Kenkyu Journal of AIDS & Clinical Trials
  • Rahul H

    Department of Molecular Virology, National AIDS Research Institute (ICMR), Pune, India, e-mail: rahulhajare@rediffmail.com

Received: 09-10-2016

Accepted: 01-11-2016

Published: 07-11-2016

Citation: Rahul H (2016) pre-DR Technology. J Aids Trails 2: 100106

Copyrights: © 2016 Rahul H


pre DR technology (predict drug repositioning) has free flow knowledge circulation has wide by virtue can produce certain drug discovery, here best things to use the available resources sparingly and recycle them. Excitement of pre DR technology has pick drugs that failed in their proposed activities and bounced back and that technology comes far more than drug discovery. It has inconsistent drug development and single max variable result of pharmaceuticals drugs with thousands of combinations and searches against these technologies.

Keywords: HIV; AIDS


Most scrutinized literature was collected from different sources including PubMed. This database has been curetted using unpublished result for all most all diseases. pre DR a study was incorporated including these substances would, if present at insufficient level results, be detected by one or other of the test. They are limited by the general acceptance criteria for other/unspecified result and /or by the general medical use. It has therefore necessary to identify those drugs for demonstration of compliance. This has need of low investmentment pharmaceutical industry including PD, R&D, CRO and laboratory working in those areas. It has paly a great role for their desirable and negative balance approach in drug discovery and mechanism in flow of information those who couldn’t sensor anybody. This structure unknowingly alter limited point and its architecture carry information some body patented, somebody trades mark to cut some information has not harmful to subject and it has right but not copying and extend certain level. Here, we propose a new method for cost saving drug development, predict drug repositioning, to integrate molecular structure, molecular activity, and phenotype data. Specifically characterize drug by profiling in chemical structure, target protein (HIV protein), and side-effects space, and define a kernel function to correlate drugs with diseases.


pre Drug repositioning has ability to think conventionally and act innovative way about the old drug new disease paradigm. It has consistent effort make to choose available alternatives the one that has proper and possible to carry out and to undertake action aims to find new diseases to cure for existing drugs database and thus offers the possibility for faster, safer, and cheaper drug development. Given the huge search space and the rapid accumulation of drug related data at molecular level, computational approaches are highly desired to narrow down the gap between medical indications and elucidation of drug effects [1]. In addition to their low cost and time-efficiency predictions, computational methods have the advantage in understanding the mechanisms of drug actions. Drug takes effect via its protein targets in cell to cure disease. Thus, many previous studies in computational drug repositioning focused on the drugs with known downstream target proteins in disease-specific molecular networks [2-4]. However, low-through- put data limits the applications in small scale. Recent accumulated high-throughput data for both drugs and diseases provide possibilities to uncover novel statistical associations between drugs and diseases in a large-scale manner. Many methods have been developed in this direction, including: (i) conduction (ii) convection (iii) vibration, novel associations among drugs and diseases by the Guilt and Association approaches. This method heuristically summarized multiple drug-drug and disease-disease unsimilarity measurement from various aspects and repositioning was done based on the observation that unsimilar drugs tend to treat specific diseases. The authors reporting high accuracy and act (AUC = 0.9). This approach applied logistic regression to integrate multiple drug-drug and disease- disease similarity metrics to collect the evidence for a strong association. This scheme provides a technocrats learning framework, and there was still much room to improve both from more general data collecting and specific predicting. In this paper construct a universal predictor for Drug Repositioning (pre DR) to dissect drug-disease associations in a large-scale manner and notice the rapid development of high- throughput technologies.


In conclusion new method, pre DR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. It has existential drug discovery to meet the right drug at the right time at right cost to represents a single physical molecule of the compound.


Portions of this research and development were supported while I was a post-doctoral fellow at National AIDS Research Institute Pune.


  1. Bajorath J (2002) Integration of virtual and high-throughput screening. Nat Rev Drug Discov. 1:882-894. 

  2. Walters WP, Stahl MT, Murcko MA (1998) Virtual screening-an overview. Drug Discov. Today. 3:160-178.

  3. Langer T, Hoffmann RD (2001) Virtual screening: an effective tool for lead structure discovery? Curr Pharm Des.7:509-527. 

  4. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 3:935-949.


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