewing and providing useful comments on this report.  ...  Derek rules are not built from the automated analysis of training sets.  ...  How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR Ferrari T, Gini G & Benfenati E (2009).  ... 
doi:10.1016/j.toxlet.2008.06.640 fatcat:7cfpd7qksffmjdzhwulo7yibpy

11th German Conference on Chemoinformatics (GCC 2015)

Uli Fechner, Chris de Graaf, Andrew E. Torda, Stefan Güssregen, Andreas Evers, Hans Matter, Gerhard Hessler, Nicola J. Richmond, Peter Schmidtke, Marwin H. S. Segler, Mark P. Waller, Stefanie Pleik (+248 others)
2016 Journal of Cheminformatics  
Statistical modeling (also termed QSAR/QSPR) is a general name for a host of methods that correlate a specific activity for a set of compounds with their structure-derived descriptors by means of a mathematical  ...  In the aqueous environment of interest in drug design applications, representing a compound as any one of its tautomers is likely to distort QSAR models trained using that tautomer.  ...  Several directions for in-silico design of molecules exhibiting augmented selectivity are shown.  ... 
doi:10.1186/s13321-016-0119-5 pmid:29270804 pmcid:PMC4896257 fatcat:akoqbbe6fvc5bgc6qwnxqhiaya

Virtual screening and lead optimisation to identify novel inhibitors for HDAC-8 [article]

Maria Antony Dhivyan JE, Anoop MN
2012 arXiv   pre-print
Bioactivity prediction of the best ranked ligands was done. Their physicochemical properties were also analyzed. Four new molecules were identified and suggested for further testing in the wet lab.  ...  There is a growing interest in the development of histone deacetylase inhibitors as anti cancer agents. Three known ligands of HDAC-8 were taken and docked.  ...  COMPUTER-AIDED DRUG DESIGN (CADD) Computer-aided drug design (CADD), is also called computer-aided molecular design (CAMD), represents more recent applications of computer as tools in the drug design process  ... 
arXiv:1209.2793v1 fatcat:l6nmes33hnc47iuuy6uhkyleiu

Computational Simulations to Aid in the Experimental Discovery of Ice Recrystallization Inhibitors and Ultra-Microporous Metal Organic Frameworks

Phil De Luna, Université D'Ottawa / University Of Ottawa, Université D'Ottawa / University Of Ottawa
Herein we present work where we have utilized a quantitative structure activity relationship (QSAR) model to predict whether a molecule is active or inactive.  ...  Robert Ben have been successful in synthesizing small organic molecules which are capable of inhibiting the growth of ice crystals during the freezing process.  ...  This is another topic of focus in the Woo Lab with QSPR models being utilized to screen for high-performing MOFs with respect to CO2 capture, 169 methane storage, 170 and development of QSPR descriptors  ... 
doi:10.20381/ruor-6669 fatcat:xscq33ussvdwbnaheu6ktgavzm

Processing and Formulation of Concentrated Protein Solutions - Strategies for their Characterization and Stabilization

Katharina Christin Bauer
"It's hard to dance with a devil on your back so shake him off."  ...  All die guten Augenblicke mit euch kann man nicht in Worte fassen. Es ist ein schönes Gefühl jeden Einzelnen in meinem Leben zu wissen.  ...  ), both funded by the German Federal Ministry of Education and Research (BMBF).  ... 
doi:10.5445/ir/1000064829 fatcat:ois3unqvkbc57g5n3r3mwf2brm
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