Deep-learning based virtual screening of antibacterial substances

1. Mayr, L. M. & Bojanic D. Novel Trends in High-Throughput Screening. Curr. Opin. Pharmacol. 9580-588 (2009).

   ArticleCAS PubMed Google Scholar

2. Martin, J. K. 2nd et al. A dual-mechanism antibacterial kills gram negative bacteria and prevents drug resistance. Cells () 1811518-1532 (2010).

   ArticleCAS PubMed PubMed Central Google Scholar

3. Imai, Y. et al. A new antibiotic selectively destroys gram-negative bacteria. Nature, . 576459-464 (2019).

   ArticleCAS PubMed PubMed Central Google Scholar

4. Scherlach K. & Hertweck C. Mining hidden biosynthetic capacity. Nat. Common. 123864 (2021).

   ArticleCAS PubMed PubMed Central Google Scholar

5. Selles Vidal L., Isalan M., Heap J. T., & Ledesma Amaro R. A primer on directed evolution: Current methodologies and future directions RSC Chem. Biol. 4271-291 (2023).

   Article PubMed PubMed Central Google Scholar

6. Mitcheltree, M. J. et al. A synthetic antibiotic class that overcomes bacterial multidrug resistant. Nature, . 599507-512 (2020).

   ArticleCAS PubMed PubMed Central Google Scholar

7. Bohacek R. S. McMartin C. & Guida W. C. The Art and Practice of Structure-Based Drug Design: A Molecular Modeling Perspective. With. Res. Rev. 163-50 (1996).

   3.0.CO;2-6″ ” data-track=””click_references” ” data-track-action=””article reference” ” data-track-item_id=””10.1002/(SICI)1098-1128(199601)16:13.0.CO;2-6″ ” data-track-label=””10.1002/(SICI)1098-1128(199601)16:13.0.CO;2-6″” data-track-value=””article reference” ” href=””https://doi.org/10.1002%2F%28SICI%291098-1128%28199601%2916%3A1%3C3%3A%3AAID-MED1%3E3.0.CO%3B2-6″ ” rel=””nofollow noopener” “> ArticleCAS PubMed Google Scholar

8. Hoffman, P. S.: Antibacterial discovery in the 21st Century. Antibiotics, Basel 9213 (2020).

   Article PubMed Google Scholar

9. GBD 2019 Antimicrobial Resistant Collaborators Global Mortality associated with 33 Bacterial Pathogens in 2019. A systematic analysis for the Global Burden of Disease Study 2019 Lancet 4002221-2248 (2022).

   Article Google Scholar

10. Nikaido H. Re-examination of the cellular basis of bacterial membrane permeability. Microbiol. Mol. Biol. Rev. 67593-656 (2003).

    ArticleCAS PubMed PubMed Central Google Scholar

11. Brown, E. D. & Wright G. D. Antibacterial Drug Discovery in the Resistance Era. Nature, 529336-343 (2016).

    ArticleCAS PubMed Google Scholar

12. Shivanyuk, A. N. et al. Enamine real database : Making chemical diversity real. Chem. Today 25,58-59 (2007).

    CAS Google Scholar

13. Melo, M. C. R., Maasch, J. R. M. A. & de la Fuente Nunez, C. & Accelerating antibiotic discoveries through artificial intelligence. Commun. Biol. 41050 (2021).

    ArticleCAS PubMed PubMed Central Google Scholar

14. Stokes, J. M. et al. A deep learning approach for antibiotic discovery. Cell, 183475-483 (2010).

    ArticleCAS PubMed Google Scholar

15. Liu, G. et al. Deep learning-guided development of an antibiotic that targets Acinetobacter baumannii (19460784]andAcinetobacter baumannii (19460784] Nat. Chem. Biol. 191342-1350 (2023).

    ArticleCAS PubMed Google Scholar

16. Swanson, K. et al. Generative AI to design and validate easily synthesizable, structurally novel antibiotics. Nat. Mach. Intel.6338-353 (2024).

    Article Google Scholar

17. Wong, F. et al. Discovery of a structurally class of antibiotics using explainable deep learning. Nature. 626177-185 (2024).

    ArticleCAS PubMed Google Scholar

18. Zoffmann, S. et al. Machine learning-powered antibiotics drug discovery. Sci. Rep. 95013 (2019).

    Article PubMed PubMed Central Google Scholar

19. Tan, X. et al. Explainable deep-learning-assisted discrimination of fluorescence for aminoglycoside antimicrobial identification. Anal. Chem. 94829-836 (2022).

    ArticleCAS PubMed Google Scholar

20. Sulavik, M. C. et al. Antibiotic susceptibility of Escherichia Coli strains lacking multidrug pump genes. Antimicrob. Agents Chemother. 451126-1136 (2001).

    ArticleCAS PubMed PubMed Central Google Scholar

21. Zgurskaya, H. I., Krishnamoorthy, G., Ntreh, A. & Lu, S. Mechanisms and functions of the outer membrane channels tolc and enterobacteria in multidrug resistant and physiology. Front. Microbiol. 2189 (2011).

    Article PubMed PubMed Central Google Scholar

22. Baba, T., et al. Construction of Escherichia coli K-12 single-gene knockouts in frame: The Keio Collection. Mol. Syst. Biol. 22006.0008 (2006).

    Article PubMed PubMed Central Google Scholar

23. Wright, M. Kaur, L. K. Thompson & Cox G. A historical view on the multifunctional outer-membrane channel protein TolC ofEscherichia coli NPJ Antimicrob. Resist 36 (2025).

    Article PubMed PubMed Central Google Scholar

24. Stumpfe, D., Hu, H. & Bajorath, J. Evolving concept for activity cliffs. ACS Omega 414360-14368 (2019).

    ArticleCAS PubMed PubMed Central Google Scholar

25. Hu, W. et al. Strategies for pretraining graph neural networks In Proceedings of the 8th International Conference on Learning Representations (ed. 8th International Conference on Learning Representations (19460784) (ed. Rush, A.) (ICLR, 2020).

26. Xu, K. et al. Representation Learning on Graphs with Jumping Knowledge Networks In Proceedings of the 35th International Conference on Machine Learning (eds Dy, J. 35th International Conference on Machine Learning (19460784) (eds Dy J. & Krause, A.) (PMLR, 2018).

27. You, Y. et al. Graph contrastive augmentations. In Proceedings of the Proc. 34th International Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) (NeurIPS, 2020).

28. Sterling, T. & Irwin J. J. ZINC 15, ligand discovery for all. J. Chem. Inf. Model. 552324-2337 (2015).

    ArticleCAS PubMed PubMed Central Google Scholar

29. Li, H., Wang, X., Zhang, Z. & Zhu W. Out-of distribution generalization on graphs: A survey. IEEE Trans. Pattern Anal. Mach. Intell.4710490-10512 (2022).

    Article Google Scholar

30. Wang, Y., Wang, J., Cao, Z. Molecular contrastive representational learning via graph neural networks. Nat. Mach. Intel. 4279-287 (2022).

    Article Google Scholar

31. Screening ultra-large Virtual Libraries. Nat. Rev. Drug Discovery 2195 (2022).

    ArticleCAS PubMed Google Scholar

32. Wu, Z. et al. MoleculeNet : a benchmark for Molecular Machine Learning. Chem. Sci. 9513-530 (2018).

    ArticleCAS PubMed Google Scholar

33. Yang, K. et al. Analyzing learned molecular representations for prediction of properties. J. Chem. Inf. Model. 593370-3388 (2019).

    ArticleCAS PubMed PubMed Central Google Scholar

34. Hu, W. et al. Open graph benchmark: datasets on graphs for machine-learning. In Proceedings of the National Academy of Sciences. 34th International Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) (NeurIPS, 2020).

35. Kiselev V. Y. Andrews T. S. & Hemberg M. Challenges of unsupervised clustering single-cell RNAseq data. Nat. Rev. Genet. 20273-282 (2019).

    ArticleCAS PubMed Google Scholar

36. Truchon, J.-F. & Bayly, C. I. Evaluating virtual screen methods: Good and bad metrics for the “early recognition” problem. J. Chem. Inf. Model. 47488-508 (2007).

    ArticleCAS PubMed Google Scholar

37. Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. In Proceedings of the Proc. 34th International Conference on Machine Learning (eds Precup D. & Teh Y.W.) (PMLR 2017).

38. Scalia, G. Grambow, C. A. Pernici, B. Li, Y. P. & Green W. H. Evaluating scalable uncertainties estimation methods for molecular property predictions based on deep learning. J. Chem. Inf. Model. 602697-2717 (2020).

    ArticleCAS PubMed Google Scholar

39. Raetz, CRH CRH et al . Discovery of new biosynthetic paths: The lipid A Story. J. Lipid Res. 50S103-S108 (2009).

    Article PubMed PubMed Central Google Scholar

40. Cho, J. et al. Structural basis of the UDP-diacylglucosamine pyrophosphohydrolase LpxH inhibition by sulfonyl piperazine antibiotics. Proc. Natl Acad. Sci. USA 1174109-4116 (2010).

    ArticleCAS PubMed PubMed Central Google Scholar

41. Mohan, S., Kelly, T. M., Eveland, S. S., Raetz, C. R. & Anderson, M. S. An Escherichia coli gene (FabZ) encoding (3R )-hydroxymyristoyl acyl carrier protein dehydrase. Relationship to fabA, and suppression of mutations within lipid A biosynthesis. J. Biol. Chem. 26932896-32903 (1994).

    ArticleCAS PubMed Google Scholar

42. Keating, DH, Carey, MR & Cronan, JE Jr. The unmodified form (apo) of Escherichia coli is a powerful inhibitor of cell proliferation. J. Biol. Chem. 27022229-22235 (1995).

    ArticleCAS PubMed Google Scholar

43. Guest, R. L., Rutherford, S. T. & Silhavy, T. J. Border control: regulating LPS biogenesis. Trends Microbiol. 29334-345 (2021).

    ArticleCAS PubMed Google Scholar

44. Richter, M. F. et al. Predictive compound-accumulation rules yield a wide-spectrum antibiotic. Nature, 545299-304 (2017).

    ArticleCAS PubMed PubMed Central Google Scholar

45. Lee, K., Lee, K., Lee, H. & Shin, J. A simple unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In Proceedings of the Proc. 32nd International Conference on Neural Information Processing Systems (eds Bengio S. et. al.). (NeurIPS, 2018).

46. Winkens, J. et al. Contrastive training to improve out-of distribution detection. Preprint at https://doi.org/10.48550/arXiv.2007.05566 (2020).

47. Graff, D. E., Shakhnovich, E. I. & Coley C. W. Accelerating virtual screening for high-throughput through molecular pools-based active learning. Chem. Sci. 127866-7881 (2021).

    ArticleCAS PubMed PubMed Central Google Scholar

48. Soleimany, A. P. et al. Evidence-based deep learning for guided prediction and discovery of molecular properties. ACS Cent. Sci. 71356-1367 (2021).

    ArticleCAS PubMed PubMed Central Google Scholar

49. Azzolin, S., Longa, A., Barbiero, P., Lio, P. & Passerini, A. Global explainability of GNNs by logical combination of learned concepts In Proceedings of the 11th International Conference on Learning Representations (ed. 11th International Conference on Learning Representations (19460784) (ed. Liu, Y.) (ICLR, 2023).

50. Xuanyuan H., Barbiero P., Georgiev D., Magister L. C. & Lio P. Global concept based interpretability via neuron analysis for graph neural networks. In Proceedings of the IEEE. 37th AAAI Conference on Artificial Intelligence (eds Williams B., Chen Y. & Neville, J.) (AAAI, 2023).

51. Scholkopf, B. et al. Toward causal representation. Proc. IEEE 109612-634 (2021).

    Article Google Scholar

52. Gao, H. et al. Robust learning of causal graph representations against confounding factors. In Proceedings of the IEEE. 37th AAAI Conference on Artificial Intelligence (eds Williams B., Chen Y. & Neville, J.) (AAAI, 2023).

53. Rawal, A. Raglin A. Rawat D. B. Sadler B. M. McCoy J. Causality in trustworthy artificial intelligence: State, challenges and prospects. ACM Comp. Surv.571-30 (2024).

    Article Google Scholar

54. Moffat, J. G., Vincent, F., Lee, J. A., Eder, J. Opportunities and challenges in phenotypic drugs discovery: An industry perspective. Nat. Rev. Drug Discov. 16531-543 (2017).

    ArticleCAS PubMed Google Scholar

55. Ortmayr, K. de la Cruz Moreno R. & Zampieri M. Expanding search for small molecule antibacterials through multidimensional profiling. Nat. Chem. Biol. 18584-595 (2022).

    ArticleCAS PubMed Google Scholar

56. Magana, M. et al. The value of antimicrobials peptides at a time of resistance. Lancet Infect. Dis. 20e216-e230 (2020).

    ArticleCAS PubMed Google Scholar

57. Wong, F. de la Fuente Nunez, C. & Collins J. J. Leveraging Artificial Intelligence in the Fight against Infectious Diseases. Science, 381164-170(2023).

    ArticleCAS PubMed PubMed Central Google Scholar

58. Nickerson, N. N., et al. A novel inhibitor of LolCDE, an ABC transporter that is essential for the trafficking of lipoproteins in Gram-negative bacteria.Antimicrob. Agents Chemother. 62e02151-17 (2018).

    Article PubMed PubMed Central Google Scholar

59. Datsenko, K. A. & Wanner B. L. One step inactivation of chromosomal gene in Escherichia coli K-12 using PCR products.Proc. Natl Acad. Sci. USA 976640-6645(2000).

    ArticleCAS PubMed PubMed Central Google Scholar

60. Plaut, R. D., Mocca, C. P., Prabhakara, R., Merkel, T. J. & Stibitz S. Stable luminescent Staphylococcus aureus for bioluminescent imaging. PLoS ONE, 8,e59232 (2012).

    ArticleCAS PubMed PubMed Central Google Scholar

61. Girgis, H. S., et al. Single-molecule nanopore sequence reveals extreme target number heterogeneity among arylomycin resistant mutants. Proc. Natl Acad. Sci. USA 118

    ArticleCAS PubMed Google Scholar

62. Xu, K., Hu, W., Leskovec, J. How powerful are graph-neural networks? In Proceedings of the Proc. 7th International Conference on Learning Representations (ICLR, 2019).

63. Xiong, Z. et al. Pushing the limits of molecular display for drug discovery using the graph attention mechanism. J.Med. Chem. 638749-8760 (2020).

    ArticleCAS PubMed Google Scholar

64. Velickovic, P. et al. Graph attention networks. In Proceedings of the Proc. 6th International Conference on Learning Representations (19460784) (eds Bengio Y. & LeCun, Y.) (ICLR, 2018).

65. Kipf, T. N. & Welling M. Semi-supervised classifier with graph convolutional network. In Proceedings of the National Academy of Sciences. 5th International Conference on Learning Representations (eds Dahl G., Petrov S. & Sindhwani V.) (ICLR 2017).

66. Wu, Z. et al. Representing long range context for graph neural network with global attention In Proceedings of the Proc. 35th International Conference on Neural Information Processing Systems (eds ranzato, M. et al.) (neurops, 2021).

67. Goh, G. B. Hodas, N. O. Siegel, C. and Vishnu A. SMILES2Vec : an interpretable deep neural network for predicting chemistry properties. Preprint at https://doi.org/10.48550/arXiv.1712.02034 (2017).

68. A simple framework for contrastive visual representations learning. Chen, T. Kornblith S. Norouzi M. Hinton G. In Proceedings of the Proc. 37th International Conference on Machine Learning (eds Iii H. D. and Singh A.) (PMLR, 2020).

69. Chen, T. Kornblith S. Swersky K. Norouzi M. & Hinton G. E. Large self-supervised models make strong semi-supervised learner. In Proceedings of the Proc. 34th International Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) (NeurIPS, 2020).

70. Sun, M., Xing, J., Wang, H., Chen, B. & Zhou J. MoCL: data driven molecular fingerprint using knowledge-aware contrastive learn from molecular graph. In Proceedings of the Proc. 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (eds Zhu F. et.al.) (Association for Computing Machinery 2021).

71. Kong, K. et al. Robust optimization for large-scale graphs. In IEEE/CVF Conference Proceedings on Computer Vision and Pattern Recognition (ed. O’Conner, L.) (IEEE, 2022).

72. Shafahi, A. et al. Adversarial Training for Free! In Proceedings. 33rd International Conference on Neural Information Processing Systems (NeurIPS 2019) .

73. Macielag, M. J. Chemical properties of antimicrobials, and their uniqueness. In Antibiotic Discovery and Development (eds Dougherty T. J. & Pucci, M. J.) (Springer, 2012).

74. Kolesnikov, A., Zhai, X. Revisiting Self-supervised Visual Representation Learning. In Proceeding of the IEEE/CVF conference on Computer Vision and Pattern Recognition (ed. O’Conner, L.) (IEEE, 2019).

75. Wilson, A. G.; Hu, Z.; Salakhutdinov R. & Xing E. P. Deep Kernel Learning. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (eds Gretton, A. 19th International Conference on Artificial Intelligence and Statistics (eds Gretton A. & Robert, C. C.) (PMLR, 2016).

76. Gal, Y. & Ghahramani, Z. Dropout as a Bayesian Approximation: Representing model uncertainty in Deep Learning. In Proceedings of the Proc. 33rd International Conference on Machine Learning (eds Balcan M. F. and Weinberger K. Q.) (PMLR, 2016).

77. Lakshminarayanan, B., Pritzel, A. & Blundell C. Simple and scalable prediction uncertainty estimation using deep ensembles. In Proceedings of the Proc. 31st International Conference on Neural Information Processing Systems (eds von Luxburg, U. et al.) (NeurIPS, 2017).

78. Wilson, A. Hu, Z. Salakhutdinov R. & Xing E. Stochastic variations in deep kernel learning. In Proceedings of the Proc. 30th International Conference on Neural Information Processing Systems (eds Lee, DD et al.) (NeurIPS, 2016).

79. He, K. Zhang, X. Ren, S. & Sun J. Delving deeply into rectifiers: Exceeding human-level performances on ImageNet classification. In Proceeding of the IEEE International Conference on Computer Vision (19460784) (ed. O’Conner, L.) (IEEE, 2015).

80. trag, va, waltman, L. & van eck, NJ from Louvan to Leeden Sci. Rep. 95233 (2019).

    ArticleCAS PubMed PubMed Central Google Scholar

81. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 1915 (2018).

    Article PubMed PubMed Central Google Scholar

82. Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J.Med. Chem. 392887-2893 (1996).

    ArticleCAS PubMed Google Scholar

83. McCloskey, K., Taly, A., Monti, F., Brenner, M. P. & Colwell, L. J. Using attribution to decode the binding mechanism in neural networks models for chemistry. Proc. Natl Acad. Sci. USA 116(2019).

    ArticleCAS PubMed PubMed Central Google Scholar

84. Gobbi A., Giannetti A. M. Chen H. & Lee M.-L. Atom atom-path similarity, sphere exclusion and clustering for prioritizing fragments.J. Cheminform. 711 (2015).

    ArticlePubMedPubMed CentralGoogle Scholar

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