For another analysis we calculated the Pearson coefficient of correlation between MP and descriptors. Considering that all models were validated using a fivefold cross validation approach, we were using up to 632=192 cores per one task simultaneously thus allowing fast processing of the data. The environment of atoms and bonds determine their type. SAR QSAR Environ Res 24:279318, Article Our current melting temperature database contains 9,375 materials, out of which 982 compounds are high-melting-temperature materials with melting points above 2,000 K. The database consists of chemical compositions (i.e., elements and concentrations) or equivalently chemical formula, of the materials, and their . A list of pharmaceutical API and excipients with their melting point. This result was expected since both properties describe different physical effects. This result was due to the absence of molecules with MP <0C in the PATENTS set. It should be noted that the calculation of large models requires significant CPU resources. Providing categorized protein sequences and structures as psychrophilic, mesophilic and thermophilic makes this database useful for the development of new tools in protein stability prediction. Wiley-VCH, Weinheim, p 667, Gasteiger J (2006) Of molecules and humans. Properties grade reagent grade Quality Level 200 Assay 98% form crystalline powder powder Four other MP data sets were used to validate the models developed in this work. All of these uncertainties decreased the accuracy of MP measurements. i 5). The estimation of logS with an error of <0.5 log units is on the level of the experimental measurement accuracy [75] and thus is very valuable for the pharma industry. The automated data extraction from PATENTS resulted in a number of systematic errors in the data, which needed to be cleaned and filtered. In the PATENTS dataset the average MP value was 155C, which can be probably used as a better estimation of MP for drug-like compounds. The two best descriptors calculated by Adriana.CODE [28] 2DACorr_PiEN_3 and 2DACorr_PiEN_4 are 2D electronegativity-weighted autocorrelation descriptors calculated for topological distances 3 and 4 [69]. It can be proposed as a single descriptor model for the estimation of MP of compounds. CompilationoftheMeltingPoints OftheMetalOxides DISCARDED3Y M.5/D.A U.S.DEPARTMENTOFCOMMERCE NATIONALBUREAUOFSTANDARDS It will be helpful for characterization and purity evaluation of a molecule and also for solubility prediction of a drug candidate. The propensity of these three groups to decompose is well known. The support of a sparse data format is efficiently realized in LibSVM making this method easily applicable to this type of data. The values were obtained from the CRC (87th edition), or Vogel's Practical Organic Chemistry (5th ed.). FIG. OCHEM software also supports a sparse data format thus making it possible to fully utilize the power of the LibSVM method. Our database will be updated periodically. Wiley, Hoboken, pp 241275, Dearden JC, Rotureau P, Fayet G (2013) QSPR prediction of physico-chemical properties for REACH. Thus, for this SNR the removal of seven outlying points will also remove one good data point. J Comput Aided Mol Des 26:135136. Moreover, descriptors which were inter-correlated with a linear correlation coefficient of R2>0.95 were grouped together and only one descriptor from the group was selected for model development. J Chem Inf Comput Sci 42:11361145, Tetko IV (2012) The perspectives of computational chemistry modeling. For each molecule we selected one record, which had MP near to the median experimental value for it. New Database Crystal Structure Database, for more information visit https://icsd.nist.gov NIST produces the Nation's Standard Reference Data (SRD). https://doi.org/10.1186/s13321-016-0113-y, DOI: https://doi.org/10.1186/s13321-016-0113-y. absorption, distribution, metabolism, exertion and toxicity, on-line chemical database and modeling environment, http://ochem.eu, quantitative structure activity relationship, quantitative structure property relationship, United States Patent and Trademark Office, Tetko IV (2007) Prediction of physicochemical properties. We also used a combined dataset (COMBINED) composed of the OCHEM, Enamine, Bradley and Bergstrm sets to simplify analysis of performances for several studies. Download NIST Simulation of Electron Spectra for Surface Analysisat no cost. >500C. p=0.001, indicated a bimodal distribution of their MPs with peaks at 60 and 280C, i.e. These data are assessed by experts and are trustworthy such that people can use the data with confidence and base significant decisions on the data. However, the training of large datasets requires significant computational resources and can take a long time. 2023 BioMed Central Ltd unless otherwise stated. Significant amounts of MP data are freely available within the patent literature and, if it were available in the appropriate form, could potentially be used to develop predictive models. This model, however, can hardly be considered as an improvement of the null hypothesis model for any practical application. Cookies policy. The outlying molecules were filtered using p=0.01. ToxAlert [38] extended functional groups (EFG) [39] included 583 groups covering different functional features of molecules. When using 1% of a randomly selected training data set we found that, surprisingly, the same parameters (C=64, =1, =0.00391) were optimal for 10 out of 13 descriptor sets. To find compounds by entering various criteria use the Therefore, after initial analysis LibSVM was used to develop all models using radial basis function (RBF) kernel. statement and It is possible that selection of SVM parameters for each set could contribute better models. a comma instead of a dot) were addressed by introducing rules to handle these non-standard forms. 2.1: Melting Point Analysis - Chemistry LibreTexts https://figshare.com/articles/Melting_Point_and_Pyrolysis_Point_Data_for_Tens_of_Thousands_of_Chemicals/2007426 (9 Dec 2015), Creative Commons. This descriptor is defined in a [0, 1] interval and it is equal to 1 if a molecule does not have side chains. 236, respectively, which contributed noise to the MP values in the high or low temperature region. J Chem Inf Model 46:24122422, Jain A, Yalkowsky SH (2006) Estimation of melting points of organic compounds-II. CAS This result confirms that consensus averaging is a powerful method to increase the accuracy of individual models. The consensus model provided an improvement, RMSE=1C, for the prediction of molecules outside of the drug-like space for the COMBINED set thus confirming the aforementioned conclusions about the influence of the outlier filtering on the data quality for molecules with this range of MP values. A .gov website belongs to an official government organization in the United States. These results indicate the separation of molecules into two classes, i.e. RIVM report 601200003. Anal Chim Acta 544:292305, Balakin KV, Savchuk NP, Tetko IV (2006) In silico approaches to prediction of aqueous and DMSO solubility of drug-like compounds: trends, problems and solutions. Thus, the accuracy of prediction of MP for the high temperature region was limited by the accuracy of experimental data. Standard Reference Data | NIST It is always possible to contact the web administrator (first author of the manuscript) to increase this limit for some specific projects. 63.3 (0.5) C. Thus, the COMBINED set has about the same percentage of decomposing compounds as the PATENTS set. Chemical Database Online The important result of this analysis was that the averaging of few models with the highest prediction ability could improve results compared to the averaging of all models. liquids) or with very high MPs, e.g. J Chem Inf Model 52:23102316, Salmina E, Haider N, Tetko IV (2016) Extended functional groups (EFG): an efficient set for chemical characterization and structure-activity relationship studies of chemical compounds. NIST provides 49 free SRD databases and 41 fee-based SRD databases. We showed that the estimated accuracy varied as a function of temperature and achieved the lowest error of =32C for the drug-like region of the dataset. Each point averaged at least 50 measurements. The Enamine set also did not have compounds with MP <0C and a model based on this set failed to predict the whole Bradley set despite the fact that it had excellent prediction ability for its drug like subset (Table3). Open Melting Point Data Thirteen thousand experimental melting points for slightly over eight thousand chemical structures. Values expressed as measurement errors were converted to ranges and all temperatures were converted to degrees Celsius. In order to better evaluate it we developed SVM models using the descriptors selected with MLRA for the PATENTS set for five descriptor sets contributing to the consensus model. The difference between the average numbers of predicted compounds for COMBINED and PATENTS sets was about 6%, that is the percentage of decomposing compounds annotated in the PATENTS literature. A screenshot of the melting temperature database I have built so far. A comparison of MLRA and SVM results developed using exactly the same sets of descriptors indicated significantly higher accuracy of the SVM models. Up to 150,000 new structures including their physical and chemical properties and synthesis J Chem Inf Model 47:11111122, Nigsch F, Bender A, van Buuren B, Tissen J, Nigsch E, Mitchell JB (2006) Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization. Thus enlargement of the training set increased prediction power of the models according to the CV protocol. The MLRA model developed with both these descriptors MP=117+0.142MW0.79nC achieved an RMSE=64.7C. The implementation of ASNN did not offer this feature. In our previous work [11] we found that ASNN [44] and SVM [45] methods provided significantly higher accuracy of MP predictions compared to other tested methods while the accuracy of models developed with both methods was similar. Other errors, which could easily be corrected by a human, e.g. also including the outlying molecules, which were excluded for different p values. The final number of records is shown in Table1. The outlying compounds were therefore again enriched with decomposing compounds. (PDF) Physical Properties of Ionic Liquids: Database and Evaluation A large number of MP measurements were duplicated across different PATENTS. Success in this area of research critically depends on the availability of high quality MP data as well as accurate chemical structure representations in order to . http://www.chemosophia.com (5 Aug 2015), Potemkin VA, Grishina MA, Bartashevich EV (2007) Modeling of drug molecule orientation within a receptor cavity in the BiS algorithm framework. As in our previous study [11] the model developed using E-stateindices calculated the lowest RMSE for the training set and provided one of the best results for the four validation sets (see Additional file 2: Table S1, Table3). By repeating the model building five times one can calculate predictions for all molecules from the initial dataset. The accuracy of the consensus model developed using the PATENTS dataset was low for the whole Bradley set despite it having a low RMSE for the drug-like subset of this set. Unsaturation and saturation indexes Ui (R=0.349) and Uc (R=0.325) were the two most highly correlated molecular property descriptors calculated by the Dragon software. The compounds from the Bergstrm dataset had the second largest MPs. The median MP for decomposing compounds was 210C as compared to 155C for the whole dataset (Table1). Indeed, we built a linear MLRA using the best 100 and ten descriptors. graph 68 presents the melting points of 70 metal oxides as given in the literature published up to January 1963. Int J Pharm 373:2440, Varnek A, Kireeva N, Tetko IV, Baskin II, Solovev VP (2007) Exhaustive QSPR studies of a large diverse set of ionic liquids: how accurately can we predict melting points? PDF Finding chemical and physical property data - University of Bath We therefore expect that better models should be calculated by considering each property independently. Such study, however, is beyond the scope of this article. Chemical Database - ChemSynthesis As with the analysis of the whole set of compounds the best accuracy of the individual model was calculated using E-state descriptors (RMSE=43.2C). This model in its design (a simple consensus average of ten individual models) was the best match to the model developed in our previous study thus allowing their straightforward comparison. Last but not least, important structural features related to the pyrolysis of chemicals were identified, and a model to predict whether a compound will decompose instead of melting was developed. In total 498,985 associations were found in patent grants and 172,886 associations were found in the patent applications. E-state indices are 2D descriptors that combine the electronic character and topological environment of each skeletal atom and bond. LibGuides: Chemical Engineering: Open Access Databases [68] The same change decreases the number of rings as well as the number of atoms in the largest -chain (relative to the overall size of the molecule) as well as other electronic parameters of the molecule. 29 Dexterity. The Bradley dataset, which was composed of many general chemical industry compounds, had the smallest average MW and MP values. the decimal point after 236 was missed). A binned plot of the accuracy as a function of the MP temperature indicates that measurements with higher and lower temperatures were less reproducible (Fig. Melting Points mp C source 32.00 Alfa Aesar 31.50 PHYSPROP 1-(chloromethyl)-2-nitrobenzene C7H6ClNO22, 3, 61, 64 Compound Data Melting point 48.75 C 321.90 K CSID 11427 Melting Points Melting . A number of data points (see Table1) from the PATENTS collection contained annotation about the thermal decomposition (pyrolysis) of chemical structures. The limitation on the number of molecules per task is also useful to prevent possible challenges from inexperienced users who can initiate very large calculations by mistake. Both of these descriptors had R=0.359. The OCHEM web site was developed with the idea of delivering full reproducibility of modeling efforts. Heat capacity. Because of the limitation on the computational resources, the grid search to select SVM parameters was done using only one set of descriptors, EFG, which contained the smallest number of non zero values. The same grammar is also used to generate a parser for identifying the different parts of a MP declaration. The development and public availability of computational models developed with an increasing volume of publicly available data mined from the published literature is important to the development of better QSAR/QSPR models and their wider acceptance by academia, industry and chemical authorities [80]. A simple average of models. Thus, the exclusion of outlying molecules, which distorted the training procedures, contributed models with higher prediction accuracies. Search by chemical names Systematic names Synonyms Trade names Database identifiers Search by chemical structure Create structure-based queries The analysis of the most correlated descriptors indicates that many of them are strongly related to the -system of electrons and thus had the possibility to interact through -interactions. The Bradley dataset [24] contains doubly curated data collected by Open Notebook science community members. Part 2. Google Scholar, Manahan SE (2003) Toxicological chemistry and biochemistry, 3rd edn. The CV RMSE for a subset of molecules that decomposed was 47.7C, i.e. In 2005, this table was adapted by Dr. Brian J. Myers, Webmaster of ACS Division of Organic Division (DOC) from: Professor Murov's Organic solvent table. As an example, a non-registered and validated registered user can submit models with up to 1000 and 10,000 molecules per task, respectively. Problems such as difficulties with experimental measurements for high temperatures, errors with reporting these values (i.e. It is interesting that similar to our previous study [11] the removal of outlying compounds practically did not affect the performance of the consensus models for the drug-like subsets. It is interesting that the experimental accuracy depended on the MP value. SRD must be compliant with rigorous critical evaluation criteria. The MP is used as a parameter for several models, e.g. J Cheminform 8, 2 (2016). The degree of improvement depended on the descriptors used. The selection of samples is repeated for each developed model used in the bagging protocol. Datasets: For Machine Learning and Searching Experiments We have developed a pipeline for the automated extraction and annotation of chemical data from published PATENTS. The LibSVM supports parallelization, which can be easily enabled by editing a few lines of code and linking the code with appropriate libraries. Detailed information on the descriptors can be found on the Talete website (http://www.talete.mi.it/). csid corresponds to Chemspider ID. Many of these challenges were related to the ambiguous representation of information within chemical PATENTS. The database currently contains 9375 materials, out of which 982 compounds are high-melting-temperature materials with melting points above 2000K. They are based on counts of MOL2 atom types around each heavy atom of the molecule and enumerate all atom environments present in a molecule. Online catalog of biochemicals & reagents for organic synthesis is also available. following pages. Google Scholar, Ran Y, Yalkowsky SH (2001) Prediction of drug solubility by the general solubility equation (GSE). Some of the problems with collected values were difficult to recognize and eliminate. The configuration includes options for data standardization, descriptor calculation and pre-processing as well as the parameters for the configuration of the machine learning methods, e.g. Molecules 15:50795092, Rogers D, Hahn M (2010) Extended-connectivity fingerprints. While this error was higher than the RMSE of 0.62 calculated for the data in the original study the results obtained in this study did not use any information about the target property. Melting points that could be automatically detected as being likely to be incorrect were flagged in the SDF. The associations between molecules and melting/decomposition/sublimation points were serialized to SDF format [23] (Fig. Search Engineering Material by Property Value - MatWeb The presence of decomposing compounds in the training set of the non-decomposing subset for the development of the pyrolysis classification model could decrease its accuracy. Following data upload to OCHEM we performed modeling and reviewed outlier molecules. Since development of models with E-state counts was faster, the counts were used. This decrease in the accuracy of predictions for this region is qualitatively similar for all five analyzed datasets and is in agreement with the decrease of the experimental accuracy of MP data as estimated for the PATENTS set. 75C, 200F, one hundred degrees Celsius. NIST Atomic Spectra Database - Ground states and ionization energies (on physics web site) Computational Chemistry Comparison and Benchmark Database; Gas Phase Kinetics Database; X-ray Photoelectron Spectroscopy Database, version 4.1; . their synthesis references and physical properties such as melting point, boiling point and density. Example of two entries from the resultant SDF. The model developed with the PATENTS dataset predicted them with RMSEs of 32.2 and 33.9C, respectively. E-state [31] refers to electro-topological state indices that are based on chemical graph theory. While some inorganic compounds are solids with accessible melting points, and some are liquids with reasonable boiling points, there are not the exhaustive tabulations of melting/boiling point data for inorganic compounds that exist for organics. J Pharm Sci 95:25622618, Bergstrom CA, Norinder U, Luthman K, Artursson P (2003) Molecular descriptors influencing melting point and their role in classification of solid drugs. The MW and the number of carbon atoms (NC) had significant linear Pearson correlation coefficients, R=0.172 and R=0.136 respectively, relative to MP. The patent-mined data from this study are publicly downloadable from the same web site as well as available from FigShare [70] under a CC-BY license [71]. Where can I find reliable data for melting points of organic compounds? The combination of PATENTS and COMBINED sets decreased the RMSE by 0.6C for the drug-like subset of the COMBINED set as well as also for the four individual subsets from the previous study. Ignited IV: Convert 50% of damage dealt to Fire. GSFrag and GSFrag-L [33] are used to calculate 2D descriptors representing fragments of length \({\text{k}} = 2 \ldots 10\) or \({\text{k}} = 2 \ldots 7,\) respectively. To simplify further handling, the textual and SGML formats were converted to an equivalent XML representation using a LeadMine [21] library function. The graph was built using N=18,058 differences in the MP temperatures and was rescaled to match the average experimental accuracy of =35C. The main interest in this property is because of its possible use for the estimation of the solubility of chemical compounds using the general solubility equation (GSE) [19]. This may be done using other forms of analysis, such as gas chromatography-mass spectroscopy coupled with a database. This can limit the application of GSE to new chemicals. The workflow for extracting compound/MP associations is summarized in Fig. -162.89 (0.05) C; b.p. For example, the distribution of MP values from PATENTS literature had peaks at 250 and 350C thus indicating that measurements were either stopped at these temperatures and threshold values were reported or simply that at these temperatures an estimated value within a fairly broad range was entered (i.e. Further branching, such as with the isomer . A locked padlock The modeling of these properties is best facilitated by obtaining large, structurally diverse, high-quality datasets. The first approach was averaging by model accuracy. These 2D descriptors are calculated with the help of the ISIDA fragmenter tool [32]. J Struct Chem 48:155160, Sushko I, Salmina E, Potemkin VA, Poda G, Tetko IV (2012) ToxAlerts: a web server of structural alerts for toxic chemicals and compounds with potential adverse reactions. The improvement in model performance for the whole COMBINED set was larger compared to the results calculated for the drug-like subsets. Lock A consensus model based on the average of five models calculated the lowest RMSE=42.3C. RMSE of LibSVM models calculated with different sets of descriptors. This approach contributed highly predictive models, as reported in the previous studies [11, 25, 26, 5153], including Rank-I submission models [52, 53] for the ToxCast challenges organized by EPA and NIH. All of these descriptor types are implemented within the OCHEM platform [29]. http://usefulchem.blogspot.com/2011/03/open-modeling-of-melting-point-data.html(5 Aug 2015), Jean-Claude Bradley Open Melting Point Dataset. The aggregation and curation of such datasets can be very exacting in terms of extraction of the data from the literature. using threshold or typing errors), as well as polymorphism and the purity of analyzed chemical compounds likely contribute to the measurement error. In this approach each model is built using 4/5 of the compounds from the initial training set. For example, one of the obvious erroneously reported values was Mp. After finding and correcting a common pattern, which was leading to errors, data extraction was repeated. As mentioned in the methods section a lot of efforts were devoted to cleaning up the data set during extraction from the literature. Because melting point depression is unique between chemicals, a mixed melting curve comparing molar fractions of the two constituents with melting point needs to either be obtained or prepared (Figure \(\PageIndex{4}\)). To learn more about this project please go to the "About us" page. In the second approach a consensus model was developed using the predictions of individual models as descriptors for a multiple linear regression model (MLRA). In the stratified bagging approach the molecules of the smallest class are selected using sampling with replacement to form a set of the same size as the class is.
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