An interpretable machine learning approach to identify mechanism of action of antibiotics | Scientific Reports – Nature.com

  • World Health Organization et al. Lack of new antibiotics threatens global efforts to contain drug-resistant infections. (2020).

  • Neill, J. O. Antimicrobial resistance: tackling a crisis for the health and wealth of nations the review on antimicrobial resistance chaired. In Review Paper-Tackling a Crisis for the Health and Wealth of Nations, 1–20 (HM Government Wellcome Trust, 2014).

  • Pence, H.E., & Williams A. Chemspider: an online chemical information resource (2010).

  • Kim, S. et al. Pubchem substance and compound databases. Nucl. Acids Res. 44(D1), D1202–D1213 (2016).

    CAS  Article  Google Scholar 

  • Irwin, J. J. & Shoichet, B. K. Zinc—A free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45(1), 177–182 (2005).

    CAS  Article  Google Scholar 

  • Ashby, J. The value and limitations of short-term genotoxicity assays and the inadequacy of current cancer bioassay chemical selection criteria. Ann. N. Y. Acad. Sci. 534(1), 133–138 (1988).

    ADS  CAS  Article  Google Scholar 

  • King, R. D., Muggleton, S., Lewis, R. A. & Sternberg, M. Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proc. Natl. Acad. Sci. 89(23), 11322–11326 (1992).

    ADS  CAS  Article  Google Scholar 

  • Hirst, J. D., King, R. D. & Sternberg, M. J. Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines. J. Comput.-Aided Mol. Des. 8(4), 405–420 (1994).

    ADS  CAS  Article  Google Scholar 

  • Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A. & Vandergheynst, P. Geometric deep learning: Going beyond Euclidean data. IEEE Signal Process. Mag. 34(4), 18–42 (2017).

    ADS  Article  Google Scholar 

  • Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180(4), 688–702 (2020).

    CAS  Article  Google Scholar 

  • Dai, H., Dai, B., & Song, L. Discriminative embeddings of latent variable models for structured data. In International Conference on Machine Learning, 2702–2711 (PMLR, 2016).

  • Yang, K. et al. Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59(8), 3370–3388 (2019).

    CAS  Article  Google Scholar 

  • De, S. K. et al. Design, synthesis, and structure–activity relationship of substrate competitive, selective, and in vivo active triazole and thiadiazole inhibitors of the c-jun n-terminal kinase. J. Med. Chem. 52(7), 1943–1952 (2009).

    CAS  Article  Google Scholar 

  • Chen, X.-W., & Jeong, J. C. Enhanced recursive feature elimination. In Sixth International Conference on Machine Learning and Applications (ICMLA 2007), 429–435 (IEEE, 2007).

  • Kursa, M. B. & Rudnicki, W. R. Feature selection with the Boruta package. J. Stat. Softw. 36, 1–13 (2010).

    Article  Google Scholar 

  • Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Methodol. 58(1), 267–288 (1996).

    MathSciNet  MATH  Google Scholar 

  • Pope, P.E., Kolouri, S., Rostami, M., Martin, C. E., & Hoffmann, H. Explainability methods for graph convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10772–10781 (2019).

  • Kojima, R. et al. kgcn: A graph-based deep learning framework for chemical structures. J. Cheminform. 12(1), 1–10 (2020).

    Article  Google Scholar 

  • Baldassarre, F., & Azizpour, H. Explainability techniques for graph convolutional networks. CoRR, Vol. abs/1905.13686, (2019).

  • Montavon, G., Lapuschkin, S., Binder, A., Samek, W. & Müller, K.-R. Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recogn. 65, 211–222 (2017).

    ADS  Article  Google Scholar 

  • Ying, R., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. Gnn explainer: A tool for post-hoc explanation of graph neural networks. arXiv preprint arXiv:1903.03894, (2019).

  • Corsello, S. M. et al. The drug repurposing hub: A next-generation drug library and information resource. Nat. Med. 23(4), 405–408 (2017).

    CAS  Article  Google Scholar 

  • Zuegg, J., Hansford, K. A., Elliott, A. G., Cooper, M. A. & Blaskovich, M. A. How to stimulate and facilitate early stage antibiotic discovery. ACS Infect. Dis. 6(6), 1302–1304 (2020).

    CAS  Article  Google Scholar 

  • Blaskovich, M. A., Zuegg, J., Elliott, A. G. & Cooper, M. A. Helping chemists discover new antibiotics. ACS Infect. Dis. 1(7), 285–287 (2015).

    CAS  Article  Google Scholar 

  • Reeve, S. M., Lombardo, M. N. & Anderson, A. C. Understanding the structural mechanisms of antibiotic resistance sets the platform for new discovery. Future Microbiol. 10(11), 1727–1733 (2015).

    CAS  Article  Google Scholar 

  • Cho, H., Uehara, T. & Bernhardt, T. G. Beta-lactam antibiotics induce a lethal malfunctioning of the bacterial cell wall synthesis machinery. Cell 159(6), 1300–1311 (2014).

    CAS  Article  Google Scholar 

  • Pham, T. D., Ziora, Z. M. & Blaskovich, M. A. Quinolone antibiotics. MedChemComm 10(10), 1719–1739 (2019).

    CAS  Article  Google Scholar 

  • Pearson, G. et al. Mitogen-activated protein (MAP) kinase pathways: Regulation and physiological functions. Endocr. Rev. 22(2), 153–183 (2001).

    CAS  PubMed  Google Scholar 

  • Fleeman, R. et al. Combinatorial libraries as a tool for the discovery of novel, broad-spectrum antibacterial agents targeting the ESKAPE pathogens. J. Med. Chem. 58(8), 3340–3355 (2015).

    CAS  Article  Google Scholar 

  • Wang, B. et al. Antibacterial diamines targeting bacterial membranes. J. Med. Chem. 59(7), 3140–3151 (2016).

    CAS  Article  Google Scholar 

  • Qian, L., Guan, Y., He, B. & Xiao, H. Modified guanidine polymers: Synthesis and antimicrobial mechanism revealed by AFM. Polymer 49(10), 2471–2475 (2008).

    CAS  Article  Google Scholar 

  • Olender, D., Żwawiak, J. & Zaprutko, L. Multidirectional efficacy of biologically active nitro compounds included in medicines. Pharmaceuticals 11(2), 54 (2018).

    Article  Google Scholar 

  • Whitt, J. et al. Synthesis of hydrazone derivatives of 4-[4-formyl-3-(2-oxochromen-3-yl) pyrazol-1-yl] benzoic acid as potent growth inhibitors of antibiotic-resistant Staphylococcus aureus and Acinetobacter baumannii. Molecules 24(11), 2051 (2019).

    Article  Google Scholar 

  • Lv, Q.-Z. et al. A new antifungal agent (4-phenyl-1, 3-thiazol-2-yl) hydrazine induces oxidative damage in Candida albicans. Front. Cell. Infect. Microbiol. 10, 557 (2020).

    Article  Google Scholar 

  • Landrum, G. et al.. Rdkit: Open-source cheminformatics. (2006).

  • Weininger, D. Smiles, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28(1), 31–36 (1988).

    CAS  Article  Google Scholar 

  • He, H. & Ma, Y. Imbalanced Learning: Foundations, Algorithms, and Applications (Wiley, 2013).

    Book  Google Scholar 

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