Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors

被引:1
|
作者
Ghiandoni, Gian Marco [1 ,8 ]
Flanagan, Stuart R. [2 ]
Bodkin, Michael J. [2 ]
Nizi, Maria Giulia [4 ]
Galera-Prat, Albert [5 ,6 ]
Brai, Annalaura [7 ]
Chen, Beining [3 ]
Wallace, James E. A. [2 ]
Hristozov, Dimitar [2 ]
Webster, James [1 ]
Manfroni, Giuseppe [4 ]
Lehtio, Lari [5 ,6 ]
Tabarrini, Oriana [4 ]
Gillet, Valerie J. [1 ,8 ]
机构
[1] Univ Sheffield, Informat Sch, Sheffield, England
[2] Evotec UK Ltd, Abingdon, England
[3] Univ Sheffield, Dept Chem, Dainton Bldg,13 Brook Hill, Sheffield S3 7HF, England
[4] Univ Perugia, Dept Pharmaceut Sci, I-06123 Perugia, Italy
[5] Univ Oulu, Fac Biochem & Mol Med, FI-90014 Oulu, Finland
[6] Univ Oulu, Bioctr Oulu, Oulu, Finland
[7] Univ Siena, Dept Biotechnol Chem & Pharm, Siena, Italy
[8] Univ Sheffield, Informat Sch, Regent Court 211, Sheffield S1 4DP, England
基金
英国生物技术与生命科学研究理事会;
关键词
de novo drug design; PARP1; inhibitors; pharmaceuticals; reaction informatics; synthetic accessibility; SCREENING LIBRARIES; COMBINATION; PREDICTION; ALGORITHM; THERAPY; ASSAY;
D O I
10.1002/minf.202300183
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner. However, the use of a small number of hand-coded transformations restricts the chemical space that can be accessed and there are few examples in the literature where molecules and their synthetic routes have been designed and executed successfully. Here we describe the application of reaction-based de novo design to the design of synthetically accessible and biologically active compounds as proof-of-concept of our reaction vector-based software. Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. The results demonstrate that reaction vectors can be applied to the design of novel molecules of biological relevance that are also synthetically accessible. ** image
引用
收藏
页数:22
相关论文
共 50 条
  • [41] Enhancing reaction-based de novo design using a multi-label reaction class recommender
    Gian Marco Ghiandoni
    Michael J. Bodkin
    Beining Chen
    Dimitar Hristozov
    James E. A. Wallace
    James Webster
    Valerie J. Gillet
    Journal of Computer-Aided Molecular Design, 2020, 34 : 783 - 803
  • [42] Discovery of Biphenylacetamide-Derived Inhibitors of BACE1 Using de Novo Structure-Based Molecular Design
    Mok, N. Yi
    Chadwick, James
    Kellett, Katherine A. B.
    Casas-Arce, Eva
    Hooper, Nigel M.
    Johnson, A. Peter
    Fishwick, Cohn W. G.
    JOURNAL OF MEDICINAL CHEMISTRY, 2013, 56 (05) : 1843 - 1852
  • [43] Design, synthesis and biological evaluation of dual inhibitors targeting AR/ AR-Vs and PARP1 in castration resistant prostate cancer therapy
    Zhang, Si-Han
    Su, Yaowu
    Zheng, Mengzhu
    Zeng, Na
    Sun, Jian-Xuan
    Xu, Jin-Zhou
    Liu, Chen-Qian
    Wang, Shao-Gang
    Zhou, Yirong
    Xia, Qi-Dong
    BIOMEDICINE & PHARMACOTHERAPY, 2024, 180
  • [44] Discovery of Pyrazolo[1,5,4-de]quinoxalin-2(3H)-one Derivatives as Highly Potent and Selective PARP1 Inhibitors
    Gao, Shanyun
    Hou, Yingjie
    Xu, Yanxiao
    Li, Jingjing
    Zhang, Chaobo
    Jiang, Shujuan
    Yu, Songda
    Liu, Lei
    Li, Leping
    Tu, Wangyang
    Yu, Bing
    Zhang, Yixiang
    JOURNAL OF MEDICINAL CHEMISTRY, 2024, 67 (23) : 21380 - 21399
  • [45] Design, Synthesis, and Biological Evaluation of a Series of Benzo[de][1,7]naphthyridin-7(8H)-ones Bearing a Functionalized Longer Chain Appendage as Novel PARP1 Inhibitors
    Ye, Na
    Chen, Chuan-Huizi
    Chen, TianTian
    Song, Zilan
    He, Jin-Xue
    Huan, Xia-Juan
    Song, Shan-Shan
    Liu, Qiufeng
    Chen, Yi
    Ding, Jian
    Xu, Yechun
    Miao, Ze-Hong
    Zhang, Ao
    JOURNAL OF MEDICINAL CHEMISTRY, 2013, 56 (07) : 2885 - 2903
  • [46] Design of inhibitors of the MurF enzyme of Streptococcus pneumoniae using docking, 3D-QSAR, and de novo design
    Khedkar, Santosh A.
    Malde, Alpeshkurnar K.
    Coutinho, Evans C.
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2007, 47 (05) : 1839 - 1846
  • [47] Identification of potential cruzain inhibitors using de novo design, molecular docking and dynamics simulations studies
    Bhowmick, Shovonlal
    Chorge, Rekha Dhondiram
    Jangam, Chaitanya Sadashiv
    Bharatrao, Lomate Dhanraj
    Patil, Pritee Chunarkar
    Chikhale, Rupesh V.
    Islam, Md. Ataul
    JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS, 2020, 38 (13): : 4005 - 4015
  • [48] Discovery of Entry Inhibitors for HIV-1 via a New De Novo Protein Design Framework
    Bellows, M. L.
    Taylor, M. S.
    Cole, P. A.
    Shen, L.
    Siliciano, R. F.
    Fung, H. K.
    Floudas, C. A.
    BIOPHYSICAL JOURNAL, 2010, 99 (10) : 3445 - 3453
  • [49] DE-NOVO DESIGN OF NONPEPTIDIC HIV-1 PROTEASE INHIBITORS - INCORPORATION OF STRUCTURAL WATER
    RANDAD, RS
    PAN, WX
    GULNIK, SV
    BURT, S
    ERICKSON, JW
    BIOORGANIC & MEDICINAL CHEMISTRY LETTERS, 1994, 4 (10) : 1247 - 1252
  • [50] De novo design based identification of potential HIV-1 integrase inhibitors: A pharmacoinformatics study
    Shinde, Pooja Balasaheb
    Bhowmick, Shovonlal
    Alfantoukh, Etidal
    Patil, Pritee Chunarkar
    Wabaidur, Saikh Mohammad
    Chikhale, Rupesh, V
    Islam, Md Ataul
    COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2020, 88 (88)