DenovoProfiling: A Webserver for Generated Molecule Library Profiling

Overview

Journal Comput Struct Biotechnol J

Specialty Biotechnology

Date 2022 Aug 26

PMID 36016718

Authors

Zhihong Liu

Jiewen Du

Ziying Lin

Ze Li

Bingdong Liu

Zongbin Cui

Jiansong Fang

Liwei Xie

Affiliations

Soon will be listed here.

Abstract

Various deep learning-based architectures for molecular generation have been proposed for drug design. The flourish of the molecular generation methods and applications has created a great demand for the visualization and functional profiling for the generated molecules. An increasing number of publicly available chemogenomic databases sets good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization module for chemical structure visualization and identify the reported structures, (2) chemical space module for chemical space exploration using similarity maps, principal components analysis (PCA), drug-like properties distribution, and scaffold-based clustering, (3) ADMET prediction module for predicting the ADMET properties of the molecules, (4) molecular alignment module for three dimensional molecular shape analysis, (5) drugs mapping module for identifying structural similar drugs, and (6) target & pathway module for identifying the reported targets and corresponding functional pathways. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their library and could guide the further selection of candidates for chemical synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.

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