作者
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,Luonan 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
摘要
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.