Estimation of Physical Properties of Amino Acids by Group-Contribution Method

Spardha Virendra Jhamb, Xiaodong Liang, Rafiqul Gani*, Amol Shivajirao Hukkerikar

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

In this paper, we present group-contribution (GC) based property models for estimation of physical properties of amino acids using their molecular structural information. The physical properties modelled in this work are normal melting point (Tm), aqueous solubility (Ws), and octanol/water partition coefficient (Kow) of amino acids. The developed GC-models are based on the published GC-method by Marrero and Gani (J. Marrero, R. Gani, Fluid Phase Equilib. 2001, 183-184, 183-208) with inclusion of new structural parameters (groups and molecular weight of compounds). The main objective of introducing these new structural parameters in the GC-model is to provide additional structural information for amino acids having large and complex structures and thereby improve predictions of physical properties of amino acids. The group-contribution values were calculated by regression analysis using a data-set of 239 values for Tm, 211 values for Ws, and 335 values for Kow. Compared to other currently used GC-models, the developed models make significant improvements in accuracy with average absolute error of 10.8 K for Tm and logarithm-unit average absolute errors of 0.16 for Kow and 0.19 for Ws.
Original languageEnglish
JournalChemical Engineering Science
Volume175
Pages (from-to)148-161
ISSN0009-2509
DOIs
Publication statusPublished - 2018

Keywords

  • Normal melting point
  • Aqueous solubility
  • Octanol/water partition coefficient
  • Group contribution method
  • Amino acids

Cite this

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title = "Estimation of Physical Properties of Amino Acids by Group-Contribution Method",
abstract = "In this paper, we present group-contribution (GC) based property models for estimation of physical properties of amino acids using their molecular structural information. The physical properties modelled in this work are normal melting point (Tm), aqueous solubility (Ws), and octanol/water partition coefficient (Kow) of amino acids. The developed GC-models are based on the published GC-method by Marrero and Gani (J. Marrero, R. Gani, Fluid Phase Equilib. 2001, 183-184, 183-208) with inclusion of new structural parameters (groups and molecular weight of compounds). The main objective of introducing these new structural parameters in the GC-model is to provide additional structural information for amino acids having large and complex structures and thereby improve predictions of physical properties of amino acids. The group-contribution values were calculated by regression analysis using a data-set of 239 values for Tm, 211 values for Ws, and 335 values for Kow. Compared to other currently used GC-models, the developed models make significant improvements in accuracy with average absolute error of 10.8 K for Tm and logarithm-unit average absolute errors of 0.16 for Kow and 0.19 for Ws.",
keywords = "Normal melting point, Aqueous solubility, Octanol/water partition coefficient, Group contribution method, Amino acids",
author = "Jhamb, {Spardha Virendra} and Xiaodong Liang and Rafiqul Gani and Hukkerikar, {Amol Shivajirao}",
year = "2018",
doi = "10.1016/j.ces.2017.09.019",
language = "English",
volume = "175",
pages = "148--161",
journal = "Chemical Engineering Science",
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}

Estimation of Physical Properties of Amino Acids by Group-Contribution Method. / Jhamb, Spardha Virendra; Liang, Xiaodong; Gani, Rafiqul; Hukkerikar, Amol Shivajirao.

In: Chemical Engineering Science, Vol. 175, 2018, p. 148-161.

Research output: Contribution to journalJournal articleResearchpeer-review

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PY - 2018

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AB - In this paper, we present group-contribution (GC) based property models for estimation of physical properties of amino acids using their molecular structural information. The physical properties modelled in this work are normal melting point (Tm), aqueous solubility (Ws), and octanol/water partition coefficient (Kow) of amino acids. The developed GC-models are based on the published GC-method by Marrero and Gani (J. Marrero, R. Gani, Fluid Phase Equilib. 2001, 183-184, 183-208) with inclusion of new structural parameters (groups and molecular weight of compounds). The main objective of introducing these new structural parameters in the GC-model is to provide additional structural information for amino acids having large and complex structures and thereby improve predictions of physical properties of amino acids. The group-contribution values were calculated by regression analysis using a data-set of 239 values for Tm, 211 values for Ws, and 335 values for Kow. Compared to other currently used GC-models, the developed models make significant improvements in accuracy with average absolute error of 10.8 K for Tm and logarithm-unit average absolute errors of 0.16 for Kow and 0.19 for Ws.

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