Modern drug discovery is time-consuming and expensive, involving coordinated multi-disciplinary research in multiple stages, each requiring intensive and specialized resources . Although rapid advancement of “omics” approaches, computational systems biology and accumulation of digital data resources have provided a vast array of significant information in life science , data relevant to drug discovery are not easily identified and recruited for application to pharmaceutical research . Despite the technological advances in drug discovery such as HTS, the approval of new drugs has remained stagnant in the past decade, resulting in an overall decline in the productivity of` the pharmaceutical industry.
In efforts to save development time and minimize the risk of failure during drug develo pment, repositioning of currently available drugs to new therapeutic indications is considered an alternative route . To date most repositioned drugs have been the consequence of serendipitous observations of unexpected efficacy and side effects of drugs in development or on the market. However, recently, systems biology approaches have been applied in efforts to discover unknown effects for existing drugs. For instance, drug repositioning approaches have incorporated in silico approaches for analyzing large data sets such as gene expression profiles [4,5], literature mining , chemical similarity , side-effect similarity , disease-drug network , pathway-based disease network , and phenotypic disease network . To establish a more logical approach to repositioning a known drug to a new indication, we established a knowledge platform comprising binary linkages between diseases, drugs, and proteins, from which new and previously unknown connections can be drawn between drugs and diseases of interest. This integrated database was designated PharmDB.
For probing the database and identifying disease-drug linkages, we have developed the Shared Neighborhood Scoring (SNS) algorithm, which predicts relationships between drugs, proteins and diseases. While the relationship data are collected from experiments, coverage of the data is still incomplete. Thus there may be undetected links and hidden nodes in the network. Up to now, a number of prediction methods and measures have been proposed to find these undetected associations from topological or structural properties of various complex networks [12,13]. To date, most of these algorithms and measures are applicable only to a monopartite network that consists only of one type of node. Therefore, multipartite network composed of more than a type of nodes cannot be analyzed using these measures. To solve this problem, researchers have used projection methods that convert multipartite networks into monopartite ones. Unfortunately, any projection method can result in information loss, especially in low-degree nodes. Accordingly projecting the PharmDB tripartite network into monopartite drug, protein and disease networks can distort many well-known network measures, such as average path length < l>, average clustering coefficient < C>, degree-dependent clustering coefficient C(k), degree distribution P(k), assortativity coefficient r , and degree-degree correlation coefficient knn(k) . To overcome these limits of the projection technique, we designed a new prediction method called Shared Neighborhood Scoring (SNS) algorithm which calculates the probability of a link existence between two nodes of interest. This can be done by evaluating the connections of their neighbors in PharmDB tripartite network.