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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Related Experiment Video

Updated: Jan 5, 2026

High Content Screening in Neurodegenerative Diseases

High Content Screening in Neurodegenerative Diseases

Published on: January 6, 2012

DeepScreening: a deep learning-based screening web server for accelerating drug discovery.

Zhihong Liu¹, Jiewen Du², Jiansong Fang³

¹State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, 100 Xianlie Middle Road, Guangzhou 510070, China.

Database : the Journal of Biological Databases and Curation

|October 15, 2019

Summary

This summary is machine-generated.

We developed DeepScreening, a user-friendly web server that uses deep learning for faster drug discovery. This tool aids biologists and chemists in virtual screening and generating novel drug libraries for specific targets.

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Last Updated: Jan 5, 2026

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Area of Science:

Computational chemistry
Drug discovery
Bioinformatics

Background:

Deep learning shows promise in accelerating drug discovery, particularly in virtual screening.
Current applications are limited by data accessibility and the lack of user-friendly tools.

Purpose of the Study:

To develop a user-friendly web server integrating advanced deep learning algorithms for virtual screening.
To enable biologists and chemists to construct deep learning models and generate target-focused chemical libraries.

Main Methods:

Developed DeepScreening, a web server utilizing state-of-the-art deep learning algorithms.
Integrated options for using public or user-provided datasets for model training.
Enabled generation of de novo chemical libraries and virtual screening against diverse libraries.

Main Results:

DeepScreening allows for convenient construction of deep learning classification and regression models.
The platform facilitates virtual screening against both generated de novo libraries and existing chemical libraries.
All tasks, from model training to library generation and screening, are integrated within the web server.

Conclusions:

DeepScreening provides a comprehensive, user-friendly platform for deep learning-based drug and chemical probe discovery.
The tool is expected to benefit biologists and chemists by streamlining the virtual screening process.
This approach has the potential to accelerate the identification of novel therapeutic agents.