Artificial intelligence methods in kinase target profiling: Advances and challenges

Shukai Gu, Huanxiang Liu, Liwei Liu, Tingjun Hou, Yu Kang

Research output: Contribution to journalReview articlepeer-review

Abstract

Kinases have a crucial role in regulating almost the full range of cellular processes, making them essential targets for therapeutic interventions against various diseases. Accurate kinase-profiling prediction is vital for addressing the selectivity/specificity challenges in kinase drug discovery, which is closely related to lead optimization, drug repurposing, and the understanding of potential drug side effects. In this review, we provide an overview of the latest advancements in machine learning (ML)-based and deep learning (DL)-based quantitative structure–activity relationship (QSAR) models for kinase profiling. We highlight current trends in this rapidly evolving field and discuss the existing challenges and future directions regarding experimental data set construction and model architecture design. Our aim is to offer practical insights and guidance for the development and utilization of these approaches.

Original languageEnglish
Article number103796
JournalDrug Discovery Today
Volume28
Issue number11
DOIs
Publication statusPublished - Nov 2023

Keywords

  • QSAR
  • deep learning
  • graph neutral network
  • kinase profiling
  • machine learning

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