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Related Experiment Video

Updated: Apr 30, 2026

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
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Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

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DeepKinomeWeb: a quantitative, panel-level platform for kinase inhibitor screening and selectivity profiling.

Jisu Eun1, Yeeun Lee2, Seunghoon Yang1,3

  • 1Bio-design Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Korea.

Nucleic Acids Research
|April 29, 2026
PubMed
Summary

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This summary is machine-generated.

DeepKinomeWeb is a new platform that helps researchers discover kinase inhibitors. It uses deep learning to analyze screening data, making inhibitor selection faster and more efficient for drug discovery.

Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Protein kinases are crucial drug targets, but developing selective inhibitors is difficult and expensive.
  • High-throughput screening data is complex and hard to interpret for early-stage drug development.

Purpose of the Study:

  • To present DeepKinomeWeb, a web platform for analyzing kinase inhibitor screening data.
  • To enable quantitative prediction of kinase-inhibitor binding and assess inhibitor selectivity.

Main Methods:

  • Developed DeepKinomeWeb based on the validated DeepKinome deep learning regression model.
  • Integrated competition-based screening data analysis with visualization of selectivity landscapes.
  • Incorporated structural and physicochemical analyses for comprehensive assessment.

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Last Updated: Apr 30, 2026

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Identification of Kinase-substrate Pairs Using High Throughput Screening
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Main Results:

  • DeepKinomeWeb transforms screening data into actionable insights for inhibitor prioritization.
  • The platform provides quantitative binding affinity predictions and selectivity metrics.
  • Offers panel-level visualization of selectivity landscapes.

Conclusions:

  • DeepKinomeWeb facilitates rational, data-driven decision-making in kinase inhibitor discovery.
  • Lowers the barrier for systematic selectivity assessment, aiding biologists and medicinal chemists.
  • Provides a user-friendly interface for efficient drug discovery processes.