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

Authors :
Shen C
Song J
Hsieh CY
Cao D
Kang Y
Ye W
Wu Z
Wang J
Zhang O
Zhang X
Zeng H
Cai H
Chen Y
Chen L
Luo H
Zhao X
Jian T
Chen T
Jiang D
Wang M
Ye Q
Wu J
Du H
Shi H
Deng Y
Hou T
Source :
Journal of chemical information and modeling [J Chem Inf Model] 2024 Jul 22; Vol. 64 (14), pp. 5381-5391. Date of Electronic Publication: 2024 Jun 26.
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 :
1549-960X
Volume :
64
Issue :
14
Database :
MEDLINE
Journal :
Journal of chemical information and modeling
Publication Type :
Academic Journal
Accession number :
38920405
Full Text :
https://doi.org/10.1021/acs.jcim.4c00621