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Tackling assay interference associated with small molecules

Nature Reviews Chemistry (2024)Cite this article

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Abstract

Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.

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Fig. 1: Integration of theoretical and experimental approaches to tackle assay interference caused by small organic compounds.
Fig. 2: Theoretical approaches for detecting and predicting assay interference caused by small organic compounds.

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References

  1. Sánchez-Ruiz, A. & Colmenarejo, G. Updated prediction of aggregators and assay-interfering substructures in food compounds. J. Agric. Food Chem. 69, 15184–15194 (2021).

    Article  PubMed  Google Scholar 

  2. Baell, J. B. & Holloway, G. A. New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem. 53, 2719–2740 (2010).

    Article  CAS  PubMed  Google Scholar 

  3. David, L. et al. Identification of compounds that interfere with high‐throughput screening assay technologies. ChemMedChem 14, 1795–1802 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bisson, J. et al. Can invalid bioactives undermine natural product-based drug discovery? J. Med. Chem. 59, 1671–1690 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Roche, O. et al. Development of a virtual screening method for identification of “frequent hitters” in compound libraries. J. Med. Chem. 45, 137–142 (2002).

    Article  CAS  PubMed  Google Scholar 

  6. Thorne, N., Auld, D. S. & Inglese, J. Apparent activity in high-throughput screening: origins of compound-dependent assay interference. Curr. Opin. Chem. Biol. 14, 315–324 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Coussens, N. P. et al. in Assay Guidance Manual (eds Markossian, S. et al.) 1067–1116 (NCATS, 2020).

  8. Baell, J. & Walters, M. A. Chemistry: chemical con artists foil drug discovery. Nature 513, 481–483 (2014).

    Article  CAS  PubMed  Google Scholar 

  9. Coussens, N. P., Auld, D. S., Thielman, J. R., Wagner, B. K. & Dahlin, J. L. Addressing compound reactivity and aggregation assay interferences: case studies of biochemical high-throughput screening campaigns benefiting from the National Institutes of Health Assay Guidance Manual guidelines. SLAS Discov. 26, 1280–1290 (2021).

    Article  PubMed  Google Scholar 

  10. Hermann, J. C. et al. Metal impurities cause false positives in high-throughput screening campaigns. ACS Med. Chem. Lett. 4, 197–200 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Chatzopoulou, M. et al. Pilot study to quantify palladium impurities in lead-like compounds following commonly used purification techniques. ACS Med. Chem. Lett. 13, 262–270 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dahlin, J. L. et al. Nuisance compounds in cellular assays. Cell Chem. Biol. 28, 356–370 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Senger, M. R., Fraga, C. A. M., Dantas, R. F. & Silva, F. P. Filtering promiscuous compounds in early drug discovery: is it a good idea? Drug Discov. Today 21, 868–872 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Rothenaigner, I. & Hadian, K. Brief guide: experimental strategies for high-quality hit selection from small-molecule screening campaigns. SLAS Discov. Adv. Sci. Drug Discov. 7, 851–854 (2021).

    Article  Google Scholar 

  15. Kallal, L. A. et al. High-throughput screening and triage assays identify small molecules targeting c-MYC in cancer cells. SLAS Discov. 26, 216–229 (2021).

    Article  CAS  PubMed  Google Scholar 

  16. Vidler, L. R., Watson, I. A., Margolis, B. J., Cummins, D. J. & Brunavs, M. Investigating the behavior of published PAINS alerts using a pharmaceutical company data set. ACS Med. Chem. Lett. 9, 792–796 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Aldrich, C. et al. The ecstasy and agony of assay interference compounds. ACS Cent. Sci. 3, 143–147 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. McCoy, M. A. et al. Biophysical survey of small-molecule β-catenin inhibitors: a cautionary tale. J. Med. Chem. 65, 7246–7261 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Dahlin, J. L. & Walters, M. A. How to triage PAINS-full research. ASSAY Drug Dev. Technol. 14, 168–174 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Arrowsmith, C. H. et al. The promise and peril of chemical probes. Nat. Chem. Biol. 11, 536–541 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Newman, D. J. Problems that can occur when assaying extracts to pure compounds in biological systems. Curr. Ther. Res. 95, 100645 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kenny, P. W. Comment on the ecstasy and agony of assay interference compounds. J. Chem. Inf. Model. 57, 2640–2645 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Seidler, J., McGovern, S. L., Doman, T. N. & Shoichet, B. K. Identification and prediction of promiscuous aggregating inhibitors among known drugs. J. Med. Chem. 46, 4477–4486 (2003).

    Article  CAS  PubMed  Google Scholar 

  24. Doak, A. K., Wille, H., Prusiner, S. B. & Shoichet, B. K. Colloid formation by drugs in simulated intestinal fluid. J. Med. Chem. 53, 4259–4265 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Baell, J. B. Feeling nature’s PAINS: natural products, natural product drugs, and pan assay interference compounds (PAINS). J. Nat. Prod. 79, 616–628 (2016).

    Article  CAS  PubMed  Google Scholar 

  26. Hendrich, A. B. Flavonoid-membrane interactions: possible consequences for biological effects of some polyphenolic compounds. Acta Pharmacol. Sin. 27, 27–40 (2006).

    Article  CAS  PubMed  Google Scholar 

  27. Pawlikowska-Pawlęga, B. et al. Modification of membranes by quercetin, a naturally occurring flavonoid, via its incorporation in the polar head group. Biochim. Biophys. Acta 1768, 2195–2204 (2007).

    Article  PubMed  Google Scholar 

  28. Kongkamnerd, J. et al. The quenching effect of flavonoids on 4-methylumbelliferone, a potential pitfall in fluorimetric neuraminidase inhibition assays. SLAS Discov. 16, 755–764 (2011).

    Article  CAS  Google Scholar 

  29. McGovern, S. L., Caselli, E., Grigorieff, N. & Shoichet, B. K. Common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J. Med. Chem. 45, 1712–1722 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Capuzzi, S. J., Muratov, E. N. & Tropsha, A. Phantom PAINS: problems with the utility of alerts for pan-assay interference compounds. J. Chem. Inf. Model. 57, 417–427 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cassinelli, G. The roots of modern oncology: from discovery of new antitumor anthracyclines to their clinical use. Tumori J. 102, 226–235 (2016).

    Article  CAS  Google Scholar 

  32. Simeonov, A. & Davis, M. I. in Assay Guidance Manual (eds Markossian, S. et al.) 1151–1162 (NCATS, 2004).

  33. Auld, D. S. & Inglese, J. in Assay Guidance Manual (eds Markossian, S. et al.) 1163–1175 (NCATS, 2018).

  34. Dahlin, J. L. & Walters, M. A. The essential roles of chemistry in high-throughput screening triage. Future Med. Chem. 6, 1265–1290 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Jones, P., McElroy, S., Morrison, A. & Pannifer, A. The importance of triaging in determining the quality of output from high-throughput screening. Future Med. Chem. 7, 1847–1852 (2015).

    Article  CAS  PubMed  Google Scholar 

  36. Auld, D. S. et al. in Assay Guidance Manual (eds Markossian, S. et al.) 1177–1202 (NCATS, 2017).

  37. Dahlin, J. L., Baell, J. & Walters, M. A. in Assay Guidance Manual (eds. Markossian, S. et al.) 1117-1150 (NCATS, 2015).

  38. Busby, S. A. et al. Advancements in assay technologies and strategies to enable drug discovery. ACS Chem. Biol. 15, 2636–2648 (2020).

    Article  CAS  PubMed  Google Scholar 

  39. Holdgate, G., Embrey, K., Milbradt, A. & Davies, G. Biophysical methods in early drug discovery. ADMET DMPK 7, 222–241 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Dahlin, J. L. et al. PAINS in the assay: chemical mechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS. J. Med. Chem. 58, 2091–2113 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kitchen, D. B. & Decornez, H. Y. in Small Molecule Medicinal Chemistry: Strategies and Technologies (eds Czechtizky, W. & Hamley, P.) Ch. 7 (Wiley, 2015).

  42. Posner, B. A., Xi, H. & Mills, J. E. J. Enhanced HTS hit selection via a local hit rate analysis. J. Chem. Inf. Model. 49, 2202–2210 (2009).

    Article  CAS  PubMed  Google Scholar 

  43. Schuffenhauer, A. et al. Evolution of Novartis’ small molecule screening deck design. J. Med. Chem. 63, 14425–14447 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Johnson, M. & Maggiora, G. (eds) Concepts and Applications of Molecular Similarity (Wiley, 1990).

  45. Willett, P. The calculation of molecular structural similarity: principles and practice. Mol. Inform. 33, 403–413 (2014).

    Article  CAS  PubMed  Google Scholar 

  46. Borrel, A. et al. High-throughput screening to predict chemical-assay interference. Sci. Rep. 10, 3986 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kenny, P. W. & Sadowski, J. in Methods and Principles in Medicinal Chemistry (ed. Oprea, T. I.) 271–285 (Wiley, 2005).

  48. Wawer, M. & Bajorath, J. Local structural changes, global data views: graphical substructure–activity relationship trailing. J. Med. Chem. 54, 2944–2951 (2011).

    Article  CAS  PubMed  Google Scholar 

  49. Guha, R. & Van Drie, J. H. Structure–activity landscape index: identifying and quantifying. J. Chem. Inf. Model. 48, 646–658 (2008).

    Article  CAS  PubMed  Google Scholar 

  50. Lajiness, M. S. in QSAR: Rational Approaches to the Design of Bioactive Compounds (eds Silipo, C. & Vittoria, A.) 201–204 (Elsevier, 1990).

  51. Medina‐Franco, J. L. Activity cliffs: facts or artifacts? Chem. Biol. Drug Des. 81, 553–556 (2013).

    Article  PubMed  Google Scholar 

  52. Guha, R. & Medina-Franco, J. L. On the validity versus utility of activity landscapes: are all activity cliffs statistically significant? J. Cheminform. 6, 11 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Wang, Y., Cheng, T. & Bryant, S. H. PubChem BioAssay: a decade’s development toward open high-throughput screening data sharing. SLAS Discov. 22, 655–666 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47, D930–D940 (2019).

    Article  CAS  PubMed  Google Scholar 

  56. ChEMBL Version 33. https://www.ebi.ac.uk/chembl/ (2023).

  57. European Chemical Biology Database (ECBD). https://ecbd.eu/ (2024).

  58. Alves, V. M. et al. SCAM Detective: accurate predictor of small, colloidally aggregating molecules. J. Chem. Inf. Model. 60, 4056–4063 (2020).

    Article  CAS  PubMed  Google Scholar 

  59. Yang, Z.-Y. et al. ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds. Brief. Bioinform. 22, bbaa282 (2021).

    Article  PubMed  Google Scholar 

  60. Ghosh, D., Koch, U., Hadian, K., Sattler, M. & Tetko, I. V. Luciferase Advisor: high-accuracy model to flag false positive hits in luciferase HTS assays. J. Chem. Inf. Model. 58, 933–942 (2018).

    Article  CAS  PubMed  Google Scholar 

  61. Yang, Z.-Y. et al. Structural analysis and identification of false positive hits in luciferase-based assays. J. Chem. Inf. Model. 60, 2031–2043 (2020).

    Article  CAS  PubMed  Google Scholar 

  62. Molina, C., Ait-Ouarab, L. & Minoux, H. Isometric Stratified Ensembles: a partial and incremental adaptive applicability domain and consensus-based classification strategy for highly imbalanced data sets with application to colloidal aggregation. J. Chem. Inf. Model. 62, 1849–1862 (2022).

    Article  CAS  PubMed  Google Scholar 

  63. Irwin, J. J. et al. An aggregation advisor for ligand discovery. J. Med. Chem. 58, 7076–7087 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Google Dataset Search. https://datasetsearch.research.google.com/ (2024).

  65. Sieg, J., Flachsenberg, F. & Rarey, M. In need of bias control: evaluating chemical data for machine learning in structure-based virtual screening. J. Chem. Inf. Model. 59, 947–961 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46, 3–26 (2001).

    Article  CAS  PubMed  Google Scholar 

  68. Daylight Chemical Information Systems. SMARTS theory manual. https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html (2023).

  69. Bruns, R. F. & Watson, I. A. Rules for identifying potentially reactive or promiscuous compounds. J. Med. Chem. 55, 9763–9772 (2012).

    Article  CAS  PubMed  Google Scholar 

  70. Brenk, R. et al. Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem 3, 435–444 (2008).

    Article  CAS  PubMed  Google Scholar 

  71. Pearce, B. C., Sofia, M. J., Good, A. C., Drexler, D. M. & Stock, D. A. An empirical process for the design of high-throughput screening deck filters. J. Chem. Inf. Model. 46, 1060–1068 (2006).

    Article  CAS  PubMed  Google Scholar 

  72. Chakravorty, S. J. et al. Nuisance compounds, PAINS filters, and dark chemical matter in the GSK HTS collection. SLAS Discov. 23, 532–544 (2018).

    Article  CAS  PubMed  Google Scholar 

  73. McCallum, M. M. et al. High-throughput identification of promiscuous inhibitors from screening libraries with the use of a thiol-containing fluorescent probe. SLAS Discov. 18, 705–713 (2013).

    Article  Google Scholar 

  74. Matlock, M. K., Hughes, T. B., Dahlin, J. L. & Swamidass, S. J. Modelling small-molecule reactivity identifies promiscuous bioactive compounds. J. Chem. Inf. Model. 58, 1483–1500 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Schorpp, K. et al. Identification of small-molecule frequent hitters from AlphaScreen high-throughput screens. J. Biomol. Screen. 19, 715–726 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Baell, J. B. & Nissink, J. W. M. Seven year itch: pan-assay interference compounds (PAINS) in 2017 — utility and limitations. ACS Chem. Biol. 13, 36–44 (2018).

    Article  CAS  PubMed  Google Scholar 

  77. Sushko, I., Salmina, E., Potemkin, V. A., Poda, G. & Tetko, I. V. ToxAlerts: a web server of structural alerts for toxic chemicals and compounds with potential adverse reactions. J. Chem. Inf. Model. 52, 2310–2316 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Yang, H., Lou, C., Li, W., Liu, G. & Tang, Y. Computational approaches to identify structural alerts and their applications in environmental toxicology and drug discovery. Chem. Res. Toxicol. 33, 1312–1322 (2020).

    Article  CAS  PubMed  Google Scholar 

  79. OCHEM ToxAlerts. https://ochem.eu/alerts/home.do (2024).

  80. Irwin, J. J. et al. ZINC20 — a free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065–6073 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. ZINC20 patterns. https://zinc20.docking.org/patterns/ (2024).

  82. Lajiness, M. S., Maggiora, G. M. & Shanmugasundaram, V. Assessment of the consistency of medicinal chemists in reviewing sets of compounds. J. Med. Chem. 47, 4891–4896 (2004).

    Article  CAS  PubMed  Google Scholar 

  83. Ekins, S. et al. Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis. Mol. BioSyst. 6, 2316–2324 (2010).

    Article  CAS  PubMed  Google Scholar 

  84. Chai, C. L. & Mátyus, P. One size does not fit all: challenging some dogmas and taboos in drug discovery. Future Med. Chem. 8, 29–38 (2016).

    Article  CAS  PubMed  Google Scholar 

  85. Dantas, R. F. et al. Dealing with frequent hitters in drug discovery: a multidisciplinary view on the issue of filtering compounds on biological screenings. Expert Opin. Drug Discov. 14, 1269-1282 (2019).

    Article  CAS  PubMed  Google Scholar 

  86. Alfonso, L. F., Srivenugopal, K. S. & Bhat, G. J. Does aspirin acetylate multiple cellular proteins? (Review). Mol. Med. Rep. 2, 533–537 (2009).

    CAS  PubMed  Google Scholar 

  87. Ehmki, E. S. R., Schmidt, R., Ohm, F. & Rarey, M. Comparing molecular patterns using the example of SMARTS: applications and filter collection analysis. J. Chem. Inf. Model. 59, 2572–2586 (2019).

    Article  CAS  PubMed  Google Scholar 

  88. Schmidt, R. et al. Comparing molecular patterns using the example of SMARTS: theory and algorithms. J. Chem. Inf. Model. 59, 2560–2571 (2019).

    Article  CAS  PubMed  Google Scholar 

  89. Tropsha, A., Gramatica, P. & Gombar, V. K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb. Sci. 22, 69–77 (2003).

    Article  CAS  Google Scholar 

  90. Netzeva, T. I. et al. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52. Altern. Lab. Anim. 33, 155–173 (2005).

    Article  CAS  PubMed  Google Scholar 

  91. Hu, Y. & Bajorath, J. High-resolution view of compound promiscuity. F1000Research 2, 144 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Jasial, S., Gilberg, E., Blaschke, T. & Bajorath, J. Machine learning distinguishes with high accuracy between pan-assay interference compounds that are promiscuous or represent dark chemical matter. J. Med. Chem. 61, 10255–10264 (2018).

    Article  CAS  PubMed  Google Scholar 

  93. Stork, C. et al. Hit Dexter: a machine-learning model for the prediction of frequent hitters. ChemMedChem 13, 564–571 (2018).

    Article  CAS  PubMed  Google Scholar 

  94. Stork, C., Chen, Y., Šícho, M. & Kirchmair, J. Hit Dexter 2.0: machine-learning models for the prediction of frequent hitters. J. Chem. Inf. Model. 59, 1030–1043 (2019).

    Article  CAS  PubMed  Google Scholar 

  95. Stork, C., Mathai, N. & Kirchmair, J. Computational prediction of frequent hitters in target-based and cell-based assays. Artif. Intell. Life Sci. 1, 100007 (2021).

    CAS  Google Scholar 

  96. Ghosh, D., Koch, U., Hadian, K., Sattler, M. & Tetko, I. V. Highly accurate filters to flag frequent hitters in AlphaScreen assays by suggesting their mechanism. Mol. Inform. 41, 2100151 (2022).

    Article  CAS  Google Scholar 

  97. Kruschke, J. K. Doing Bayesian Data Analysis: a Tutorial with R, JAGS, and Stan 2nd edn (Academic, 2015).

  98. Yongye, A. B. & Medina‐Franco, J. L. Toward an efficient approach to identify molecular scaffolds possessing selective or promiscuous compounds. Chem. Biol. Drug Des. 82, 367–375 (2013).

    Article  CAS  PubMed  Google Scholar 

  99. Goodwin, S., Shahtahmassebi, G. & Hanley, Q. S. Statistical models for identifying frequent hitters in high throughput screening. Sci. Rep. 10, 17200 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Yang, J. J. et al. Badapple: promiscuity patterns from noisy evidence. J. Cheminform. 8, 29 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Hu, Y. & Bajorath, J. Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. J. Chem. Inf. Model. 50, 2112–2118 (2010).

    Article  CAS  PubMed  Google Scholar 

  102. M Nissink, J. W. & Blackburn, S. Quantification of frequent-hitter behavior based on historical high-throughput screening data. Future Med. Chem. 6, 1113–1126 (2014).

    Article  PubMed  Google Scholar 

  103. Upadhyay, R., Phlypo, R., Saini, R. & Liwicki, M. Sharing to learn and learning to share — fitting together meta-learning, multi-task learning, and transfer learning: a meta review. Preprint at https://arxiv.org/abs/2111.12146 (2021).

  104. Walters, P. We need better benchmarks for machine learning in drug discovery. Practical Cheminformatics http://practicalcheminformatics.blogspot.com/2023/08/we-need-better-benchmarks-for-machine.html (2023).

  105. Mellin, W. D. Work with new electronic ‘brains’ opens field for army math experts. Hammond Times 65 (10 November 1957).

  106. Jiménez-Luna, J., Grisoni, F. & Schneider, G. Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2, 573–584 (2020).

    Article  Google Scholar 

  107. Alves, V. et al. Lies and liabilities: computational assessment of high-throughput screening hits to identify artifact compounds. J. Med. Chem. 66, 12828–12839 (2023).

    Article  CAS  PubMed  Google Scholar 

  108. Blaschke, T., Feldmann, C. & Bajorath, J. Prediction of promiscuity cliffs using machine learning. Mol. Inform. 40, 2000196 (2021).

    Article  CAS  PubMed  Google Scholar 

  109. Gilberg, E., Stumpfe, D. & Bajorath, J. Activity profiles of analog series containing pan assay interference compounds. RSC Adv. 7, 35638–35647 (2017).

    Article  CAS  Google Scholar 

  110. Choo, M. Z. Y. & Chai, C. L. L. Promoting GAINS (give attention to limitations in assays) over PAINS alerts: no pains, more gains. ChemMedChem 17, e202100710 (2022).

    Article  CAS  PubMed  Google Scholar 

  111. Jasial, S., Hu, Y. & Bajorath, J. How frequently are pan-assay interference compounds active? Large-scale analysis of screening data reveals diverse activity profiles, low global hit frequency, and many consistently inactive compounds. J. Med. Chem. 60, 3879–3886 (2017).

    Article  CAS  PubMed  Google Scholar 

  112. Berthold, M. R. et al. in Studies in Classification, Data Analysis, and Knowledge Organization (eds Preisach, C. et al.) 319–326 (Springer, 2007).

  113. Brenke, J. K. et al. Identification of small-molecule frequent hitters of glutathione S-transferase–glutathione interaction. SLAS Discov. 21, 596–607 (2016).

    Article  CAS  Google Scholar 

  114. Huth, J. R. et al. ALARM NMR: a rapid and robust experimental method to detect reactive false positives in biochemical screens. J. Am. Chem. Soc. 127, 217–224 (2005).

    Article  CAS  PubMed  Google Scholar 

  115. Metz, J. T., Huth, J. R. & Hajduk, P. J. Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups. J. Comput. Aided Mol. Des. 21, 139–144 (2007).

    Article  CAS  PubMed  Google Scholar 

  116. Walters, W. P., Stahl, M. T. & Murcko, M. A. Virtual screening — an overview. Drug Discov. Today 3, 160–178 (1998).

    Article  CAS  Google Scholar 

  117. Yang, Z.-Y., Yang, Z.-J., Lu, A.-P., Hou, T.-J. & Cao, D.-S. Scopy: an integrated negative design Python library for desirable HTS/VS database design. Brief. Bioinform. 22, bbaa194 (2021).

    Article  PubMed  Google Scholar 

  118. Blaschke, T., Miljković, F. & Bajorath, J. Prediction of different classes of promiscuous and nonpromiscuous compounds using machine learning and nearest neighbor analysis. ACS Omega 4, 6883–6890 (2019).

    Article  CAS  Google Scholar 

  119. Couronne, C., Koptelov, M. & Zimmermann, A. in Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track Vol. 12461 (eds Dong, Y. et al.) 570–573 (Springer, 2021).

  120. Schneider, P., Röthlisberger, M., Reker, D. & Schneider, G. Spotting and designing promiscuous ligands for drug discovery. Chem. Commun. 52, 1135–1138 (2016).

    Article  CAS  Google Scholar 

  121. De Matos, A. M. et al. Glucosylpolyphenols as inhibitors of Aβ-induced Fyn kinase activation and tau phosphorylation: synthesis, membrane permeability, and exploratory target assessment within the scope of type 2 diabetes and Alzheimer’s disease. J. Med. Chem. 63, 11663–11690 (2020).

    Article  PubMed  Google Scholar 

  122. Bajorath, J. Evolution of assay interference concepts in drug discovery. Expert Opin. Drug Discov. 16, 719–721 (2021).

    Article  CAS  PubMed  Google Scholar 

  123. Feldmann, C. & Bajorath, J. Advances in computational polypharmacology. Mol. Inform. 41, 2200190 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Enamine HTS Collection. https://enamine.net/compound-collections/screening-collection/hts-collection (2024).

  125. Tritsch, D., Zinglé, C., Rohmer, M. & Grosdemange-Billiard, C. Flavonoids: true or promiscuous inhibitors of enzyme? The case of deoxyxylulose phosphate reductoisomerase. Bioorg. Chem. 59, 140–144 (2015).

    Article  CAS  PubMed  Google Scholar 

  126. Yang, Z.-Y. et al. Structural analysis and identification of colloidal aggregators in drug discovery. J. Chem. Inf. Model. 59, 3714–3726 (2019).

    Article  CAS  PubMed  Google Scholar 

  127. O’Donnell, H. R., Tummino, T. A., Bardine, C., Craik, C. S. & Shoichet, B. K. Colloidal aggregators in biochemical SARS-CoV-2 repurposing screens. J. Med. Chem. 64, 17530–17539 (2021).

    Article  PubMed  Google Scholar 

  128. SCAM Detective. https://scamdetective.mml.unc.edu/ (2024).

  129. Lee, K. et al. Combating small-molecule aggregation with machine learning. Cell Rep. Phys. Sci. 2, 100573 (2021).

    Article  Google Scholar 

  130. Hughes, T. B., Dang, N. L., Miller, G. P. & Swamidass, S. J. Modelling reactivity to biological macromolecules with a deep multitask network. ACS Cent. Sci. 2, 529–537 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Alsibaee, A. M., Aljohar, H. I., Attwa, M. W., Abdelhameed, A. S. & Kadi, A. A. Reactive intermediates formation and bioactivation pathways of spebrutinib revealed by LC-MS/MS: in vitro and in silico metabolic study. Heliyon 9, e17058 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Al-Shakliah, N. S., Kadi, A. A., Aljohar, H. I., AlRabiah, H. & Attwa, M. W. Profiling of in vivo, in vitro and reactive zorifertinib metabolites using liquid chromatography ion trap mass spectrometry. RSC Adv. 12, 20991–21003 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Alsubi, T. A., Attwa, M. W., Darwish, H. W., Abuelizz, H. A. & Kadi, A. A. Piperazine ring toxicity in three novel anti-breast cancer drugs: an in silico and in vitro metabolic bioactivation approach using olaparib as a case study. Naunyn Schmiedebergs Arch. Pharmacol. 396, 1435–1450 (2023).

    Article  CAS  PubMed  Google Scholar 

  134. Hughes, T. B., Flynn, N., Dang, N. L. & Swamidass, S. J. Modelling the bioactivation and subsequent reactivity of drugs. Chem. Res. Toxicol. 34, 584–600 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Borrel, A. et al. InterPred: a webtool to predict chemical autofluorescence and luminescence interference. Nucleic Acids Res. 48, W586–W590 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Thomas, R. The US Federal Tox21 Program: a strategic and operational plan for continued leadership. ALTEX 35, 163–168 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Wassermann, A. M. et al. Dark chemical matter as a promising starting point for drug lead discovery. Nat. Chem. Biol. 11, 958–966 (2015).

    Article  CAS  PubMed  Google Scholar 

  138. NCATS. NPACT Chemical Library — innovative chemical biology library for translational sciences. https://ncats.nih.gov/preclinical/core/compound/npact (2024).

  139. ChemFH — integrated online platform for the identification of potential frequent hitters. https://chemfh.scbdd.com/ (2024).

  140. RDKit. https://www.rdkit.org/ (2024).

  141. Mathai, N., Chen, Y. & Kirchmair, J. Validation strategies for target prediction methods. Brief. Bioinform. 21, 791–802 (2020).

    Article  CAS  PubMed  Google Scholar 

  142. Hanser, T. Federated learning for molecular discovery. Curr. Opin. Struct. Biol. 79, 102545 (2023).

    Article  CAS  PubMed  Google Scholar 

  143. Heyndrickx, W. et al. MELLODDY: cross-pharma federated learning at unprecedented scale unlocks benefits in QSAR without compromising proprietary information. J. Chem. Inf. Model. https://doi.org/10.1021/acs.jcim.3c00799 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  144. FAF-Drugs4. https://mobyle2.rpbs.univ-paris-diderot.fr (2024).

  145. KNIME_MedChem_filters. https://gitlab.com/Jukic/knime_medchem_filters/ (2024).

  146. SmartsFilter. https://datascience.unm.edu/tomcat/biocomp/smartsfilter (2024).

  147. SwissADME. http://www.swissadme.ch/ (2024).

  148. RDKit PAINS filter. https://www.rdkit.org/ (2024).

  149. Hit Dexter. https://nerdd.univie.ac.at/hitdexter3/ (2024).

  150. Lilly Rules. https://github.com/IanAWatson/Lilly-Medchem-Rules (2024).

  151. NIBR substructure filters for hit finding and triaging. https://github.com/rdkit/rdkit/tree/master/Contrib/NIBRSubstructureFilters (2024).

  152. RDKit NIBR substructure filters for hit finding and triaging. https://www.rdkit.org/ (2024).

  153. Badapple. https://datascience.unm.edu/tomcat/badapple/badapple (2024).

  154. Jasial, S., Gilberg, E., Blaschke, T. & Bajorath, J. Distinguishing between pan assay interference compounds (PAINS) that are promiscuous or represent dark chemical matter — data set and prediction models. Zenodo https://zenodo.org/record/1453913 (2018).

  155. Blaschke, T., Feldman, C. & Bajorath, J. Prediction of promiscuity cliffs using machine learning. Zenodo https://zenodo.org/record/4013954 (2020).

  156. OCHEM model for frequent hitter prediction in AlphaScreen assays. https://ochem.eu/article/125278 (2024).

  157. ChemAgg. https://admet.scbdd.com/ChemAGG/index/ (2024).

  158. DeepSCAMs. https://github.com/tcorodrigues/DeepSCAMs (2024).

  159. Isometric Stratified Ensembles (ISE). https://pikairos.eu/download/aggregation_classification/ (2024).

  160. InterPred. https://sandbox.ntp.niehs.nih.gov/interferences/ (2024).

  161. OCHEM Luciferase Advisor model. https://ochem.eu/model/697 (2024).

  162. Mangal, M., Sagar, P., Singh, H., Raghava, G. P. S. & Agarwal, S. M. NPACT: naturally occurring plant-based anti-cancer compound-activity-target database. Nucleic Acids Res. 41, D1124–D1129 (2013).

    Article  CAS  PubMed  Google Scholar 

  163. LiabilityPredictor. https://liability.mml.unc.edu/ (2024).

  164. Guha, R. et al. Exploratory analysis of kinetic solubility measurements of a small molecule library. Bioorg. Med. Chem. 19, 4127–4134 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Chen, C. et al. Fragment-based drug nanoaggregation reveals drivers of self-assembly. Nat. Commun. 14, 8340 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Hann, M. M. Molecular obesity, potency and other addictions in drug discovery. MedChemComm 2, 349 (2011).

    Article  CAS  Google Scholar 

  167. Azzaoui, K. et al. Modelling promiscuity based on in vitro safety pharmacology profiling data. ChemMedChem 2, 874–880 (2007).

    Article  CAS  PubMed  Google Scholar 

  168. Gleeson, M. P., Hersey, A., Montanari, D. & Overington, J. Probing the links between in vitro potency, ADMET and physicochemical parameters. Nat. Rev. Drug Discov. 10, 197–208 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Peters, J.-U. et al. Can we discover pharmacological promiscuity early in the drug discovery process? Drug Discov. Today 17, 325–335 (2012).

    Article  PubMed  Google Scholar 

  170. Hu, Y. & Bajorath, J. Compound promiscuity: what can we learn from current data? Drug Discov. Today 18, 644–650 (2013).

    Article  CAS  PubMed  Google Scholar 

  171. Waring, M. J. Lipophilicity in drug discovery. Expert Opin. Drug Discov. 5, 235–248 (2010).

    Article  CAS  PubMed  Google Scholar 

  172. Schneider, P. & Schneider, G. Privileged structures revisited. Angew. Chem. Int. Ed. 56, 7971–7974 (2017).

    Article  CAS  Google Scholar 

  173. DeSimone, R., Currie, K., Mitchell, S., Darrow, J. & Pippin, D. Privileged structures: applications in drug discovery. Comb. Chem. High Throughput Screen. 7, 473–493 (2004).

    Article  CAS  PubMed  Google Scholar 

  174. Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682–690 (2008).

    Article  CAS  PubMed  Google Scholar 

  175. Anighoro, A., Bajorath, J. & Rastelli, G. Polypharmacology: challenges and opportunities in drug discovery: miniperspective. J. Med. Chem. 57, 7874–7887 (2014).

    Article  CAS  PubMed  Google Scholar 

  176. Bajorath, J. Origins and progression of the polypharmacology concept in drug discovery. Artif. Intell. Life Sci. 5, 100094 (2024).

    CAS  Google Scholar 

  177. Evans, B. E. et al. Methods for drug discovery: development of potent, selective, orally effective cholecystokinin antagonists. J. Med. Chem. 31, 2235–2246 (1988).

    Article  CAS  PubMed  Google Scholar 

  178. Feng, B. Y. & Shoichet, B. K. A detergent-based assay for the detection of promiscuous inhibitors. Nat. Protoc. 1, 550–553 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Reker, D., Bernardes, G. J. L. & Rodrigues, T. Computational advances in combating colloidal aggregation in drug discovery. Nat. Chem. 11, 402–418 (2019).

    Article  CAS  PubMed  Google Scholar 

  180. Coan, K. E. D. & Shoichet, B. K. Stability and equilibria of promiscuous aggregates in high protein milieus. Mol. Biosyst. 3, 208 (2007).

    Article  CAS  PubMed  Google Scholar 

  181. Coan, K. E. D., Maltby, D. A., Burlingame, A. L. & Shoichet, B. K. Promiscuous aggregate-based inhibitors promote enzyme unfolding. J. Med. Chem. 52, 2067–2075 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Coan, K. E. D. & Shoichet, B. K. Stoichiometry and physical chemistry of promiscuous aggregate-based inhibitors. J. Am. Chem. Soc. 130, 9606–9612 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. Blevitt, J. M. et al. Structural basis of small-molecule aggregate induced inhibition of a protein–protein interaction. J. Med. Chem. 60, 3511–3517 (2017).

    Article  CAS  PubMed  Google Scholar 

  184. Blanusa, M., Varnai, V. M., Piasek, M. & Kostial, K. Chelators as antidotes of metal toxicity: therapeutic and experimental aspects. Curr. Med. Chem. 12, 2771–2794 (2005).

    Article  CAS  PubMed  Google Scholar 

  185. Repac Antić, D., Parčina, M., Gobin, I. & Petković Didović, M. Chelation in antibacterial drugs: from nitroxoline to cefiderocol and beyond. Antibiotics 11, 1105 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  186. Feng, B. Y. et al. A high-throughput screen for aggregation-based inhibition in a large compound library. J. Med. Chem. 50, 2385–2390 (2007).

    Article  CAS  PubMed  Google Scholar 

  187. Shoichet, B. K. Interpreting steep dose-response curves in early inhibitor discovery. J. Med. Chem. 49, 7274–7277 (2006).

    Article  CAS  PubMed  Google Scholar 

  188. McGovern, S. L., Helfand, B. T., Feng, B. & Shoichet, B. K. A specific mechanism of nonspecific inhibition. J. Med. Chem. 46, 4265–4272 (2003).

    Article  CAS  PubMed  Google Scholar 

  189. Feng, B. Y., Shelat, A., Doman, T. N., Guy, R. K. & Shoichet, B. K. High-throughput assays for promiscuous inhibitors. Nat. Chem. Biol. 1, 146–148 (2005).

    Article  CAS  PubMed  Google Scholar 

  190. LaPlante, S. R. et al. Compound aggregation in drug discovery: implementing a practical NMR assay for medicinal chemists. J. Med. Chem. 56, 5142–5150 (2013).

    Article  CAS  PubMed  Google Scholar 

  191. Feng, B. Y. et al. Small-molecule aggregates inhibit amyloid polymerization. Nat. Chem. Biol. 4, 197–199 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Giannetti, A. M., Koch, B. D. & Browner, M. F. Surface plasmon resonance based assay for the detection and characterization of promiscuous inhibitors. J. Med. Chem. 51, 574–580 (2008).

    Article  CAS  PubMed  Google Scholar 

  193. Singh, J., Petter, R. C., Baillie, T. A. & Whitty, A. The resurgence of covalent drugs. Nat. Rev. Drug Discov. 10, 307–317 (2011).

    Article  CAS  PubMed  Google Scholar 

  194. Kwiatkowski, N. et al. Targeting transcription regulation in cancer with a covalent CDK7 inhibitor. Nature 511, 616–620 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. Ang, K. K. H. et al. Mining a cathepsin inhibitor library for new antiparasitic drug leads. PLoS Negl. Trop. Dis. 5, e1023 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  196. Arnold, L. A. et al. Discovery of small molecule inhibitors of the interaction of the thyroid hormone receptor with transcriptional coregulators. J. Biol. Chem. 280, 43048–43055 (2005).

    Article  CAS  PubMed  Google Scholar 

  197. Copeland, R. A., Basavapathruni, A., Moyer, M. & Scott, M. P. Impact of enzyme concentration and residence time on apparent activity recovery in jump dilution analysis. Anal. Biochem. 416, 206–210 (2011).

    Article  CAS  PubMed  Google Scholar 

  198. Copeland, R. A. Evaluation of Enzyme Inhibitors in Drug Discovery: a Guide for Medicinal Chemists and Pharmacologists (Wiley, 2013).

  199. Ehmann, D. E. et al. Avibactam is a covalent, reversible, non-β-lactam β-lactamase inhibitor. Proc. Natl Acad. Sci. USA 109, 11663–11668 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  200. Johnston, P. A. et al. Development of a 384-well colorimetric assay to quantify hydrogen peroxide generated by the redox cycling of compounds in the presence of reducing agents. ASSAY Drug Dev. Technol. 6, 505–518 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Simeonov, A. et al. Fluorescence spectroscopic profiling of compound libraries. J. Med. Chem. 51, 2363–2371 (2008).

    Article  CAS  PubMed  Google Scholar 

  202. Imbert, P.-E. et al. Recommendations for the reduction of compound artifacts in time-resolved fluorescence resonance energy transfer assays. ASSAY Drug Dev. Technol. 5, 363–372 (2007).

    Article  CAS  PubMed  Google Scholar 

  203. Simeonov, A. et al. Quantitative high-throughput screen identifies inhibitors of the Schistosoma mansoni redox cascade. PLoS Negl. Trop. Dis. 2, e127 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  204. Baljinnyam, B., Ronzetti, M. & Simeonov, A. Advances in luminescence-based technologies for drug discovery. Expert Opin. Drug Discov. 18, 25–35 (2023).

    Article  CAS  PubMed  Google Scholar 

  205. Auld, D. S. et al. Molecular basis for the high-affinity binding and stabilization of firefly luciferase by PTC124. Proc. Natl Acad. Sci. USA 107, 4878–4883 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  206. Inglese, J. et al. Genome editing-enabled HTS assays expand drug target pathways for Charcot–Marie–Tooth disease. ACS Chem. Biol. 9, 2594–2602 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Cheng, K. C.-C. & Inglese, J. A coincidence reporter-gene system for high-throughput screening. Nat. Methods 9, 937–937 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  208. Hasson, S. A. et al. Chemogenomic profiling of endogenous PARK2 expression using a genome-edited coincidence reporter. ACS Chem. Biol. 10, 1188–1197 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  209. Lang, L. & Teng, Y. in Clinical and Preclinical Models for Maximizing Healthspan Vol. 2138 (ed. Guest, P. C.) 159–166 (Springer, 2020).

  210. Bender, A. et al. Evaluation guidelines for machine learning tools in the chemical sciences. Nat. Rev. Chem. 6, 428–442 (2022).

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors thank M. Brenek from the University of Vienna for her help in elaborating drafts of the figures presented in this publication. The financial support received for the Christian Doppler Laboratory for Molecular Informatics in the Biosciences by the Austrian Federal Ministry of Labour and Economy, the National Foundation for Research, Technology and Development, the Christian Doppler Research Association, BASF SE and Boehringer-Ingelheim RCV GmbH & Co KG, as well as the funding received for V.P. from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Actions, grant agreement ‘Advanced machine learning for Innovative Drug Discovery (AIDD)’ no. 956832, is gratefully acknowledged.

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  1. Conrad Stork

    Present address: BASF SE, Ludwigshafen am Rhein, Germany

Authors and Affiliations

  1. Drug Discovery Sciences, Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria

    Lu Tan

  2. Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria

    Steffen Hirte, Vincenzo Palmacci & Johannes Kirchmair

  3. Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, Vienna, Austria

    Steffen Hirte & Vincenzo Palmacci

  4. Department of Informatics, Center for Bioinformatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany

    Conrad Stork

  5. Christian Doppler Laboratory for Molecular Informatics in the Biosciences, Department for Pharmaceutical Sciences, University of Vienna, Vienna, Austria

    Johannes Kirchmair

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C.S. and J.K. are developers of the Hit Dexter machine learning models for frequent hitter prediction.

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Tan, L., Hirte, S., Palmacci, V. et al. Tackling assay interference associated with small molecules. Nat Rev Chem (2024). https://doi.org/10.1038/s41570-024-00593-3

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