Ashok M. Khandekar1*, Sagar J. Kanase1, Atul S. Gurav1, Yogesh N. Gavhane1,A. V. Yadav1
1.Government College of Pharmacy, Karad 415124(M.S.) India.
Drug discovery process is a critical issue in the pharmaceutical industry since it is a very cost-effective and time consuming process to produce new drug potentials and enlarge the scope of diseases incurred. Drug target identification, being the first phase in drug discovery is becoming an overly time consuming process. In many cases, it produces inefficient results due to failure of conventional approaches like in vitro and in vivo to investigate large scale data. Sophisticated in silico approaches has given a tremendous opportunity to pharmaceutical companies to identify new potential drug targets which in turn affect the success and time of performing clinical trials for discovering new drug targets. Insilico pharmacology includes databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer.Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The main goal of this work is to review in silico methods for drug discovery process with emphasis on identifying drug targets, where there are genes or proteins associated with specific diseases.
Key words:-target identification, in vitro& in vivo, data bases, pharmacophores, data mining, etc.