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Updated: Jun 23, 2025

Modeling an Enzyme Active Site using Molecular Visualization Freeware

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Published on: December 25, 2021

Image-based molecular representation learning for drug development: a survey.

Yue Li¹, Bingyan Liu², Jinyan Deng¹

¹Division of Gastroenterology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No. 5 Haiyun Warehouse, 100700, Beijing, China.

Briefings in Bioinformatics

|June 26, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) accelerates drug development by leveraging molecular images. This survey explores image-based AI approaches, offering insights into their applications and future potential.

Keywords:

Computer vision Drug development Image-based Molecule representation

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Area of Science:

Computer Vision
Drug Discovery
Artificial Intelligence

Background:

Traditional drug development methods are time-consuming and expensive.
Existing surveys on AI in drug discovery often overlook computer vision.
Molecular images offer a unique and intuitive representation for AI-driven analysis.

Purpose of the Study:

To provide the first comprehensive survey on image-based molecular representation for AI-powered drug development.
To categorize existing research based on computer vision learning paradigms.
To highlight the impact and potential of visual molecular data in accelerating drug discovery.

Main Methods:

Systematic review of research papers focusing on image-based molecular representation.
Development of a taxonomy based on computer vision learning paradigms.
Analysis of applications, limitations, and future research directions.

Main Results:

Identified and categorized numerous studies utilizing molecular images for AI drug development.
Demonstrated the effectiveness of image-based AI in overcoming limitations of traditional methods.
Highlighted key contributions of visual molecular representations to drug discovery.

Conclusions:

Image-based molecular representation is a promising and intuitive approach for AI in drug development.
Further research is needed to explore the full potential and address current limitations.
This survey provides valuable insights for researchers in the field.