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DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery

Authors :
Shen, Chao
Song, Jianfei
Hsieh, Chang-Yu
Cao, Dongsheng
Kang, Yu
Ye, Wenling
Wu, Zhenxing
Wang, Jike
Zhang, Odin
Zhang, Xujun
Zeng, Hao
Cai, Heng
Chen, Yu
Chen, Linkang
Luo, Hao
Zhao, Xinda
Jian, Tianye
Chen, Tong
Jiang, Dejun
Wang, Mingyang
Ye, Qing
Wu, Jialu
Du, Hongyan
Shi, Hui
Deng, Yafeng
Hou, Tingjun
Source :
Journal of Chemical Information and Modeling; July 2024, Vol. 64 Issue: 14 p5381-5391, 11p
Publication Year :
2024

Abstract

Artificial intelligence (AI)-aided drug design has demonstrated unprecedented effects on modern drug discovery, but there is still an urgent need for user-friendly interfaces that bridge the gap between these sophisticated tools and scientists, particularly those who are less computer savvy. Herein, we present DrugFlow, an AI-driven one-stop platform that offers a clean, convenient, and cloud-based interface to streamline early drug discovery workflows. By seamlessly integrating a range of innovative AI algorithms, covering molecular docking, quantitative structure–activity relationship modeling, molecular generation, ADMET (absorption, distribution, metabolism, excretion and toxicity) prediction, and virtual screening, DrugFlow can offer effective AI solutions for almost all crucial stages in early drug discovery, including hit identification and hit/lead optimization. We hope that the platform can provide sufficiently valuable guidance to aid real-word drug design and discovery. The platform is available at https://drugflow.com.

Details

Language :
English
ISSN :
15499596 and 1549960X
Volume :
64
Issue :
14
Database :
Supplemental Index
Journal :
Journal of Chemical Information and Modeling
Publication Type :
Periodical
Accession number :
ejs66729926
Full Text :
https://doi.org/10.1021/acs.jcim.4c00621