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3 results for “RDKit”
ΔG-RDKit: Solvation Free Energy Database
<p>We present the full database of the article "Explainable Supervised Machine Learning Model to Predict Solvation Free Energy".</p> <p>This is the database used for a ML model, containing a variety of solvent-solute pairs with known experimental solvation free energy Δ<em>G</em><sub>solv</sub> values. Data entries were collected from two separate databases. The <a href="https://link.springer.com/article/10.1007/s10822-014-9747-x">FreeSolv</a> library, with 642 experimental aqueous Δ<em>G</em><sub>solv </sub>determinations and the <a href="https://mediatum.ub.tum.de/1452571?v=1">Solv@TUM</a> database with 5597 entries for non-aqueous solvents. Both databases were selected given their wide-scale of solute/solvents pairs, amassing 6239 experimental values across light and heavy-atom solutes with a diverse solvent structure and with small value uncertainties.</p> <p>Experimental Δ<em>G</em><sub>solv</sub> values range from -14 to 4 kcal mol<sup>-1</sup> and each solute/solvent pair is represented by their chemical family, SMILES string and InChlKey. We generated 213 chemical descriptors for every solvent and solute in each entry using <a href="http://http://www.rdkit.org/">RDKit</a> software, version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and Δ<em>G</em><sub>solv</sub>.</p> <p>Through Machine Learning regression algorithms, our models were able to make Δ<em>G</em><sub>solv</sub> predictions with high accuracy, based on the information encoded in each chemical feature.</p>
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>
ΔvapHm-RDKit: Standard Vaporization Enthalpy Database
<p>We present a database to Predict Standard Vaporization Enthalpies through Intelligible Features.</p><p>This database presents a variety of molecules of diverse chemical families with known experimental Standard Vaporization Enthalpy (Δvap<i>H</i>m°) values. Entries were collected from the <a href="https://webbook.nist.gov/chemistry/">NIST WebBook Library</a> through a web scraping routine, considering only Δvap<i>H</i>m° experimental values, ranging from 15 kJ mol-1 to 400 kJ mol-1. Experimental value scrapping was automatically made, without duplicates, by selecting the first listed value (either a from single or average of values as determined by NIST). The database includes each value's original literature, along with experimental uncertainties (when available). </p><p>Each compound is represented by its molecular family, SMILES string and respective InChlKey & InChl identifiers. The curated database consists of 1781 molecules over 15 different chemical families. The chemical descriptors used to feed our model were generated using <a href="http://www.rdkit.org/">RDKit</a> version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the "MolFromSmiles" function in "RDKIT.Chem" as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and Δvap<i>H</i>m° determinations.</p><p>Through Machine Learning regression algorithms, we have developed models that were able to make accurate Δvap<i>H</i>m° predictions, based on the information encoded in each chemical feature. Our models can be accessed through <a href="https://github.com/jfcaetano/EnthVapML">GitHub</a>.</p>
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