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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Experimental Pharmacology and Drug Discovery
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1491699
This article is part of the Research Topic Advancing Drug Discovery with AI: Drug-Target Interactions, Mechanisms of Action, and Screening View all articles

De Novo Design of mIDH1 Inhibitors by Integrating Deep Learning and Molecular Modeling

Provisionally accepted
Dingkang Sun Dingkang Sun 1*Lulu Xu Lulu Xu 2*Mengfan Tong Mengfan Tong 3*Weitong Zhang Weitong Zhang 1*Zhao Wei Zhao Wei 4Jialong Liang Jialong Liang 5*Yuwei Wang Yuwei Wang 1*Xueying Liu Xueying Liu 4*
  • 1 College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
  • 2 General Hospital of Xinjiang Military Comand, Xinjiang, China
  • 3 State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Xi’an, China
  • 4 Department of Medicinal Chemistry, School of Pharmacy, Air Force Medical University, Xi'an, China
  • 5 No.946 Hospital, Yining 835000,Xinjiang Uygur Autonomous Regions, Xinjiang, China

The final, formatted version of the article will be published soon.

    Mutations in the IDH1 gene have been shown to be an important driver in the development of acute myeloid leukemia (AML), gliomas and certain solid tumors, which are a promising target for cancer therapy. The aim of this study was to generate candidate compounds with potential activity and good drug-likeness using the bidirectional recurrent neural network (BRNN) model and scaffold hopping, followed by virtual screening and molecular dynamics simulations. The BRNN model ultimately generated 3890 new compounds, while scaffold hopping finally generated 3680 new compounds. The molecules generated by both approaches were evaluated by PCA, QED, SA analysis and molecular docking, which was found that the molecules generated by the BRNN model had superior molecular diversity, druggability, synthesizability and docking scores. Therefore, 3890 new compounds generated by BRNN model were screened using glide-based virtual screening. Ultimately, 10 structurally diverse compounds were retained, all of which showed the potential to become candidate drugs in terms of ADME properties. Molecular dynamics simulations of 6 small molecules with better scores than positive compounds showed that the RMSD of the four systems of M1 and M2, M3 and M6 remained stable, and had local flexibility and compactness similar to the positive drugs. Finally, the free energy decomposition results showed that compound M1 exhibited the best binding properties in all energy aspects and was the best candidate molecule among the 6 compounds. This study is the first attempt to use deep learning to design mIDH1 inhibitors, which provides theoretical guidance for the design of mIDH1 inhibitors.

    Keywords: mIDH1, BRNN model, Scaffold hopping, Virtual Screening, molecular dynamics

    Received: 05 Sep 2024; Accepted: 10 Oct 2024.

    Copyright: © 2024 Sun, Xu, Tong, Zhang, Wei, Liang, Wang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Dingkang Sun, College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
    Lulu Xu, General Hospital of Xinjiang Military Comand, Xinjiang, China
    Mengfan Tong, State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Xi’an, China
    Weitong Zhang, College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
    Jialong Liang, No.946 Hospital, Yining 835000,Xinjiang Uygur Autonomous Regions, Xinjiang, China
    Yuwei Wang, College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi Province, China
    Xueying Liu, Department of Medicinal Chemistry, School of Pharmacy, Air Force Medical University, Xi'an, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.