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Frontiers of Agricultural Science and Engineering

ISSN 2095-7505

ISSN 2095-977X(Online)

CN 10-1204/S

邮发代号 80-906

Frontiers of Agricultural Science and Engineering  2022, Vol. 9 Issue (1): 150-154   https://doi.org/10.15302/J-FASE-2021419
  本期目录
HIGH-PERFORMANCE COMPUTATION AND ARTIFICIAL INTELLIGENCE IN PESTICIDE DISCOVERY: STATUS AND OUTLOOK
Li ZHANG1(), Jialin CUI1, Qi HE1, Qing X. LI2()
1. Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China
2. Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA
 全文: PDF(398 KB)   HTML
收稿日期: 2021-08-01      出版日期: 2022-01-17
Corresponding Author(s): Li ZHANG,Qing X. LI   
 引用本文:   
. [J]. Frontiers of Agricultural Science and Engineering, 2022, 9(1): 150-154.
Li ZHANG, Jialin CUI, Qi HE, Qing X. LI. HIGH-PERFORMANCE COMPUTATION AND ARTIFICIAL INTELLIGENCE IN PESTICIDE DISCOVERY: STATUS AND OUTLOOK. Front. Agr. Sci. Eng. , 2022, 9(1): 150-154.
 链接本文:  
https://academic.hep.com.cn/fase/CN/10.15302/J-FASE-2021419
https://academic.hep.com.cn/fase/CN/Y2022/V9/I1/150
1 F Wu , L Zhuo , F Wang , W Huang , G Hao , G Yang . Auto in silico ligand directing evolution (AILDE) to facilitate the rapid and efficient discovery of drug lead. iScience, 2020, 23( 6): 101179
https://doi.org/10.1016/j.isci.2020.101179
2 Q Zhao , N Miriyala , Y Su , W Chen , X Gao , L Shao , R Yan , H Li , X Yao , D Cao , Y Wang , D Ouyang . Computer-aided formulation design for a highly soluble lutein-cyclodextrin multiple-component delivery system. Molecular Pharmaceutics, 2018, 15( 4): 1664–1673
https://doi.org/10.1021/acs.molpharmaceut.8b00056
3 G F Hao , W Jiang , Y N Ye , F X Wu , X L Zhu , F B Guo , G F Yang . ACFIS: a web server for fragment-based drug discovery. Nucleic Acids Research, 2016, 44( W1): W550–W556
https://doi.org/10.1093/nar/gkw393
4 H Yang , C Lou , L Sun , J Li , Y Cai , Z Wang , W Li , G Liu , Y Tang . admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics, 2019, 35( 6): 1067–1069
https://doi.org/10.1093/bioinformatics/bty707
5 G F Hao , F Wang , H Li , X L Zhu , W C Yang , L S Huang , J W Wu , E A Berry , G F Yang . Computational discovery of picomolar Q(o) site inhibitors of cytochrome bc1 complex. Journal of the American Chemical Society, 2012, 134( 27): 11168–11176
https://doi.org/10.1021/ja3001908
6 H Lin , X Chen , J Chen , D Wang , F Wu , S Lin , C Zhan , J Wu , W Yang , G Yang . Crystal Structure of 4-hydroxyphenylpyruvate dioxygenase in complex with substrate reveals a new starting point for herbicide discovery. Research, 2019, 2602414
7 L Xiong , H Li , L N Jiang , J M Ge , W C Yang , X L Zhu , G F Yang . Structure-based discovery of potential fungicides as succinate ubiquinone oxidoreductase inhibitors. Journal of Agricultural and Food Chemistry, 2017, 65( 5): 1021–1029
https://doi.org/10.1021/acs.jafc.6b05134
8 Z Liang , Q X Li . π-Cation interactions in molecular recognition: perspectives on pharmaceuticals and pesticides. Journal of Agricultural and Food Chemistry, 2018, 66( 13): 3315–3323
https://doi.org/10.1021/acs.jafc.8b00758
9 J F Yang , F Wang , Y Z Chen , G F Hao , G F Yang . LARMD: integration of bioinformatic resources to profile ligand-driven protein dynamics with a case on the activation of estrogen receptor. Briefings in Bioinformatics, 2020, 21( 6): 2206–2218
https://doi.org/10.1093/bib/bbz141
10 G Yang . Chemical biology-oriented molecular design of green pesticide. Bulletin of National Natural Science Foundation of China, 2020, 34( 4): 495–501
11 Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Žídek A, Bridgland A, Cowie A, Meyer C, Laydon A, Velankar S, Kleywegt G J, Bateman A, Evans R, Pritzel A, Figurnov M, Ronneberger O, Bates R, Kohl S A A, Potapenko A, Ballard A J, Romera-Paredes B, Nikolov S, Jain R, Clancy E, Reiman D, Petersen S, Senior A W, Kavukcuoglu K, Birney E, Kohli P, Jumper J, Hassabis D. Highly accurate protein structure prediction for the human proteome. Nature, 2021. doi: 10.1038/s41586-021-03828-1
12 L K Rathnayake , S H Northrup . Structure and mode of action of organophosphate pesticides: a computational study. Computational & Theoretical Chemistry, 2016, 1088 : 9–23
https://doi.org/10.1016/j.comptc.2016.04.024
13 G F Hao , Y Tan , W F Xu , R J Cao , Z Xi , G F Yang . Understanding resistance mechanism of protoporphyrinogen oxidase-inhibiting herbicides: insights from computational mutation scanning and site-directed mutagenesis. Journal of Agricultural and Food Chemistry, 2014, 62( 29): 7209–7215
https://doi.org/10.1021/jf5018115
14 F X Wu , F Wang , J F Yang , W Jiang , M Y Wang , C Y Jia , G F Hao , G F Yang . AIMMS suite: a web server dedicated for prediction of drug resistance on protein mutation. Briefings in Bioinformatics, 2018, 21( 1): 318–328
https://doi.org/10.1093/bib/bby113
15 X Li , Y Zhang , H Chen , H Li , Y Zhao . Insights into the molecular basis of the acute contact toxicity of diverse organic chemicals in the honey bee. Journal of Chemical Information and Modeling, 2017, 57( 12): 2948–2957
https://doi.org/10.1021/acs.jcim.7b00476
16 F Wang , J F Yang , M Y Wang , C Y Jia , X X Shi , G F Hao , G F Yang . Graph attention convolutional neural network model for chemical poisoning of honey bees prediction. Science Bulletin, 2020, 65( 14): 1184–1191
https://doi.org/10.1016/j.scib.2020.04.006
17 F Li , D Fan , H Wang , H Yang , W Li , Y Tang , G Liu . In silico prediction of pesticide aquatic toxicity with chemical category approaches. Toxicology Research, 2017, 6( 6): 831–842
https://doi.org/10.1039/C7TX00144D
18 K Khan , E Benfenati , K Roy . Consensus QSAR modeling of toxicity of pharmaceuticals to different aquatic organisms: ranking and prioritization of the DrugBank database compounds. Ecotoxicology and Environmental Safety, 2019, 168 : 287–297
https://doi.org/10.1016/j.ecoenv.2018.10.060
19 L He , K Xiao , C Zhou , G Li , H Yang , Z Li , J Cheng . Insights into pesticide toxicity against aquatic organism: QSTR models on Daphnia Magna. Ecotoxicology and Environmental Safety, 2019, 173 : 285–292
https://doi.org/10.1016/j.ecoenv.2019.02.014
20 F Lunghini , G Marcou , P Azam , M H Enrici , E van Miert , A Varnek . Consensus QSAR models estimating acute toxicity to aquatic organisms from different trophic levels: algae, Daphnia and fish. SAR and QSAR in Environmental Research, 2020, 31( 9): 655–675
https://doi.org/10.1080/1062936X.2020.1797872
21 X Li , L Chen , F Cheng , Z Wu , H Bian , C Xu , W Li , G Liu , X Shen , Y Tang . In silico prediction of chemical acute oral toxicity using multi-classification methods. Journal of Chemical Information and Modeling, 2014, 54( 4): 1061–1069
https://doi.org/10.1021/ci5000467
22 G Sun , Y Zhang , L Pei , Y Lou , Y Mu , J Yun , F Li , Y Wang , Z Hao , S Xi , C Li , C Chen , L Zhao , N Zhang , R Zhong , Y Peng . Chemometric QSAR modeling of acute oral toxicity of Polycyclic Aromatic Hydrocarbons (PAHs) to rat using simple 2D descriptors and interspecies toxicity modeling with mouse. Ecotoxicology and Environmental Safety, 2021, 222 : 112525
https://doi.org/10.1016/j.ecoenv.2021.112525
23 E Minerali , D H Foil , K M Zorn , S Ekins . Evaluation of assay central machine learning models for rat acute oral toxicity prediction. ACS Sustainable Chemistry & Engineering, 2020, 8( 42): 16020–16027
https://doi.org/10.1021/acssuschemeng.0c06348
24 H Zhu , T M Martin , L Ye , A Sedykh , D M Young , A Tropsha . Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chemical Research in Toxicology, 2009, 22( 12): 1913–1921
https://doi.org/10.1021/tx900189p
25 D Mulliner , F Schmidt , M Stolte , H P Spirkl , A Czich , A Amberg . Computational models for human and animal hepatotoxicity with a global application scope. Chemical Research in Toxicology, 2016, 29( 5): 757–767
https://doi.org/10.1021/acs.chemrestox.5b00465
26 Q Wang , X Li , H Yang , Y Cai , Y Wang , Z Wang , W Li , Y Tang , G Liu . In silico prediction of serious eye irritation or corrosion potential of chemicals. RSC Advances, 2017, 7( 11): 6697–6703
https://doi.org/10.1039/C6RA25267B
27 C Xu , F Cheng , L Chen , Z Du , W Li , G Liu , P W Lee , Y Tang . In silico prediction of chemical Ames mutagenicity. Journal of Chemical Information and Modeling, 2012, 52( 11): 2840–2847
https://doi.org/10.1021/ci300400a
28 X Li , Z Du , J Wang , Z Wu , W Li , G Liu , X Shen , Y Tang . In silico estimation of chemical carcinogenicity with binary and ternary classification methods. Molecular Informatics, 2015, 34( 4): 228–235
https://doi.org/10.1002/minf.201400127
29 H Hasic , T Ishida . Single-step retrosynthesis prediction based on the identification of potential disconnection sites using molecular substructure fingerprints. Journal of Chemical Information and Modeling, 2021, 61( 2): 641–652
https://doi.org/10.1021/acs.jcim.0c01100
30 J Dong , H Gao , D Ouyang . PharmSD: a novel AI-based computational platform for solid dispersion formulation design. International Journal of Pharmaceutics, 2021, 604 : 120705
https://doi.org/10.1016/j.ijpharm.2021.120705
31 W A Birru , D B Warren , S Han , H Benameur , C J H Porter , C W Pouton , D K Chalmers . Computational models of the gastrointestinal environment. 2. Phase behavior and drug solubilization capacity of a type i lipid-based drug formulation after digestion. Molecular Pharmaceutics, 2017, 14( 3): 580–592
https://doi.org/10.1021/acs.molpharmaceut.6b00887
32 A A Metwally , R M Hathout . Computer-assisted drug formulation design: novel approach in drug delivery. Molecular Pharmaceutics, 2015, 12( 8): 2800–2810
https://doi.org/10.1021/mp500740d
33 T Sou , F Soukarieh , P Williams , M J Stocks , M Cámara , C A S Bergström . Model-informed drug discovery and development in pulmonary delivery: biopharmaceutical pharmacometric modeling for formulation evaluation of pulmonary suspensions. ACS Omega, 2020, 5( 40): 25733–25746
https://doi.org/10.1021/acsomega.0c03004
34 R Meenashi , K Selvaraju , A D Stephen , C Jelsch . Theoretical crystal structure prediction of aminosalicylic acid: charge density topological and electrostatic analyses. Journal of Molecular Structure, 2020, 1213 : 128139
https://doi.org/10.1016/j.molstruc.2020.128139
35 D Cyranoski . AI drug discovery booms in China. Nature Biotechnology, 2021, 39( 8): 900–902
https://doi.org/10.1038/s41587-021-01016-0
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