Document Type

Article

Publication Date

2016

Publication Title

International Journal of TROPICAL DISEASE & Health

Volume

15

Issue

2

First Page

1

Last Page

17

Abstract

Aims: To present the molecular structures of compounds that has been shown to inhibit the proliferation of Ebola virus. To elucidate the molecular properties of these virus inhibiting compounds.

Study Design: The molecular properties of virus inhibiting compounds are elucidated and compiled. Pattern recognition methods and statistical analysis are applied to determine optimal properties of this group of compounds.

Place and Duration of Study: Chemistry Department, Durham Science Center, University of Nebraska, Omaha NE. between December 2015 and February 2016.

Methodology: A total of 60 compounds were identified as inhibiting the virus Ebola. The molecular properties such as Log P, molecular weight, and 7 other descriptors were elucidated utilizing heuristic methods. Structures are compared by applying classification methods with statistical tests to determine trends, underlying relationships, and pattern recognition.

Results: For 60 compounds identified the averages determined: for Log P (3.51), polar surface area (89.45 Angstroms2), molecular weight (432.6), molecular volume (393.96 Angstroms3), and number of rotatable bonds (7). Molecular weight showed a strong positive correlation to number of oxygen and nitrogen atoms, number of rotatable bonds, and molecular volume. K-means clustering indicated seven clusters divided according to highest similarity of members in the cluster. Ranges found: formula weights (157.1 to 822.94), Log P (-2.24 to 8.93), polar surface area (6.48 to 267.04 A2), and number of atoms (11 to 58). Multiple regression analysis produced an algorithm to predict similar compounds.

Conclusion: The formula weights and Log P values of Ebola virus inhibitors show a broad range in numerical values. Consistency in properties was identified by statistical analysis with grouping for similarity by K-means pattern recognition. Multiple regression analysis enables prediction of similar compounds as drug candidates. Only 29 compounds showed zero violations of rule of 5, an indication of favorable drug-likeness. These compounds are highly varied in structures and properties.

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