Computational Drug Repositioning: A Lateral Approach to Traditional Drug Discovery?

Abstract

Computational drug repositioning is popular in academia and pharmaceutical industry globally. The repositioning hypotheses, generated using a variety of computational methods, can be quickly tested experimentally. Several success stories have emerged in the past decade or so. Newer concepts and methods such as drug profile matching are being tried to address the limitations of current computational repositioning methods. The trend is shifting from earlier small-scale to large-scale or global-scale repositioning applications. Other related approaches such as prediction of molecular targets for novel molecules, prediction of side-effect profiles of new molecular entities (NMEs), etc., are applied routinely. The current article focuses on state-of-the-art of computational drug repositioning field with the help of relevant examples and case studies. This 'lateral' approach has significant potential to bring down the time and cost of the awfully expensive drug discovery research and clinical development. The persistence and perseverance in the successful application of these methods is likely to be paid off in near future.