Correlation between Structure and Normal Boiling Points of Haloalkanes C1-C4 Using Neural Networks

Balaban, Alexandrù T.; Basak, Subhash C.; Colburn, Timothy R.; Grunwald, Gregory D.

doi:10.1021/ci00021a016

Cited by 55 publications

(51 citation statements)

References 2 publications

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“…[8][9][10][11][12][13][14][15][16][17][18][19] Although the above approach has proved useful in many applications, it has a number of limitations. 11,17,[19][20][21][22] The quantitative relationships between structure and physicochemical properties can be complex and highly nonlinear; thus, determining the optimal analytical form of the QSPR model presents a challenge. Moreover, regression analysis becomes complex and less reliable as the number of descriptors increases.…”

Section: Introductionmentioning

confidence: 99%

“…8,11,21,22 As a consequence, most recent studies have explored the development of QSPRs for commonly available physicochemical parameters (e.g., boiling point, heat capacity, density, refractive index) for selected organic compound classes for which accurate and rich data sets are available. 8,17,[19][20][21][22] The use of boiling point data to test the applicability of various molecular descriptors has been particularly popular given the availability of data for large sets of organic chemical classes. QSPRs for boiling points have been proposed by a number of investigators based on backpropagation neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…QSPRs for boiling points have been proposed by a number of investigators based on backpropagation neural networks. 8,17,[20][21][22] For example, Gakh et al 19 developed a composite neural network/QSPR boiling points model, based on 134 noncyclic alkanes ranging from 6 to 10 carbon atoms. Their model, which included five additional physicochemical properties, was based on six Weiner-type structural graph invariants representing the number of pathways for carbon lengths ranging from three to eight atoms and also included the number of carbon atoms.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Based Quantitative Structural Property Relations (QSPRs) for Predicting Boiling Points of Aliphatic Hydrocarbons

Espinosa

Yaffe

Cohen

et al. 2000

J. Chem. Inf. Comput. Sci.

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Quantitative structural property relations (QSPRs) for boiling points of aliphatic hydrocarbons were derived using a back-propagation neural network and a modified Fuzzy ARTMAP architecture. With the backpropagation model, the selected molecular descriptors were capable of distinguishing between diastereomers. The QSPRs were obtained from four valance molecular connectivity indices ( 1 v , 2 v , 3 v , 4 v ), a second-order Kappa shape index ( 2 κ), dipole moment, and molecular weight. The inclusion of dipole moment proved to be particularly useful for distinguishing between cis and trans isomers. A back-propagation 7-4-1 architecture predicted boiling points for the test, validation, and overall data sets of alkanes with average absolute errors of 0.37% (1.65 K), 0.42% (1.73 K), and 0.37% (1.54 K), respectively. The error for the test and overall data sets decreased to 0.19% (0.81 K) and 0.31% (1.30 K), respectively, using the modified Fuzzy ARTMAP network. A back-propagation alkene model, with a 7-10-1 architecture, yielded predictions with average absolute errors for the test, validation, and overall data sets of 1.96% (6.79 K), 1.83% (6.45 K), and 1.25% (4.42 K), respectively. Fuzzy ARTMAP reduced the errors for the test and overall data sets to 0.19% (0.73 K) and 0.25% (0.95 K), respectively. The back-propagation composite model for aliphatic hydrocarbons, with a 7-9-1 architecture, yielded boiling points with average absolute errors for the test, validation, and overall set of 1.74% (6.09 K), 1.25% (4.68 K), and 1.37% (4.85 K), respectively. The error for the test and overall data sets using the Fuzzy ARTMAP composite model decreased to 0.84% (1.15 K) and 0.35% (1.35 K), respectively. Performance of the QSPRs, developed from a simple set of molecular descriptors, displayed accuracy well within the range of expected experimental errors and of better accuracy than other regression analysis and neural network-based boiling points QSPRs previously reported in the literature.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network Based Quantitative Structural Property Relations (QSPRs) for Predicting Boiling Points of Aliphatic Hydrocarbons

Espinosa

Yaffe

Cohen

et al. 2000

J. Chem. Inf. Comput. Sci.

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show abstract

“…The volatility of a molecule can be assessed by its boiling point, a property measured only for a small proportion of possible halogenated hydrocarbons. We studied a data set of 543 haloalkanes, whose boiling points were previously predicted by Multi Linear Regression (MLR) [41,42]. This regression required the computation of numerous molecular descriptors, including arithmetic descriptors, topological indices, geometrical indices, and counts of substructures and fragments.…”

Section: Predicting the Boiling Points Of Halogenated Hydrocarbonsmentioning

confidence: 99%

Computer-Aided Drug Design: An Innovative Tool for Modeling

Kore¹,

Mutha²,

Antre³

et al. 2012

OJMC

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Strategies for CADD vary depending on the extent of structural and other information available regarding the target (enzyme/receptor) and the ligands. Computer-aided drug design (CADD) is an exciting and diverse discipline where various aspects of applied and basic research merge and stimulate each other. In the early stage of a drug discovery process, researchers may be faced with little or no structure activity relationship (SAR) information. The process by which a new drug is brought to market stage is referred to by a number of names most commonly as the development chain or "pipeline" and consists of a number of distinct stages. To design a rational drug, we must firstly find out which proteins can be the drug targets in pathogenesis. In present review we reported a brief history of CADD, DNA as target, receptor theory, structure optimization, structure-based drug design, virtual high-throughput screening (vHTS), graph machines.

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“…The subset used in this study (469 chemicals) represents a chemically diverse set with experimental values for P vap ranging from 3 to 10,000 mmHg. 46,47 This set consisted of boiling point data for 276 chlorofluorocarbons (CFCs) with carbon skeletons containing one to four atoms. Nine of the compounds included in the studies by Balaban et al were removed as outliers, leaving a total of 267 CFCs.…”

Section: Normal Vapor Pressure (Log 10 P Vap )mentioning

confidence: 99%

Similarity methods in analog selection, property estimation and clustering of diverse chemicals

Basak¹,

Gute²,

Mills³

2006

Arkivoc

Self Cite

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This account summarizes Dr. Subhash Basak's work in the field of molecular similarity. In particular, it looks at the development and application of quantitative molecular similarity analysis (QMSA) techniques using physicochemical properties, topological indices, and atom pairs as descriptors for developing structure-or property-based similarity spaces and the use of a k-nearest neighbors (kNN) technique to estimate the properties or activities of chemicals within the space. Additionally, the account discusses the novel tailored similarity technique pioneered by Dr. Basak's research group and discusses the future of molecular similarity analysis techniques.

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Correlation between Structure and Normal Boiling Points of Haloalkanes C1-C4 Using Neural Networks

Cited by 55 publications

References 2 publications

Neural Network Based Quantitative Structural Property Relations (QSPRs) for Predicting Boiling Points of Aliphatic Hydrocarbons

Neural Network Based Quantitative Structural Property Relations (QSPRs) for Predicting Boiling Points of Aliphatic Hydrocarbons

Computer-Aided Drug Design: An Innovative Tool for Modeling

Similarity methods in analog selection, property estimation and clustering of diverse chemicals

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