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Showing 1 to 12 of 71 entries
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Chemical Space: Big Data Challenge for Molecular Diversity.

Chimia

Awale M, Visini R, Probst D, Arús-Pous J, Reymond JL.
PMID: 29070411
Chimia (Aarau). 2017 Oct 25;71(10):661-666. doi: 10.2533/chimia.2017.661.

Chemical space describes all possible molecules as well as multi-dimensional conceptual spaces representing the structural diversity of these molecules. Part of this chemical space is available in public databases ranging from thousands to billions of compounds. Exploiting these databases...

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Awale M, Visini R, Probst D, et al. Chemical Space: Big Data Challenge for Molecular Diversity. Chimia (Aarau). 2017;71(10):661-666doi: 10.2533/chimia.2017.661.
Awale, M., Visini, R., Probst, D., Arús-Pous, J., & Reymond, J. L. (2017). Chemical Space: Big Data Challenge for Molecular Diversity. Chimia, 71(10), 661-666. https://doi.org/10.2533/chimia.2017.661
Awale, Mahendra, et al. "Chemical Space: Big Data Challenge for Molecular Diversity." Chimia vol. 71,10 (2017): 661-666. doi: https://doi.org/10.2533/chimia.2017.661
Awale M, Visini R, Probst D, Arús-Pous J, Reymond JL. Chemical Space: Big Data Challenge for Molecular Diversity. Chimia (Aarau). 2017 Oct 25;71(10):661-666. doi: 10.2533/chimia.2017.661. PMID: 29070411.
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Identification of Chemical Toxicity Using Ontology Information of Chemicals.

Computational and mathematical methods in medicine

Jiang Z, Xu R, Dong C.
PMID: 26508991
Comput Math Methods Med. 2015;2015:246374. doi: 10.1155/2015/246374. Epub 2015 Oct 05.

With the advance of the combinatorial chemistry, a large number of synthetic compounds have surged. However, we have limited knowledge about them. On the other hand, the speed of designing new drugs is very slow. One of the key...

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Jiang Z, Xu R, Dong C. Identification of Chemical Toxicity Using Ontology Information of Chemicals. Comput Math Methods Med. 2015;2015:246374doi: 10.1155/2015/246374.
Jiang, Z., Xu, R., & Dong, C. (2015). Identification of Chemical Toxicity Using Ontology Information of Chemicals. Computational and mathematical methods in medicine, 2015246374. https://doi.org/10.1155/2015/246374
Jiang, Zhanpeng, et al. "Identification of Chemical Toxicity Using Ontology Information of Chemicals." Computational and mathematical methods in medicine vol. 2015 (2015): 246374. doi: https://doi.org/10.1155/2015/246374
Jiang Z, Xu R, Dong C. Identification of Chemical Toxicity Using Ontology Information of Chemicals. Comput Math Methods Med. 2015;2015:246374. doi: 10.1155/2015/246374. Epub 2015 Oct 05. PMID: 26508991; PMCID: PMC4609800.
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2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine.

Journal of the Royal Society, Interface

Hamon V, Bourgeas R, Ducrot P, Theret I, Xuereb L, Basse MJ, Brunel JM, Combes S, Morelli X, Roche P.
PMID: 24196694
J R Soc Interface. 2013 Nov 06;11(90):20130860. doi: 10.1098/rsif.2013.0860. Print 2014 Jan 06.

Over the last 10 years, protein-protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to...

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Hamon V, Bourgeas R, Ducrot P, et al. 2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine. J R Soc Interface. 2013;11(90):20130860doi: 10.1098/rsif.2013.0860.
Hamon, V., Bourgeas, R., Ducrot, P., Theret, I., Xuereb, L., Basse, M. J., Brunel, J. M., Combes, S., Morelli, X., & Roche, P. (2014). 2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine. Journal of the Royal Society, Interface, 11(90), 20130860. https://doi.org/10.1098/rsif.2013.0860
Hamon, Véronique, et al. "2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine." Journal of the Royal Society, Interface vol. 11,90 (2014): 20130860. doi: https://doi.org/10.1098/rsif.2013.0860
Hamon V, Bourgeas R, Ducrot P, Theret I, Xuereb L, Basse MJ, Brunel JM, Combes S, Morelli X, Roche P. 2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine. J R Soc Interface. 2013 Nov 06;11(90):20130860. doi: 10.1098/rsif.2013.0860. Print 2014 Jan 06. PMID: 24196694; PMCID: PMC3836326.
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Using self-organizing maps to accelerate similarity search.

Bioorganic & medicinal chemistry

Bonachera F, Marcou G, Kireeva N, Varnek A, Horvath D.
PMID: 22595424
Bioorg Med Chem. 2012 Sep 15;20(18):5396-409. doi: 10.1016/j.bmc.2012.04.024. Epub 2012 Apr 21.

While self-organizing maps (SOM) have often been used to map and describe chemical space, this paper focuses on their use to accelerate similarity searches based on vectors of high-dimensional real-value descriptors for which classical, binary fingerprint-based similarity speed-up procedures...

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Bonachera F, Marcou G, Kireeva N, et al. Using self-organizing maps to accelerate similarity search. Bioorg Med Chem. 2012;20(18):5396-409doi: 10.1016/j.bmc.2012.04.024.
Bonachera, F., Marcou, G., Kireeva, N., Varnek, A., & Horvath, D. (2012). Using self-organizing maps to accelerate similarity search. Bioorganic & medicinal chemistry, 20(18), 5396-409. https://doi.org/10.1016/j.bmc.2012.04.024
Bonachera, Fanny, et al. "Using self-organizing maps to accelerate similarity search." Bioorganic & medicinal chemistry vol. 20,18 (2012): 5396-409. doi: https://doi.org/10.1016/j.bmc.2012.04.024
Bonachera F, Marcou G, Kireeva N, Varnek A, Horvath D. Using self-organizing maps to accelerate similarity search. Bioorg Med Chem. 2012 Sep 15;20(18):5396-409. doi: 10.1016/j.bmc.2012.04.024. Epub 2012 Apr 21. PMID: 22595424.
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Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics.

Drug design, development and therapy

Liu L, Ma Y, Wang RL, Xu WR, Wang SQ, Chou KC.
PMID: 23630413
Drug Des Devel Ther. 2013 Apr 08;7:279-88. doi: 10.2147/DDDT.S42113. Print 2013.

The high prevalence of type 2 diabetes mellitus in the world as well as the increasing reports about the adverse side effects of the existing diabetes treatment drugs have made developing new and effective drugs against the disease a...

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Liu L, Ma Y, Wang RL, et al. Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics. Drug Des Devel Ther. 2013;7:279-88doi: 10.2147/DDDT.S42113.
Liu, L., Ma, Y., Wang, R. L., Xu, W. R., Wang, S. Q., & Chou, K. C. (2013). Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics. Drug design, development and therapy, 7279-88. https://doi.org/10.2147/DDDT.S42113
Liu, Lei, et al. "Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics." Drug design, development and therapy vol. 7 (2013): 279-88. doi: https://doi.org/10.2147/DDDT.S42113
Liu L, Ma Y, Wang RL, Xu WR, Wang SQ, Chou KC. Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics. Drug Des Devel Ther. 2013 Apr 08;7:279-88. doi: 10.2147/DDDT.S42113. Print 2013. PMID: 23630413; PMCID: PMC3623550.
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Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery.

Current medicinal chemistry

Yang J, Wang D, Jia C, Wang M, Hao G, Yang G.
PMID: 29737247
Curr Med Chem. 2019;26(42):7581-7597. doi: 10.2174/0929867325666180508100436.

BACKGROUND: In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to...

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Yang J, Wang D, Jia C, et al. Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery. Curr Med Chem. 2019;26(42):7581-7597doi: 10.2174/0929867325666180508100436.
Yang, J., Wang, D., Jia, C., Wang, M., Hao, G., & Yang, G. (2019). Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery. Current medicinal chemistry, 26(42), 7581-7597. https://doi.org/10.2174/0929867325666180508100436
Yang, JingFang, et al. "Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery." Current medicinal chemistry vol. 26,42 (2019): 7581-7597. doi: https://doi.org/10.2174/0929867325666180508100436
Yang J, Wang D, Jia C, Wang M, Hao G, Yang G. Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery. Curr Med Chem. 2019;26(42):7581-7597. doi: 10.2174/0929867325666180508100436. PMID: 29737247.
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Virtual Chemical Libraries.

Journal of medicinal chemistry

Walters WP.
PMID: 30148631
J Med Chem. 2019 Feb 14;62(3):1116-1124. doi: 10.1021/acs.jmedchem.8b01048. Epub 2018 Sep 10.

Advances in computer processing speed and storage capacity have enabled researchers to generate virtual chemical libraries containing billions of molecules. While these numbers appear large, they are only a small fraction of the number of organic molecules that could...

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Walters WP. Virtual Chemical Libraries. J Med Chem. 2018;62(3):1116-1124doi: 10.1021/acs.jmedchem.8b01048.
Walters, W. P. (2019). Virtual Chemical Libraries. Journal of medicinal chemistry, 62(3), 1116-1124. https://doi.org/10.1021/acs.jmedchem.8b01048
Walters, W Patrick. "Virtual Chemical Libraries." Journal of medicinal chemistry vol. 62,3 (2019): 1116-1124. doi: https://doi.org/10.1021/acs.jmedchem.8b01048
Walters WP. Virtual Chemical Libraries. J Med Chem. 2019 Feb 14;62(3):1116-1124. doi: 10.1021/acs.jmedchem.8b01048. Epub 2018 Sep 10. PMID: 30148631.
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The Evolution of Digital Chemistry at Southampton.

Molecular informatics

Bird C, Coles SJ, Frey JG.
PMID: 27490710
Mol Inform. 2015 Sep;34(9):585-97. doi: 10.1002/minf.201500008.

In this paper we take a historical view of e-Science and e-Research developments within the Chemical Sciences at the University of Southampton, showing the development of several stages of the evolving data ecosystem as Chemistry moves into the digital...

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Bird C, Coles SJ, Frey JG. The Evolution of Digital Chemistry at Southampton. Mol Inform. 2015;34(9):585-97doi: 10.1002/minf.201500008.
Bird, C., Coles, S. J., & Frey, J. G. (2015). The Evolution of Digital Chemistry at Southampton. Molecular informatics, 34(9), 585-97. https://doi.org/10.1002/minf.201500008
Bird, Colin, et al. "The Evolution of Digital Chemistry at Southampton." Molecular informatics vol. 34,9 (2015): 585-97. doi: https://doi.org/10.1002/minf.201500008
Bird C, Coles SJ, Frey JG. The Evolution of Digital Chemistry at Southampton. Mol Inform. 2015 Sep;34(9):585-97. doi: 10.1002/minf.201500008. PMID: 27490710.
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Illuminating Flash Point: Comprehensive Prediction Models.

Molecular informatics

Le TC, Ballard M, Casey P, Liu MS, Winkler DA.
PMID: 27490859
Mol Inform. 2015 Jan;34(1):18-27. doi: 10.1002/minf.201400098. Epub 2014 Nov 04.

Flash point is an important property of chemical compounds that is widely used to evaluate flammability hazard. However, there is often a significant gap between the demand for experimental flash point data and their availability. Furthermore, the determination of...

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Le TC, Ballard M, Casey P, et al. Illuminating Flash Point: Comprehensive Prediction Models. Mol Inform. 2014;34(1):18-27doi: 10.1002/minf.201400098.
Le, T. C., Ballard, M., Casey, P., Liu, M. S., & Winkler, D. A. (2015). Illuminating Flash Point: Comprehensive Prediction Models. Molecular informatics, 34(1), 18-27. https://doi.org/10.1002/minf.201400098
Le, Tu C, et al. "Illuminating Flash Point: Comprehensive Prediction Models." Molecular informatics vol. 34,1 (2015): 18-27. doi: https://doi.org/10.1002/minf.201400098
Le TC, Ballard M, Casey P, Liu MS, Winkler DA. Illuminating Flash Point: Comprehensive Prediction Models. Mol Inform. 2015 Jan;34(1):18-27. doi: 10.1002/minf.201400098. Epub 2014 Nov 04. PMID: 27490859.
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nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data.

Analytical chemistry

Schober D, Jacob D, Wilson M, Cruz JA, Marcu A, Grant JR, Moing A, Deborde C, de Figueiredo LF, Haug K, Rocca-Serra P, Easton J, Ebbels TMD, Hao J, Ludwig C, Günther UL, Rosato A, Klein MS, Lewis IA, Luchinat C, Jones AR, Grauslys A, Larralde M, Yokochi M, Kobayashi N, Porzel A, Griffin JL, Viant MR, Wishart DS, Steinbeck C, Salek RM, Neumann S.
PMID: 29035042
Anal Chem. 2018 Jan 02;90(1):649-656. doi: 10.1021/acs.analchem.7b02795. Epub 2017 Dec 14.

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage...

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Schober D, Jacob D, Wilson M, et al. nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data. Anal Chem. 2017;90(1):649-656doi: 10.1021/acs.analchem.7b02795.
Schober, D., Jacob, D., Wilson, M., Cruz, J. A., Marcu, A., Grant, J. R., Moing, A., Deborde, C., de Figueiredo, L. F., Haug, K., Rocca-Serra, P., Easton, J., Ebbels, T. M. D., Hao, J., Ludwig, C., Günther, U. L., Rosato, A., Klein, M. S., Lewis, I. A., Luchinat, C., Jones, A. R., Grauslys, A., Larralde, M., Yokochi, M., Kobayashi, N., Porzel, A., Griffin, J. L., Viant, M. R., Wishart, D. S., Steinbeck, C., Salek, R. M., & Neumann, S. (2018). nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data. Analytical chemistry, 90(1), 649-656. https://doi.org/10.1021/acs.analchem.7b02795
Schober, Daniel, et al. "nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data." Analytical chemistry vol. 90,1 (2018): 649-656. doi: https://doi.org/10.1021/acs.analchem.7b02795
Schober D, Jacob D, Wilson M, Cruz JA, Marcu A, Grant JR, Moing A, Deborde C, de Figueiredo LF, Haug K, Rocca-Serra P, Easton J, Ebbels TMD, Hao J, Ludwig C, Günther UL, Rosato A, Klein MS, Lewis IA, Luchinat C, Jones AR, Grauslys A, Larralde M, Yokochi M, Kobayashi N, Porzel A, Griffin JL, Viant MR, Wishart DS, Steinbeck C, Salek RM, Neumann S. nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data. Anal Chem. 2018 Jan 02;90(1):649-656. doi: 10.1021/acs.analchem.7b02795. Epub 2017 Dec 14. PMID: 29035042.
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Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery.

Expert opinion on drug discovery

Nowotka MM, Gaulton A, Mendez D, Bento AP, Hersey A, Leach A.
PMID: 28602100
Expert Opin Drug Discov. 2017 Aug;12(8):757-767. doi: 10.1080/17460441.2017.1339032. Epub 2017 Jun 12.

INTRODUCTION: ChEMBL is a manually curated database of bioactivity data on small drug-like molecules, used by drug discovery scientists. Among many access methods, a REST API provides programmatic access, allowing the remote retrieval of ChEMBL data and its integration...

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Nowotka MM, Gaulton A, Mendez D, et al. Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery. Expert Opin Drug Discov. 2017;12(8):757-767doi: 10.1080/17460441.2017.1339032.
Nowotka, M. M., Gaulton, A., Mendez, D., Bento, A. P., Hersey, A., & Leach, A. (2017). Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery. Expert opinion on drug discovery, 12(8), 757-767. https://doi.org/10.1080/17460441.2017.1339032
Nowotka, Michał M, et al. "Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery." Expert opinion on drug discovery vol. 12,8 (2017): 757-767. doi: https://doi.org/10.1080/17460441.2017.1339032
Nowotka MM, Gaulton A, Mendez D, Bento AP, Hersey A, Leach A. Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery. Expert Opin Drug Discov. 2017 Aug;12(8):757-767. doi: 10.1080/17460441.2017.1339032. Epub 2017 Jun 12. PMID: 28602100; PMCID: PMC6321761.
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Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening.

SLAS discovery : advancing life sciences R & D

Mazoure B, Caraus I, Nadon R, Makarenkov V.
PMID: 29346010
SLAS Discov. 2018 Jun;23(5):448-458. doi: 10.1177/2472555217750377. Epub 2018 Jan 18.

Data generated by high-throughput screening (HTS) technologies are prone to spatial bias. Traditionally, bias correction methods used in HTS assume either a simple additive or, more recently, a simple multiplicative spatial bias model. These models do not, however, always...

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Mazoure B, Caraus I, Nadon R, et al. Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening. SLAS Discov. 2018;23(5):448-458doi: 10.1177/2472555217750377.
Mazoure, B., Caraus, I., Nadon, R., & Makarenkov, V. (2018). Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening. SLAS discovery : advancing life sciences R & D, 23(5), 448-458. https://doi.org/10.1177/2472555217750377
Mazoure, Bogdan, et al. "Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening." SLAS discovery : advancing life sciences R & D vol. 23,5 (2018): 448-458. doi: https://doi.org/10.1177/2472555217750377
Mazoure B, Caraus I, Nadon R, Makarenkov V. Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening. SLAS Discov. 2018 Jun;23(5):448-458. doi: 10.1177/2472555217750377. Epub 2018 Jan 18. PMID: 29346010.
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Showing 1 to 12 of 71 entries