Scalable prediction of compound-protein interactions using minwise hashing

Yasuo Tabei, Yoshihiro Yamanishi

研究成果: ジャーナルへの寄稿学術誌査読

35 被引用数 (Scopus)

抄録

The identification of compound-protein interactions plays key roles in the drug development toward discovery of new drug leads and new therapeutic protein targets. There is therefore a strong incentive to develop new efficient methods for predicting compound-protein interactions on a genome-wide scale. In this paper we develop a novel chemogenomic method to make a scalable prediction of compound-protein interactions from heterogeneous biological data using minwise hashing. The proposed method mainly consists of two steps: 1) construction of new compact fingerprints for compound-protein pairs by an improved minwise hashing algorithm, and 2) application of a sparsity-induced classifier to the compact fingerprints. We test the proposed method on its ability to make a large-scale prediction of compound-protein interactions from compound substructure fingerprints and protein domain fingerprints, and show superior performance of the proposed method compared with the previous chemogenomic methods in terms of prediction accuracy, computational efficiency, and interpretability of the predictive model. All the previously developed methods are not computationally feasible for the full dataset consisting of about 200 millions of compound-protein pairs. The proposed method is expected to be useful for virtual screening of a huge number of compounds against many protein targets.

本文言語英語
論文番号S3
ジャーナルBMC systems biology
7
DOI
出版ステータス出版済み - 2013

!!!All Science Journal Classification (ASJC) codes

  • 構造生物学
  • モデリングとシミュレーション
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 応用数学

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