Symmetry of van der Waals molecular shape and melting points of organic compounds

2004 ◽  
Vol 28 (8) ◽  
pp. 967 ◽  
Author(s):  
Yuri L. Slovokhotov ◽  
Ivan S. Neretin ◽  
Judith A. K. Howard
Keyword(s):  
Organic Compounds ◽  
Van Der Waals ◽  
Molecular Shape ◽  
Melting Points

Estimation of Melting Points of Organic Compounds-II

2006 ◽  
Vol 95 (12) ◽  
pp. 2562-2618 ◽  
Author(s):  
Akash Jain ◽  
Samuel H. Yalkowsky
Keyword(s):  
Organic Compounds ◽  
Melting Points

A method for estimating the boiling points of organic compounds from their melting points

Chemosphere ◽  
1995 ◽  
Vol 31 (4) ◽  
pp. 3001-3008 ◽  
Author(s):  
Andrea E. Walters ◽  
Paul B. Myrdal ◽  
Samuel H. Yalkowsky
Keyword(s):  
Organic Compounds ◽  
Melting Points ◽  
Boiling Points

Molecular Shape Descriptors. 1. Three-Dimensional Molecular Shape Descriptor

1985 ◽  
Vol 40 (11) ◽  
pp. 1108-1113 ◽  
Author(s):  
I. Motoc ◽  
G. R. Marshall ◽  
R. A. Dammkoehler ◽  
J. Labanowski
Keyword(s):  
Numerical Integration ◽  
Van Der Waals ◽  
Three Dimensional ◽  
Shape Descriptor ◽  
Molecular Shape ◽  
Shape Descriptors ◽  
Van Der Waals Volume

The paper presents and illustrates a method which uses numerical integration of the van der Waals envelope(s) to calculate with desired accuracy the molecular van der Waals volume and the three-dimensional molecular shape descriptor defined as the twin-number [OV(α, β); NOV(β, α), where OV and NOV represent the overlapping and, respectively, the nonoverlapping van der Waals volumes of the molecules α and ß superimposed according to appropriate criteria.

Effect of molecular shape on rejection of uncharged organic compounds by nanofiltration membranes and on calculated pore radii

2010 ◽  
Vol 358 (1-2) ◽  
pp. 101-113 ◽  
Author(s):  
Yoshiaki Kiso ◽  
Kentaro Muroshige ◽  
Tatsuo Oguchi ◽  
Toshiro Yamada ◽  
Masahiko Hhirose ◽  
...  
Keyword(s):  
Organic Compounds ◽  
Molecular Shape ◽  
Nanofiltration Membranes

QSAR Analysis of Multimodal Antidepressants Vortioxetine Analogs Using Physicochemical Descriptors and MLR Modeling

2019 ◽  
Vol 15 (4) ◽  
pp. 294-307
Author(s):  
David M. Rajathei ◽  
Subbiah Parthasarathy ◽  
Samuel Selvaraj
Keyword(s):  
Information Content ◽  
Van Der Waals ◽  
Antidepressant Drug ◽  
Antidepressant Activity ◽  
Molecular Shape ◽  
Van Der Waals Volume ◽  
Qsar Analysis ◽  
Qsar Models ◽  
Van Der Waals Surface ◽  
Selection Operator

Background: Vortioxetine is a multimodal antidepressant drug with combined effects on SERT as an inhibitor, 5-HT1A as agonist and 5-HT3A as an antagonist. Series of vortioxetine analogs have been reported as multi antidepressant compounds and they block serotonin transport into the neuronal cells, activate the postsynaptic 5-HT1A receptors and eliminate the low activity of 5-HT3A receptors. Objective: To explore the important properties of vortioxetine analogs involved in antidepressant activity by developing 2D QSAR models. Methods: Selections of significant descriptors were performed by Least Absolute Shrinkage and Selection Operator (LASSO) method and, the Multiple Linear Regression (MLR) method and All Subsets and GA algorithm included in QSARINS software were used for generating QSAR models. Further, the virtual screening was performed based on bioactivity and structure similarity using the PubChem database. Results: The four descriptor model of complementary information content (CIC2), solubility (bcutp3), mass (bcutm8) and partial charge in van der Waals surface area (PEOEVSA7) of the molecules is obtained for SERT inhibition with the significant statistics of R2= 0.69, RMSEtr= 0.44, R2 ext= 0.62 and CCCext= 0.78. For 5-HT1A agonist, the two descriptor model of molecular shape (Kappm3) and van der Waals volume of the atoms (bcutv11) with R2= 0.78, RMSEtr= 0.33, R2 ext = 0.83, and CCCext= 0.87 is established. The three descriptor model of information content (IC3), solubility (bcutp9) and electronegativity (GATSe5) of the molecules with R2= 0.61, RMSEtr= 0.34, R2 ext= 0.69 and CCCext= 0.72 is obtained for 5-HT3A antagonist. The antidepressant activities of 16 virtual screened compounds were predicted using the developed models. Conclusion: The developed QSAR models may be useful to predict antidepressant activity for the newly synthesized vortioxetine analogs.

Prediction of Melting Points of Organic Compounds Using Extreme Learning Machines

2008 ◽  
Vol 47 (3) ◽  
pp. 920-925 ◽  
Author(s):  
Akshay U. Bhat ◽  
Shamel S. Merchant ◽  
Sunil S. Bhagwat
Keyword(s):  
Organic Compounds ◽  
Extreme Learning Machines ◽  
Melting Points ◽  
Learning Machines
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