Halogen Bond: Its Role beyond Drug-Target Binding Affinity for Drug Discovery and Development

被引:307
|
作者
Xu, Zhijian [1 ]
Yang, Zhuo [1 ]
Liu, Yingtao [1 ]
Lu, Yunxiang [2 ]
Chen, Kaixian [1 ]
Zhu, Weiliang [1 ]
机构
[1] Chinese Acad Sci, Drug Discovery & Design Ctr, Key Lab Receptor Res, State Key Lab Drug Res,Shanghai Inst Mat Med, Shanghai 201203, Peoples R China
[2] E China Univ Sci & Technol, Dept Chem, Shanghai 200237, Peoples R China
关键词
NONCOVALENT INTERACTIONS; DENSITY FUNCTIONALS; CRYSTAL-STRUCTURES; RATIONAL DESIGN; CHARGE-DENSITY; FORCE-FIELD; TRANSTHYRETIN; GEOMETRY; INHIBITORS; CHEMISTRY;
D O I
10.1021/ci400539q
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Halogen bond has attracted a great deal of attention in the past years for hit-to-lead-to-candidate optimization aiming at improving drug-target binding affinity. In general, heavy organohalogens (i.e., organochlorines, organobromines, and organoiodines) are capable of forming halogen bonds while organofluorines are not. In order to explore the possible roles that halogen bonds could play beyond improving binding affinity, we performed a detailed database survey and quantum chemistry calculation with close attention paid to (1) the change of the ratio of heavy organohalogens to organofluorines along the drug discovery and development process and (2) the halogen bonds between organohalogens and nonbiopolymers or nontarget biopolymers. Our database survey revealed that (1) an obviously increasing trend of the ratio of heavy organohalogens to organofluorines was observed along the drug discovery and development process, illustrating that more organofluorines are worn and eliminated than heavy organohalogens during the process, suggesting that heavy halogens with the capability of forming halogen bonds should have priority for lead optimization; and (2) more than 16% of the halogen bonds in PDB are formed between organohalogens and water, and nearly 20% of the halogen bonds are formed with the proteins that are involved in the ADME/T process. Our QM/MM calculations validated the contribution of the halogen bond to the binding between organohalogens and plasma transport proteins. Thus, halogen bonds could play roles not only in improving drug-target binding affinity but also in tuning ADME/T property. Therefore, we suggest that albeit halogenation is a valuable approach for improving ligand bioactivity, more attention should be paid in the future to the application of the halogen bond for ligand ADME/T property optimization.
引用
收藏
页码:69 / 78
页数:10
相关论文
共 50 条
  • [1] Affinity-Based Methods in Drug-Target Discovery
    Rylova, Gabriela
    Ozdian, Tomas
    Varanasi, Lakshman
    Soural, Miroslav
    Hlavac, Jan
    Holub, Dusan
    Dzubak, Petr
    Hajduch, Marian
    CURRENT DRUG TARGETS, 2015, 16 (01) : 60 - 76
  • [2] Drug-Target Kinetics in Drug Discovery
    Tonge, Peter J.
    ACS CHEMICAL NEUROSCIENCE, 2018, 9 (01): : 29 - 39
  • [3] DeepDTA: deep drug-target binding affinity prediction
    Ozturk, Hakime
    Ozgur, Arzucan
    Ozkirimli, Elif
    BIOINFORMATICS, 2018, 34 (17) : 821 - 829
  • [4] Drug discovery - Drug discovery: Affinity and beyond
    Janjic, N
    BIOPHARM-THE APPLIED TECHNOLOGIES OF BIOPHARMACEUTICAL DEVELOPMENT, 1998, 11 (02): : 28 - 28
  • [5] Drug-Target Association Kinetics in Drug Discovery
    IJzerman, Adriaan P.
    Guo, Dong
    TRENDS IN BIOCHEMICAL SCIENCES, 2019, 44 (10) : 861 - 871
  • [6] Explainable deep drug-target representations for binding affinity prediction
    Monteiro, Nelson R. C.
    Simoes, Carlos J. V.
    avila, Henrique V.
    Abbasi, Maryam
    Oliveira, Jose L.
    Arrais, Joel P.
    BMC BIOINFORMATICS, 2022, 23 (01)
  • [7] GANsDTA: Predicting Drug-Target Binding Affinity Using GANs
    Zhao, Lingling
    Wang, Junjie
    Pang, Long
    Liu, Yang
    Zhang, Jun
    FRONTIERS IN GENETICS, 2020, 10
  • [8] ImageDTA: A Simple Model for Drug-Target Binding Affinity Prediction
    Han, Li
    Kang, Ling
    Guo, Quan
    ACS OMEGA, 2024, 9 (26): : 28485 - 28493
  • [9] Prediction of drug-target binding affinity based on deep learning models
    Zhang H.
    Liu X.
    Cheng W.
    Wang T.
    Chen Y.
    Computers in Biology and Medicine, 2024, 174
  • [10] AttentionDTA: prediction of drug-target binding affinity using attention model
    Zhao, Qichang
    Xiao, Fen
    Yang, Mengyun
    Li, Yaohang
    Wang, Jianxin
    2019 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2019, : 64 - 69