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Structure-Property Based Model Estimation of Alkanes Boiling Points

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CONTRIBUTORS:
  Author Sorana-Daniela BOLBOACA (Iuliu Hatieganu University of Medicine and Pharmacy Cluj-Napoca)
  Author Lorentz JÄNTSCHI (Technical University of Cluj-Napoca)
CONFERENCE NAME:
  Fourth International Conference of Applied Mathematics and Computing
CONF. LOCATION: Plovdiv, Bulgaria
CONFERENCE YEAR: 2007
PUB TYPE: Conference Presentation
SUBJECT(S): chemistry - computational; chemistry - organic; chemistry - physical; informatics - fundamentals; mathematics - applied; mathematics - modeling; mathematics - statistics
DISCIPLINE: Mathematics
HTTP: http://lori.academicdirect.org/works/?f=145
LANGUAGE: English
PUB ID: 103-436-930 (Last edited on 2007/08/20 01:38:39 GMT-6)
SPONSOR(S):
 
ABSTRACT:
This study discusses a family of molecular descriptors on structure-property relationships (MDF SPR) to model the boiling points of alkanes based on their chemical structure.
The proposed approach uses the complex information obtained from the all alkanes from C3 to C9 structures in order to generate and calculate the molecular descriptors family. The structure-property relationship models were built based on the generated descriptors.
The obtained models (model with one and two descriptors, respectively) were validated through the assessment of the cross-validation leave-one-out score. The comparison between the uni-varied model and the model with two descriptors was performed using Steiger's Z test. The best performing MDF SPR model was validated, and its correlation coefficient was compared with a previously reported model.
The analysis of the statistical characteristics of the obtained models demonstrated that the model with two descriptors has greater abilities in estimation and prediction compared with the model with one descriptor. This observation was also sustained by the results of training versus test analysis.
The results of this study revealed that the MDF SPR approach is a useful method to model the boiling points of alkanes providing stable models.
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