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4 results for “chemoinformatics”
Benchmark comparison tests between Ambit-SMIRKS and RDKit chemoinformatics tools
<p>This archive contains benchmark code and results for Ambit-SMIRKS software package (<a href="http://ambit.sf.net">http://ambit.sf.net</a>) , described in the publication “Kochev N,, Avramova S., Jeliazkova N. Ambit-SMIRKS: a Software Module for Reaction Representation, Reaction Search and Structure Transformation”. </p> <p>We have performed benchmark testing of Ambit-SMIRKS and RDKit SMIRKS transformation algorithms. For this purpose we used a set of 545 compounds (see file <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/smiles-set.txt?versionId=a49915af-23b7-41d2-9625-4a90cee172b4">smiles-set.txt</a>) including normal constituents of the body or common components of food, provided by Munro et al. [1] and a set of 84 reactions from RetroTransformDB [2] represented as SMIRKS linear notations (see file <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/SMIRKS-RetroDB.txt?versionId=2fa6cb02-013a-4f69-b89a-c18d03b09222">SMIRKS-RetroDB.txt</a>). In both software tools (RDKit and Ambit-SMIRKS), each reaction was applied for all compounds at all possible sites thus performing more than 46000 SMIRKS transformations. For the purpose of comparison, Ambit-SMIRKS was applied in mode ALL with a single copy of the products for each reaction site. The java code for Ambit-SMIRKS test is available in file <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/TestAmbitSmirks.java?versionId=fcb01365-b869-4801-9c5d-43cf8765103a">TestAmbitSmirks.java</a> and respectively python code for RDKit test is present in <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/rdkit-smirks-test-02.py?versionId=d42ab1c5-4c1f-4fad-8457-a6e8a8f4a3ec">rdkit-smirks-test-02.py</a>. In order to run the tests, Ambit dependency modules (version 3.2.0) are required (see more about Ambit at https://ambit.sf.net/) as well as RDKit (release 2018.03) installation is needed (see http://www.rdkit.org/).</p> <p>The tests were performed on a PC computer (Intel/Core i5-8250U, 1.6GHz/12 GB RAM), under Win10 Operating system. The calculations took about 30 seconds for RDKit software and about 40 seconds for Ambit-SMIRKS. The computational time for both software includes the SMIRKS parsing and reaction application as well as molecule preprocessing and file operations. Each algorithm was run 3 times. Detail timing info is present in file <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/time-stat.txt?versionId=57e06cd1-d454-4bc0-a369-13b1e4d6150f">time-stat.txt</a>.</p> <p>The raw data outputs for both software tools respectively are stored in files: <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/rdkit-out.txt?versionId=2fa730f9-6ed8-4e51-a42c-c47799368f3b">rdkit-out.txt </a>and <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/ambit-out-no-eq-filter.txt?versionId=0519c609-06ae-4da8-b3c8-28a316d517d0">ambit-out-no-eq-filter.txt </a></p> <p>The generated output files are constructed from blocks for each SMIRKS in the following format:</p> <p>##smirks-number <SMIRKS></p> <p><smiles1> --> <number of reaction sites> <products1>, <products2>, …</p> <p><smiles2> --> <number of reaction sites> <products1>, <products2>, …</p> <p>…</p> <p><smiles545> --> <number of reaction sites> <products1>, <products2>, …</p> <p>On the base of generated raw test data, comparison statistics was summarized in file <a href="https://zenodo.org/api/files/b8c44e94-55a6-48b9-bd11-ff5cee71e55a/compare-ambit-rdkit.xlsx?versionId=5d4d6673-a9f0-47fd-8512-86112b3483f6">compare-ambit-rdkit.xlsx </a>containing following columns: <strong>SMILES</strong> – target molecule smiles, <strong>smirks_num</strong> – the index of reaction SMIRKS applied against the target, <strong>Ambit-NEF</strong> – number of reacted sites in the target molecule for Ambit algorithm, <strong>RDKit</strong> - number of reacted sites in the target molecule for RDKit algorithm , <strong>Diff</strong> – absolute difference the number reacted sites in Ambit and RDKit, <strong>FlagDiff</strong> – it is 1 (true) if the <strong>Diff</strong> is non zero, <strong>FlagRDKitReact</strong> – it is 1 (true) if at least one site is reacted in the target molecule by RDKit tool (i.e. RDKit column values > 0), <strong>FlagAmbitReact</strong> - it is 1 (true) if at least one site is reacted in the target molecule by Ambit-SMIRKS tool (i.e. Ambit-NEF column values > 0).</p> <p>Out of 46410 tests, 6096 test reactions were successfully applied for at least one site in Ambit-SMIRKS (i.e. the value in column Ambit-NEF is not zero) and 5729 reactions were successfully applied for at least one site in RDKit accordingly (i.e. the value in column RDKit is not zero). The obtained total number of reacted sites for Ambit-SMIRKS and RDKit is 41453 and 40782 respectively. We have performed statistics of the number of reacted sites for both software tools and differences were observed for 436 reactions. From our analysis we may infer that the observed differences are mainly due to different treatment of equivalent molecules sites and some small differences of the internal presentation of the molecules and the chemical reactions on both software packages. </p> <p>[1] Munro I., Ford RA, Kennepohl E, Sprenger J. Correlation of structural class with no-observed-effect-levels: a proposal for establishing a threshold of concern. Food Chem Toxicol. 1996;34:829–867.</p> <p>[2] https://doi.org/10.5281/zenodo.1209313</p>
Novel Insights into the Bioactive Metabolites of Macrocybe gigantea (Massee) Pegler & Lodge, a Wild Edible Macrofungi Using Gas Chromatography Mass Spectrometry (GC-MS) Combined with Chemoinformatics Approaches
<p><em>Macrocybe gigantea </em>(MG) is an edible mushroom and has multiple pharmacological activities such as antibacterial, antioxidant, and antitumor activities. However, only a few reports were available on the bioactive compounds and bioactivity of this mushroom. In this concern, the present study was aimed to explore the unique chemical diversity from the fruiting body of MG<em>. </em>The species identification was done accurately with morphological and molecular methods followed by mycochemical extraction in different solvent systems. The ethanolic extract of the fruiting body gave maximum yield and its Gas Chromatography-Mass Spectrometry (GC-MS) analysis was performed along with antibacterial activity and cell viability by MTT assay. The GC-MS analysis revealed 50 metabolites and further chemoinformatics analysis of these metabolites revealed their possible biological activities. In addition, the mushrooms' physico-chemical and mineral element analysis revealed the quality and authenticity of the species. Altogether, the current investigation gives a comprehensive overview of the bioactive metabolites of MG.</p>
Programs for chemoinformatics and computational medicinal chemistry
<p>Programs provided herein are a part of a freely available database of annotated compound data sets and software tools developed in our laboratory for chemoinformatics and computational medicinal chemistry. The original release is described in the following article:<br /> <br /> Hu Y, Bajorath J: Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications [v1; ref status: indexed, http://f1000r.es/Mu9krs] <em>F1000Research</em> 2012; 1:11 (doi: 10.12688/f1000research.1-11.v1).<br /> <br /> The updated version containing the data provided herein will be described in a forthcoming data note in <em>F1000Research</em>.</p>
Data sets for chemoinformatics and computational medicinal chemistry
<p>Data sets provided herein are a part of a freely available database of annotated compound data sets and software tools developed in our laboratory for chemoinformatics and computational medicinal chemistry. The original release is described in the following article:<br /> <br /> Hu Y, Bajorath J: Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications [v1; ref status: indexed, http://f1000r.es/Mu9krs] <em>F1000Research </em>2012; 1:11 (doi: 10.12688/f1000research.1-11.v1).<br /> <br /> The updated version containing the data provided herein will be described in a forthcoming data note in <em>F1000Research</em>.</p>
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