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Updated: Jul 14, 2025

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

Artificial intelligence methods in kinase target profiling: Advances and challenges.

Shukai Gu¹, Huanxiang Liu², Liwei Liu³

¹College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.

Drug Discovery Today

|October 7, 2023

Summary

This summary is machine-generated.

Machine learning and deep learning models improve kinase profiling for drug discovery. These quantitative structure-activity relationship (QSAR) approaches aid in optimizing drugs and predicting side effects.

Keywords:

QSAR deep learning graph neutral network kinase profiling machine learning

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Published on: August 29, 2015

Area of Science:

Biochemistry and Molecular Biology
Computational Chemistry
Pharmacology

Background:

Kinases regulate cellular processes, making them key therapeutic targets.
Accurate kinase profiling is crucial for drug discovery, addressing selectivity and predicting side effects.

Purpose of the Study:

To review advancements in machine learning (ML) and deep learning (DL) based quantitative structure-activity relationship (QSAR) models for kinase profiling.
To highlight trends, challenges, and future directions in ML/DL for kinase profiling.

Main Methods:

Review of recent literature on ML and DL applications in kinase profiling.
Focus on quantitative structure-activity relationship (QSAR) models.

Main Results:

ML and DL-based QSAR models show significant progress in kinase profiling.
These models assist in lead optimization, drug repurposing, and understanding drug side effects.

Conclusions:

ML/DL-based QSAR models are powerful tools for kinase drug discovery.
Future work should focus on experimental data set construction and advanced model architectures.