World Health Organization et al. The lack of new antibiotics threatens global efforts to contain drug-resistant infections. (2020).
Neill, JO Antimicrobial Resistance: Confronting a Crisis for the Health and Wealth of Nations Presided over by the Antimicrobial Resistance Review. In Briefing Paper – Confronting a Crisis for the Health and Wealth of Nations1–20 (HM Government Wellcome Trust, 2014).
Pence, HE, & 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).
Google Scholar
Irwin, JJ & Shoichet, BK Zinc—A free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45(1), 177-182 (2005).
Google Scholar
Ashby, J. The value and limitations of short-term genotoxicity testing and the inadequacy of current chemical screening criteria for cancer bioassays. Anne. NY Acad. Science. 534(1), 133-138 (1988).
Google Scholar
King, RD, Muggleton, S., Lewis, RA, and Sternberg, M. Machine Learning Drug Design: Using Inductive Logic Programming to Model the Structure-Activity Relationships of Trimethoprim Analogs Binding to Dihydrofolate Reductase. proc. Natl. Acad. Science. 89(23), 11322–11326 (1992).
Google Scholar
Hirst, JD, King, RD & Sternberg, MJ Quantitative structure-activity relationships using neural networks and inductive logic programming. I. Dihydrofolate reductase inhibition by pyrimidines. J. Comput.-Aided Mol. Of the. 8(4), 405–420 (1994).
Google Scholar
Bronstein, MM, Bruna, J., LeCun, Y., Szlam, A. & Vandergheynst, P. Geometric Deep Learning: Going Beyond Euclidean Data. IEEE signal process. Mag. 34(4), 18-42 (2017).
Google Scholar
Stokes, JM et al. A deep learning approach to antibiotic discovery. Cell 180(4), 688–702 (2020).
Google Scholar
Dai, H., Dai, B., and Song, L. Discriminant Embeddings of Latent Variable Models for Structured Data. In International Conference on Machine Learning2702–2711 (PMLR, 2016).
Yang, K. et al. Analyze learned molecular representations for property prediction. J. Chem. Inf. Model. 59(8), 3370–3388 (2019).
Google Scholar
Desk et al. Design, synthesis, and structure-activity relationship of competitive, selective, and in vivo active triazole and thiadiazole inhibitors on the n-terminal c-jun kinase substrate. J.Med. Chem. 52(7), 1943-1952 (2009).
Google Scholar
Chen, X.-W., & Jeong, JC Improved recursive feature elimination. In Sixth International Conference on Machine Learning and its Applications (ICMLA 2007)429–435 (IEEE, 2007).
Kursa, MB & Rudnicki, WR Feature selection with the Boruta package. J. Stat. Software 361–13 (2010).
Google Scholar
Tibshirani, R. Regression shrinkage and selection via lasso. JR statistics. Soc. Ser. B methodology. 58(1), 267–288 (1996).
Google Scholar
Pope, PE, Kolouri, S., Rostami, M., Martin, CE, and Hoffmann, H. Explainability methods for graph convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition10772–10781 (2019).
Kojima, R. et al. kgcn: A graph-based deep learning framework for chemical structures. J. Cheminform. 12(1), 1–10 (2020).
Google Scholar
Baldassarre, F., & Azizpour, H. Explainability techniques for convolutional networks of graphs. CoRR, Flight. abs/1905.13686, (2019).
Montavon, G., Lapuschkin, S., Binder, A., Samek, W. & Müller, K.-R. Explain nonlinear classification decisions with a deep Taylor decomposition. Pattern recognition. 65211-222 (2017).
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, SM et al. The Medication Redirection Center: A library and information resource for next-generation medications. Nat. Med. 23(4), 405–408 (2017).
Google Scholar
Zuegg, J., Hansford, KA, Elliott, AG, Cooper, MA, and Blaskovich, MA How to stimulate and facilitate early antibiotic discovery. ACS Infect. Say. 6(6), 1302-1304 (2020).
Google Scholar
Blaskovich, MA, Zuegg, J., Elliott, AG & Cooper, MA Helping chemists discover new antibiotics. ACS Infect. Say. 1(7), 285-287 (2015).
Google Scholar
Reeve, SM, Lombardo, MN & Anderson, AC Understanding the structural mechanisms of antibiotic resistance paves the way for new discoveries. Future Microbiol. ten(11), 1727-1733 (2015).
Google Scholar
Cho, H., Uehara, T. & Bernhardt, TG Beta-lactam antibiotics induce lethal dysfunction of bacterial cell wall synthesis machinery. Cell 159(6), 1300–1311 (2014).
Google Scholar
Pham, TD, Ziora, ZM, and Blaskovich, MA Quinolone antibiotics. MedChemComm ten(10), 1719-1739 (2019).
Google Scholar
Person, G. et al. Mitogen-activated protein kinase (MAP) pathways: Regulation and physiological functions. Endocr. Round. 22(2), 153–183 (2001).
Google Scholar
Fleman, R. et al. Combinatorial libraries as a tool for the discovery of novel broad-spectrum antibacterial agents targeting ESKAPE pathogens. J.Med. Chem. 58(8), 3340–3355 (2015).
Google Scholar
Wang, B. et al. Antibacterial diamines targeting bacterial membranes. J.Med. Chem. 59(7), 3140–3151 (2016).
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).
Google Scholar
Olender, D., Żwawiak, J. & Zaprutko, L. Multidirectional efficacy of biologically active nitro compounds included in drugs. Medications 11(2), 54 (2018).
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 bacteria Staphylococcus aureus and Acinetobacter baumannii. Molecules 24(11), 2051 (2019).
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. ten557 (2020).
Google Scholar
Landrum, G. et al.. Rdkit: Open-source chemistry. (2006).
Weininger, D. Smiles, a chemical language and an information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Calculation. Science. 28(1), 31-36 (1988).
Google Scholar
Him, H. & Ma, Y. Unbalanced learning: foundations, algorithms and applications (Wiley, 2013).
Google Scholar
