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

IF 5.6 2区化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-06-26 DOI:10.1021/acs.jcim.4c00621

Chao Shen, Jianfei Song, Chang-Yu Hsieh, Dongsheng Cao, Yu Kang, Wenling Ye, Zhenxing Wu, Jike Wang, Odin Zhang, Xujun Zhang, Hao Zeng, Heng Cai, Yu Chen, Linkang Chen, Hao Luo, Xinda Zhao, Tianye Jian, Tong Chen, Dejun Jiang, Mingyang Wang, Qing Ye, Jialu Wu, Hongyan Du, Hui Shi, Yafeng Deng, Tingjun Hou

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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.

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DrugFlow：人工智能驱动的创新药物发现一站式平台。

人工智能（AI）辅助药物设计对现代药物发现产生了前所未有的影响，但目前仍迫切需要用户友好型界面，以弥合这些复杂工具与科学家之间的差距，尤其是那些不太精通计算机的科学家。在此，我们介绍了人工智能驱动的一站式平台DrugFlow，它提供了一个简洁、方便、基于云的界面，可简化早期药物发现工作流程。通过无缝集成一系列创新的人工智能算法，包括分子对接、定量结构-活性关系建模、分子生成、ADMET（吸收、分布、代谢、排泄和毒性）预测和虚拟筛选，DrugFlow 可以为早期药物发现的几乎所有关键阶段提供有效的人工智能解决方案，包括新药发现和新药/先导物优化。我们希望该平台能提供足够有价值的指导，帮助实际药物设计和发现。该平台可在 https://drugflow.com 上获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Chemical Information and Modeling 化学-化学综合

CiteScore

9.80

自引率

10.70%

发文量

529

审稿时长

1.4 months

期刊介绍： The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.