Login Register Cart 0
Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe Translate in Chinese
Review Article

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

Author(s): Antreas Afantitis*, Andreas Tsoumanis and Georgia Melagraki *

Volume 27, Issue 38, 2020

Page: [6523 - 6535] Pages: 13

DOI: 10.2174/0929867327666200727114410

Price: $65

Open Access Journals Promotions 2
conference banner
Abstract

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

Keywords: Enalos+ KNIME nodes, chemoinformatics-aided material design, Nanoinformatics, Enalos Suite, Chemical data base, KINME, Efficient data mining, PubChem.

« Previous Next »
[1]
NovaMechanics Ltd. Enalos+ KNIME Nodes, available at http://enalosplus.novamechanics.com (Access date: November 2017.).
[2]
Berthold, M.R.; Cebron, N.; Dill, F.; Gabriel, T.R.; Kötter, T.; Meinl, T.; Ohl, P.; Thiel, K.; Wiswedel, B. KNIME - The konstanz information miner. SIGKDD Explor., 2009, 11(1), 26-31.
[http://dx.doi.org/10.1145/1656274.1656280]
[3]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. Pubchem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[http://dx.doi.org/10.1093/nar/gkv951 ] [PMID: 26400175]
[4]
Melagraki, G.; Afantitis, A. Enalos KNIME nodes: exploring corrosion inhibition of Steel in acidic medium. Chemom. Intell. Lab. Syst., 2013, 123, 9-14.
[http://dx.doi.org/10.1016/j.chemolab.2013.02.003]
[5]
Chambers, J.; Davies, M.; Gaulton, A.; Hersey, A.; Velankar, S.; Petryszak, R.; Hastings, J.; Bellis, L.; McGlinchey, S.; Overington, J.P. UniChem: a unified chemical structure cross-referencing and identifier tracking system. J. Cheminform., 2013, 5(1), 3.
[http://dx.doi.org/10.1186/1758-2946-5-3 ] [PMID: 23317286]
[6]
KNIME Cheminformatics Extensions. Available at https://www.knime. com/cheminformatics-extensions (Accessed Nov 13, 2017).
[7]
Greg Landrum. RDKit: Open-Source Cheminformatics. Available at , http://www.rdkit.org/ (Accessed Nov 13, 2017).
[8]
Steinbeck, C.; Han, Y.; Kuhn, S.; Horlacher, O.; Luttmann, E.; Willighagen, E. The Chemistry Development Kit (CDK): an open-source Java library for chemo- and bioinformatics. J. Chem. Inf. Comput. Sci., 2003, 43(2), 493-500.
[http://dx.doi.org/10.1021/ci025584y ] [PMID: 12653513]
[9]
McGuire, R.; Verhoeven, S.; Vass, M.; Vriend, G.; de Esch, I.J.P.; Lusher, S.J.; Leurs, R.; Ridder, L.; Kooistra, A.J.; Ritschel, T.; de Graaf, C. 3D-e-Chem-VM: structural cheminformatics research infrastructure in a freely available virtual machine. J. Chem. Inf. Model., 2017, 57(2), 115-121.
[http://dx.doi.org/10.1021/acs.jcim.6b00686 ] [PMID: 28125221]
[10]
Gütlein, M.; Karwath, A.; Kramer, S. CheS-mapper - chemical space mapping and visualization in 3D. J. Cheminform., 2012, 4(1), 7.
[http://dx.doi.org/10.1186/1758-2946-4-7 ] [PMID: 22424447]
[11]
BioSolveIT KNIME Interfaces. Available at https://www.biosolveit.de/KNIME/ (Accessed November 2017).
[12]
Schrodinger KNIME Extensions. Available at https://www.schrodinger. com/KNIME-Extensions (Accessed November 2017).
[13]
KNIME Technology Partners. Available at https://www.knime.com/ (Accessed November 2017).
[14]
Melagraki, G.; Afantitis, A. A risk assessment tool for the virtual screening of metal oxide nanoparticles through enalos in silico nano platform. Curr. Top. Med. Chem., 2015, 15(18), 1827-1836.
[http://dx.doi.org/10.2174/1568026615666150506144536 ] [PMID: 26002591]
[15]
Varnek, a; Tropsha, a. Chemoinformatics approaches to virtual screening 2008.
[16]
NIH. NCI/CADD Chemical Identifier Resolver. Available at https://cactus.nci.nih.gov/chemical/structure (Accessed November 2017).
[17]
NanoCommons H2020 Project. Available at www.nanocommons.eu (Accessed November 2017).
[18]
Tsoumanis, A.; Afantitis, A.; Melagraki, G. Enalos APIs for NanoCommons Knowledge Base. Available at, http://enaloscloud. novamechanics.com/EnalosWebServices/KnimeDBWebService?wsdl (Accessed November 2017).
[19]
Hong, H.; Xie, Q.; Ge, W.; Qian, F.; Fang, H.; Shi, L.; Su, Z.; Perkins, R.; Tong, W. Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J. Chem. Inf. Model., 2008, 48(7), 1337-1344.
[http://dx.doi.org/10.1021/ci800038f ] [PMID: 18564836]
[20]
Willighagen, E.L.; Mayfield, J.W.; Alvarsson, J.; Berg, A.; Carlsson, L.; Jeliazkova, N.; Kuhn, S.; Pluskal, T.; Rojas-Chertó, M.; Spjuth, O.; Torrance, G.; Evelo, C.T.; Guha, R.; Steinbeck, C. The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J. Cheminform., 2017, 9(1), 33.
[http://dx.doi.org/10.1186/s13321-017-0220-4] [PMID: 29086040]
[21]
Chambers, J.; Davies, M.; Gaulton, A.; Papadatos, G.; Hersey, A.; Overington, J.P. UniChem: extension of InChI-based compound mapping to salt, connectivity and stereochemistry layers. J. Cheminform., 2014, 6(1), 43.
[http://dx.doi.org/10.1186/s13321-014-0043-5] [PMID: 25221628]
[22]
Tropsha, A. Best Practices for QSAR model development, validation, and exploitation. Mol. Inform., 2010, 29(6-7), 476-488.
[http://dx.doi.org/10.1002/minf.201000061 ] [PMID: 27463326]
[23]
Kennard, R.W.; Stone, L.A. Computer aided design of experiments. Technometrics, 1969, 11(1), 137-148.
[http://dx.doi.org/10.1080/00401706.1969.10490666]
[24]
Hudson, B.D.; Hyde, R.M.; Rahr, E.; Wood, J.; Osman, J. Parameter based methods for compound selection from chemical databases. Quant. Struct. Relationships, 1996, 15(4), 285-289.
[http://dx.doi.org/10.1002/qsar.19960150402]
[25]
Afantitis, A.; Melagraki, G.; Sarimveis, H.; Koutentis, P.A.; Markopoulos, J.; Igglessi-Markopoulou, O. Development and evaluation of a QSPR model for the prediction of diamagnetic susceptibility. QSAR Comb. Sci., 2008, 27(4), 432-436.
[http://dx.doi.org/10.1002/qsar.200730083]
[26]
Afantitis, A.; Melagraki, G.; Tsoumanis, A.; Valsami-Jones, E.; Lynch, I. A nanoinformatics decision support tool for the virtual screening of gold nanoparticle cellular association using protein corona fingerprints. Nanotoxicology, 2018, 12(10), 1148-1165.
[http://dx.doi.org/10.1080/17435390.2018.1504998 ] [PMID: 30182778]
[27]
Varsou, D-D.; Afantitis, A.; Tsoumanis, A.; Papadiamantis, A.; Valsami-Jones, E.; Lynch, I.; Melagraki, G. zeta-potential read-across model utilizing nanodescriptors extracted via the nanoxtract image analysis tool available on the enalos nanoinformatics cloud platform. Small, 2020, 16(21)e1906588
[http://dx.doi.org/10.1002/smll.201906588 ] [PMID: 32174008]
[28]
Afantitis, A. Nanoinformatics: artificial intelligence and nanotechnology in the new decade. Comb. Chem. High Throughput Screen., 2020, 23(1), 4-5.
[http://dx.doi.org/10.2174/138620732301200316112000 ] [PMID: 32189589]
[29]
Harris, M.J.; Baxter, W.V., III; Scheuermann, T.; Lastra, A. Simulation of cloud dynamics on graphics hardware. Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware, 2003, pp. 92-102.
[30]
Varsou, D.D.; Tsoumanis, A.; Afantitis, A.; Melagraki, G. Enalos cloud platform: nanoinformatics and cheminformatics tools. Methods in Pharmacology and Toxicology; Humana Press Inc., 2020, pp. 789-800.
[http://dx.doi.org/10.1007/978-1-0716-0150-1_3]
[31]
Varsou, D-D.; Nikolakopoulos, S.; Tsoumanis, A.; Melagraki, G.; Afantitis, A. Enalos suite: new cheminformatics platform for drug discovery and computational toxicology. Methods Mol. Biol., 2018, 1800, 287-311.
[http://dx.doi.org/10.1007/978-1-4939-7899-1_14 ] [PMID: 29934899]
[32]
NanoSolveIT H2020 Nanoinformatics Project. Available at: www.nanosolveit.eu (Access date: November 2017).
[33]
Afantitis, A.; Melagraki, G.; Isigonis, P.; Tsoumanis, A.; Varsou, D.D.; Valsami-Jones, E.; Papadiamantis, A.; Ellis, L.A.; Sarimveis, H.; Doganis, P.; Karatzas, P.; Tsiros, P.; Liampa, I.; Lobaskin, V.; Greco, D.; Serra, A.; Kinaret, P.A.S.; Saarimäki, L.A.; Grafström, R.; Kohonen, P.; Nymark, P.; Willighagen, E.; Puzyn, T.; Rybinska-Fryca, A.; Lyubartsev, A.; Alstrup Jensen, K.; Brandenburg, J.G.; Lofts, S.; Svendsen, C.; Harrison, S.; Maier, D.; Tamm, K.; Jänes, J.; Sikk, L.; Dusinska, M.; Longhin, E.; Rundén-Pran, E.; Mariussen, E.; El Yamani, N.; Unger, W.; Radnik, J.; Tropsha, A.; Cohen, Y.; Leszczynski, J.; Ogilvie Hendren, C.; Wiesner, M.; Winkler, D.; Suzuki, N.; Yoon, T.H.; Choi, J-S.; Sanabria, N.; Gulumian, M.; Lynch, I. NanoSolveIT project: driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment. Comput. Struct. Biotechnol. J., 2020, 18, 583-602.
[http://dx.doi.org/10.1016/j.csbj.2020.02.023 ] [PMID: 32226594]

Rights & Permissions Print Cite
Article Metrics
35
1
Wayfinder Image
Open Access Journals Promotions
World Antimicrobial Awareness Week
© 2024 Bentham Science Publishers | Privacy Policy