MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery.

Journal: Journal of chemical information and modeling
Published Date:

Abstract

Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimization, and compound targeting, as well as abundant analyzing tools to check the results. MolProphet is an accessible and user-friendly web-based platform that is fully designed according to the practices in the drug discovery industry. The molecule screened, generated, or optimized by the MolProphet is purchasable and synthesizable at low cost but with good drug-likeness. More than 400 users from industry and academia have used MolProphet in their work. We hope this platform can provide a powerful solution to assist each normal researcher in drug design and related research areas. It is available for everyone at https://www.molprophet.com/.

Authors

  • Keda Yang
    Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310015, P. R. China.
  • Zewen Xie
    Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China.
  • Zhen Li
    PepsiCo R&D, Valhalla, NY, United States.
  • Xiaoliang Qian
    School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
  • Nannan Sun
    Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China.
  • Tao He
  • Zuodong Xu
    Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China.
  • Jing Jiang
    Department of Critical Care Medicine, Chongqing General Hospital, Chongqing, China.
  • Qi Mei
    Department of Thoracic Oncology, Cancer Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030002, China; Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. Electronic address: qimei@tjh.tjmu.edu.cn.
  • Jie Wang
  • Shugang Qu
    Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China.
  • Xiaoling Xu
    Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310015, P. R. China.
  • Chaoxiang Chen
    Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310015, P. R. China.
  • Bin Ju
    Hangzhou Wowjoy Information Technology Co., Ltd, Hangzhou, China. bin.ju@wowjoy.cn.