RESEARCH ARTICLE


The Effect of Adding Indirect Relationship to Turbo Similarity Searching



Nurul H. A. Hassain Malima, *, Yong Pei-Chiaa, Marwah H. Al-Lailaa, b, Shereena M. Arif c
a School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia
b University of Mosul, Mosul, Iraq
c University Kebangsaan Malaysia, Selangor, Malaysia


Article Metrics

CrossRef Citations:
0
Total Statistics:

Full-Text HTML Views: 1047
Abstract HTML Views: 604
PDF Downloads: 202
ePub Downloads: 92
Total Views/Downloads: 1945
Unique Statistics:

Full-Text HTML Views: 415
Abstract HTML Views: 326
PDF Downloads: 127
ePub Downloads: 71
Total Views/Downloads: 939



© Malim et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the School of Computer Sciences, University Sains Malaysia, 11800, Penang, Malaysia; Tel/Fax: +604-6534645; E-mail: nurulhashimah@usm.my


Abstract

Background:

Turbo Similarity Searching (TSS) has been proved as one of the effective and simple searching method in Cheminformatics. Emerging from the conventional similarity searching, TSS depended on the concept of fusion where relationship between the target being sought and the compound in the database are indirect. Previous works has looked at only one level of indirect relationship and indicates that there are further potential that more levels of such relationship be added to TSS to increase its ability to recover more actives. Hence, in this work, we aimed to investigate the impact of the indirect relationship on TSS.

Method:

This study has further investigated the enhancement of TSS using additional layers of indirect relationship and fusion process. We implemented TSS by adding another layer of fusion between the target and database compound.

Results:

The experiments with MDDR database showed that the proposed new strategy described in this paper provide a way of enhancing the effectiveness of the TSS process in chemical databases. The experiments also showed that the increases in performance are particularly better when the sought actives are structurally diverse.

Conclusion:

We may conclude that the additional layers do increase the recall of TSS. Hence, the new TSS strategy could be used as an alternative to the old TSS.

Keywords: Chemoinformatics, MDL Drug Data Report, Nearest Neighbors, Similarity Searching, Turbo Similarity Searching, Virtual Screening.