A Combination Method of the Tanimoto Coefficient and Proximity Measure of Random Forest for Compound Activity Prediction

Search this Article

Author(s)

Abstract

Chemical and biological activities of compounds provide valuable information for discovering new drugs. The compound fingerpring that is represented by structural information of the activities is used for candidates for investigating similarity. However, there are several problems with predicting accuracy from the requirement in the compound structural similarity. Although the amount of compound data is growing rapidly, the number of well-annotated compounds, e.g., those in the MDL Drug Data Report (MDDR) database, has not increased quickly. Since the compounds that are known to have some activities of a biological class of the target are rare in the drug discovery process, the accuracy of the prediction should be increased as the activity decreases or the false positive rate should be maintained in databases that have a large number of un-annotated compounds and a small number of annotated compounds of the biologiccal activity. In this paper, we propose a new similarity scoring method composed of a combination of the Tanimoto coefficient and the proximity measure of random forest. The score contains two propertied that are derived from unsupervised and supervised methods of partial dependence for compounds. Thus, the proposed method is expected to indicate compoinds that have accurate activities. By evalyating the performance of the prediction compared with the two scores of the Tanimoto coefficient and the proximitiy measure, we demonstrate that the prediction result of the proposed scoring method is better than those of the two method by using the Linear Discriminant Analysis (LDA) method. We estimate the predicition accuracy of compound datasets extracted from MDDR using the proposed method. It is also shown that the proposed method can identify active compounds in datasets including several un-annotated compounds.

Journal

  • 情報処理学会論文誌. バイオ情報学

    情報処理学会論文誌. バイオ情報学 49(4), 46-57, 2008-03-15

    Information Processing Society of Japan (IPSJ)

References:  33

Codes

  • NII Article ID (NAID)
    110006684909
  • NII NACSIS-CAT ID (NCID)
    AA12177013
  • Text Lang
    ENG
  • Article Type
    ART
  • ISSN
    03875806
  • NDL Article ID
    9421193
  • NDL Call No.
    Z74-C192
  • Data Source
    CJP  NDL  NII-ELS 
Page Top