Name
Abstract | ||
|---|---|---|
Understanding the molecular mechanism that govern drug protein interactions is essential for efficient drug design. The progress in drug development is very slow compared to the rising variation in human genomes and the discovery of new diseases. Thus the need to have more efficient and effective drug discovery pipelines is becoming essential. In this paper, we study and analyze the relationships between drugs and proteins that they target. We consider some properties of the proteins that can be used to give weight to protein-protein relationships. We aim to identify protein properties that might guide the drug to proteins. Amino acid enrichment in target clusters is analyzed to assess if certain drugs prefer particular amino acids. The correlation between the net charge of the drug with the amino acids in the target protein is studies as well. Moreover, characterizing the functional components in drug target clusters is necessary to find drug preference. Sequence motifs and domains, post-translational modification and biological pathways of target proteins are analyzed to understand drug preference. This characterizes the drug target proteins from sequence and functional angles. Finally, we realized the importance of the social network model in analyzing any problem that can be modeled as a network. Fortunately, the problem tackled in this paper fits well the network requirement of the social network model. Hence, we analyze the correlations using the social network model where actors are drugs and proteins; our aim is to analyze the relations between drugs and proteins by benefiting from the rich metrics developed to analyze social networks. In this paper, we briefly mention the social network technique in order to demonstrate its applicability which will be detailed in a future publication. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3233/IDA-2011-0514 | Intell. Data Anal. |
Keywords | Field | DocType |
drug preference,effective drug discovery pipeline,drug target cluster,efficient drug design,drug development,drug protein interaction,certain drug,functional characterization,drug target protein,target protein,social network model,drug-protein interactions network | Drug discovery,Social network,Protein–protein interaction,Computer science,Drug development,Target protein,Drug target,Artificial intelligence,Computational biology,Bioinformatics,Drug,Machine learning | Journal |
Volume | Issue | ISSN |
16 | 1 | 1088-467X |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mona Okasha | 1 | 0 | 0.34 |
| Abdallah M. ElSkeikh | 2 | 0 | 0.34 |
| Mohammed Al-shalalfa | 3 | 124 | 10.26 |
| Ghada Naji | 4 | 3 | 1.41 |
| Reda Alhajj | 5 | 1919 | 205.67 |
| Jon G. Rokne | 6 | 263 | 45.63 |