Using Supervised Learning and Comparing General and ANTI-HIV Drug Databases Using Chemoinformatics

  • Taneja Shweta
  • Raheja Shipra
  • Kaur Savneet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

Earlier People used to discover new drugs either by chance that is serendipity or by screening the natural products. This process was time consuming, costly as well as required a lot of investment in terms of man-hours. The process of discovering a new drug was very complex and had no rational. Prior to Data Mining , researchers were trying computer methods such as potential drug molecules interactions with the targets and that was also time consuming, costly and required high expertise. Data mining is often described as a discipline to find hidden information in a database. It involves different techniques and algorithms to discover useful knowledge lying hidden in the data. Data mining and the term Knowledge Discovery in Databases (KDD) are often used interchangeably . In this paper, we are implementing the classification technique using WEKA tool for the analysis of similarity between GENERAL DRUGS and ANTI-HIV DRUGS.

Keywords

Classification Chemoinformatics Data mining HIV drugs 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Taneja Shweta
    • 1
  • Raheja Shipra
    • 1
  • Kaur Savneet
    • 1
  1. 1.Guru Teg Bahadur Institute of TechnologyNew Delhi

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