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Related Experiment Video

Updated: Aug 30, 2025

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

DenovoProfiling: A webserver for de novo generated molecule library profiling.

Zhihong Liu^1,2, Jiewen Du³, Ziying Lin²

¹School of Public Health, Xinxiang Medical University, Xinxiang, China.

Computational and Structural Biotechnology Journal

|August 26, 2022

Summary

DenovoProfiling is a new webserver for visualizing and functionally profiling novel drug molecules generated using deep learning. It aids in selecting candidates for synthesis and biological testing by providing comprehensive analysis.

Keywords:

DDR1, Discovered potent discoidin domain receptor 1 De novo drug design De novo molecule library Deep learning FBDD, Fragment-based drug design FDR, False discovery rate GAN, Generative adversarial networks HTS, High throughput screening LSTM, Long short-term memory Library profiling PCA, Principal components analysis RNN, Recurrent neural networks SCA, Scaffold-based classification approach VAE, Variational autoencoders

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Published on: February 18, 2022

Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

Deep learning architectures are increasingly used for *de novo* drug design.
A growing need exists for visualizing and functionally profiling these generated molecules.
Public chemogenomic databases offer opportunities for comprehensive library profiling.

Purpose of the Study:

To present DenovoProfiling, a webserver for visualizing and functionally profiling *de novo* molecular libraries.
To provide tools for structural identification, chemical space exploration, and target/pathway analysis.

Main Methods:

Development of a webserver with six integrated modules.
Modules include structure identification, chemical space analysis (PCA, clustering), ADMET prediction, 3D molecular alignment, drug mapping, and target/pathway identification.
Utilizes publicly available chemogenomic databases.

Main Results:

DenovoProfiling offers comprehensive analysis of *de novo* molecular libraries.
Provides structural identification, chemical space exploration, drug similarity mapping, and target/pathway information.
Facilitates clear understanding and guided selection of candidate molecules.

Conclusions:

DenovoProfiling serves as a valuable resource for researchers in *de novo* drug design.
The webserver aids in the selection of promising drug candidates for synthesis and biological validation.
It enhances the drug discovery pipeline by providing integrated visualization and profiling tools.