In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences

Su-Qing Yang, Liu-Xia Zhang, You-Jin Ge, Jin-Wei Zhang, Jian-Xin Hu, Cheng-Ying Shen, Ai-Ping Lu, Ting-Jun Hou*, Dong-Sheng Cao*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)

Abstract

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing. Graphical Abstract: [Figure not available: see fulltext.]

Original languageEnglish
Article number48
Number of pages14
JournalJournal of Cheminformatics
Volume15
DOIs
Publication statusPublished - 23 Apr 2023

Scopus Subject Areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Computer Graphics and Computer-Aided Design
  • Library and Information Sciences

User-Defined Keywords

  • Target prediction
  • Chemogenomic
  • XGBoost
  • Ensemble model

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