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.
Similar content being viewed by others
References
Sánchez-Ruiz, A. & Colmenarejo, G. Updated prediction of aggregators and assay-interfering substructures in food compounds. J. Agric. Food Chem. 69, 15184–15194 (2021).
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).
David, L. et al. Identification of compounds that interfere with high‐throughput screening assay technologies. ChemMedChem 14, 1795–1802 (2019).
Bisson, J. et al. Can invalid bioactives undermine natural product-based drug discovery? J. Med. Chem. 59, 1671–1690 (2016).
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).
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).
Coussens, N. P. et al. in Assay Guidance Manual (eds Markossian, S. et al.) 1067–1116 (NCATS, 2020).
Baell, J. & Walters, M. A. Chemistry: chemical con artists foil drug discovery. Nature 513, 481–483 (2014).
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).
Hermann, J. C. et al. Metal impurities cause false positives in high-throughput screening campaigns. ACS Med. Chem. Lett. 4, 197–200 (2013).
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).
Dahlin, J. L. et al. Nuisance compounds in cellular assays. Cell Chem. Biol. 28, 356–370 (2021).
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).
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).
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).
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).
Aldrich, C. et al. The ecstasy and agony of assay interference compounds. ACS Cent. Sci. 3, 143–147 (2017).
McCoy, M. A. et al. Biophysical survey of small-molecule β-catenin inhibitors: a cautionary tale. J. Med. Chem. 65, 7246–7261 (2022).
Dahlin, J. L. & Walters, M. A. How to triage PAINS-full research. ASSAY Drug Dev. Technol. 14, 168–174 (2016).
Arrowsmith, C. H. et al. The promise and peril of chemical probes. Nat. Chem. Biol. 11, 536–541 (2015).
Newman, D. J. Problems that can occur when assaying extracts to pure compounds in biological systems. Curr. Ther. Res. 95, 100645 (2021).
Kenny, P. W. Comment on the ecstasy and agony of assay interference compounds. J. Chem. Inf. Model. 57, 2640–2645 (2017).
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).
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).
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).
Hendrich, A. B. Flavonoid-membrane interactions: possible consequences for biological effects of some polyphenolic compounds. Acta Pharmacol. Sin. 27, 27–40 (2006).
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).
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).
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).
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).
Cassinelli, G. The roots of modern oncology: from discovery of new antitumor anthracyclines to their clinical use. Tumori J. 102, 226–235 (2016).
Simeonov, A. & Davis, M. I. in Assay Guidance Manual (eds Markossian, S. et al.) 1151–1162 (NCATS, 2004).
Auld, D. S. & Inglese, J. in Assay Guidance Manual (eds Markossian, S. et al.) 1163–1175 (NCATS, 2018).
Dahlin, J. L. & Walters, M. A. The essential roles of chemistry in high-throughput screening triage. Future Med. Chem. 6, 1265–1290 (2014).
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).
Auld, D. S. et al. in Assay Guidance Manual (eds Markossian, S. et al.) 1177–1202 (NCATS, 2017).
Dahlin, J. L., Baell, J. & Walters, M. A. in Assay Guidance Manual (eds. Markossian, S. et al.) 1117-1150 (NCATS, 2015).
Busby, S. A. et al. Advancements in assay technologies and strategies to enable drug discovery. ACS Chem. Biol. 15, 2636–2648 (2020).
Holdgate, G., Embrey, K., Milbradt, A. & Davies, G. Biophysical methods in early drug discovery. ADMET DMPK 7, 222–241 (2019).
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).
Kitchen, D. B. & Decornez, H. Y. in Small Molecule Medicinal Chemistry: Strategies and Technologies (eds Czechtizky, W. & Hamley, P.) Ch. 7 (Wiley, 2015).
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).
Schuffenhauer, A. et al. Evolution of Novartis’ small molecule screening deck design. J. Med. Chem. 63, 14425–14447 (2020).
Johnson, M. & Maggiora, G. (eds) Concepts and Applications of Molecular Similarity (Wiley, 1990).
Willett, P. The calculation of molecular structural similarity: principles and practice. Mol. Inform. 33, 403–413 (2014).
Borrel, A. et al. High-throughput screening to predict chemical-assay interference. Sci. Rep. 10, 3986 (2020).
Kenny, P. W. & Sadowski, J. in Methods and Principles in Medicinal Chemistry (ed. Oprea, T. I.) 271–285 (Wiley, 2005).
Wawer, M. & Bajorath, J. Local structural changes, global data views: graphical substructure–activity relationship trailing. J. Med. Chem. 54, 2944–2951 (2011).
Guha, R. & Van Drie, J. H. Structure–activity landscape index: identifying and quantifying. J. Chem. Inf. Model. 48, 646–658 (2008).
Lajiness, M. S. in QSAR: Rational Approaches to the Design of Bioactive Compounds (eds Silipo, C. & Vittoria, A.) 201–204 (Elsevier, 1990).
Medina‐Franco, J. L. Activity cliffs: facts or artifacts? Chem. Biol. Drug Des. 81, 553–556 (2013).
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).
Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).
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).
Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47, D930–D940 (2019).
ChEMBL Version 33. https://www.ebi.ac.uk/chembl/ (2023).
European Chemical Biology Database (ECBD). https://ecbd.eu/ (2024).
Alves, V. M. et al. SCAM Detective: accurate predictor of small, colloidally aggregating molecules. J. Chem. Inf. Model. 60, 4056–4063 (2020).
Yang, Z.-Y. et al. ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds. Brief. Bioinform. 22, bbaa282 (2021).
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).
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).
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).
Irwin, J. J. et al. An aggregation advisor for ligand discovery. J. Med. Chem. 58, 7076–7087 (2015).
Google Dataset Search. https://datasetsearch.research.google.com/ (2024).
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).
Yang, K. et al. Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59, 3370–3388 (2019).
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).
Daylight Chemical Information Systems. SMARTS theory manual. https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html (2023).
Bruns, R. F. & Watson, I. A. Rules for identifying potentially reactive or promiscuous compounds. J. Med. Chem. 55, 9763–9772 (2012).
Brenk, R. et al. Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem 3, 435–444 (2008).
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).
Chakravorty, S. J. et al. Nuisance compounds, PAINS filters, and dark chemical matter in the GSK HTS collection. SLAS Discov. 23, 532–544 (2018).
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).
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).
Schorpp, K. et al. Identification of small-molecule frequent hitters from AlphaScreen high-throughput screens. J. Biomol. Screen. 19, 715–726 (2014).
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).
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).
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).
OCHEM ToxAlerts. https://ochem.eu/alerts/home.do (2024).
Irwin, J. J. et al. ZINC20 — a free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065–6073 (2020).
ZINC20 patterns. https://zinc20.docking.org/patterns/ (2024).
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).
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).
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).
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).
Alfonso, L. F., Srivenugopal, K. S. & Bhat, G. J. Does aspirin acetylate multiple cellular proteins? (Review). Mol. Med. Rep. 2, 533–537 (2009).
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).
Schmidt, R. et al. Comparing molecular patterns using the example of SMARTS: theory and algorithms. J. Chem. Inf. Model. 59, 2560–2571 (2019).
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).
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).
Hu, Y. & Bajorath, J. High-resolution view of compound promiscuity. F1000Research 2, 144 (2013).
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).
Stork, C. et al. Hit Dexter: a machine-learning model for the prediction of frequent hitters. ChemMedChem 13, 564–571 (2018).
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).
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).
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).
Kruschke, J. K. Doing Bayesian Data Analysis: a Tutorial with R, JAGS, and Stan 2nd edn (Academic, 2015).
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).
Goodwin, S., Shahtahmassebi, G. & Hanley, Q. S. Statistical models for identifying frequent hitters in high throughput screening. Sci. Rep. 10, 17200 (2020).
Yang, J. J. et al. Badapple: promiscuity patterns from noisy evidence. J. Cheminform. 8, 29 (2016).
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).
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).
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).
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).
Mellin, W. D. Work with new electronic ‘brains’ opens field for army math experts. Hammond Times 65 (10 November 1957).
Jiménez-Luna, J., Grisoni, F. & Schneider, G. Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2, 573–584 (2020).
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).
Blaschke, T., Feldmann, C. & Bajorath, J. Prediction of promiscuity cliffs using machine learning. Mol. Inform. 40, 2000196 (2021).
Gilberg, E., Stumpfe, D. & Bajorath, J. Activity profiles of analog series containing pan assay interference compounds. RSC Adv. 7, 35638–35647 (2017).
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).
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).
Berthold, M. R. et al. in Studies in Classification, Data Analysis, and Knowledge Organization (eds Preisach, C. et al.) 319–326 (Springer, 2007).
Brenke, J. K. et al. Identification of small-molecule frequent hitters of glutathione S-transferase–glutathione interaction. SLAS Discov. 21, 596–607 (2016).
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).
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).
Walters, W. P., Stahl, M. T. & Murcko, M. A. Virtual screening — an overview. Drug Discov. Today 3, 160–178 (1998).
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).
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).
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).
Schneider, P., Röthlisberger, M., Reker, D. & Schneider, G. Spotting and designing promiscuous ligands for drug discovery. Chem. Commun. 52, 1135–1138 (2016).
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).
Bajorath, J. Evolution of assay interference concepts in drug discovery. Expert Opin. Drug Discov. 16, 719–721 (2021).
Feldmann, C. & Bajorath, J. Advances in computational polypharmacology. Mol. Inform. 41, 2200190 (2022).
Enamine HTS Collection. https://enamine.net/compound-collections/screening-collection/hts-collection (2024).
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).
Yang, Z.-Y. et al. Structural analysis and identification of colloidal aggregators in drug discovery. J. Chem. Inf. Model. 59, 3714–3726 (2019).
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).
SCAM Detective. https://scamdetective.mml.unc.edu/ (2024).
Lee, K. et al. Combating small-molecule aggregation with machine learning. Cell Rep. Phys. Sci. 2, 100573 (2021).
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).
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).
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).
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).
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).
Borrel, A. et al. InterPred: a webtool to predict chemical autofluorescence and luminescence interference. Nucleic Acids Res. 48, W586–W590 (2020).
Thomas, R. The US Federal Tox21 Program: a strategic and operational plan for continued leadership. ALTEX 35, 163–168 (2018).
Wassermann, A. M. et al. Dark chemical matter as a promising starting point for drug lead discovery. Nat. Chem. Biol. 11, 958–966 (2015).
NCATS. NPACT Chemical Library — innovative chemical biology library for translational sciences. https://ncats.nih.gov/preclinical/core/compound/npact (2024).
ChemFH — integrated online platform for the identification of potential frequent hitters. https://chemfh.scbdd.com/ (2024).
RDKit. https://www.rdkit.org/ (2024).
Mathai, N., Chen, Y. & Kirchmair, J. Validation strategies for target prediction methods. Brief. Bioinform. 21, 791–802 (2020).
Hanser, T. Federated learning for molecular discovery. Curr. Opin. Struct. Biol. 79, 102545 (2023).
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).
FAF-Drugs4. https://mobyle2.rpbs.univ-paris-diderot.fr (2024).
KNIME_MedChem_filters. https://gitlab.com/Jukic/knime_medchem_filters/ (2024).
SmartsFilter. https://datascience.unm.edu/tomcat/biocomp/smartsfilter (2024).
SwissADME. http://www.swissadme.ch/ (2024).
RDKit PAINS filter. https://www.rdkit.org/ (2024).
Hit Dexter. https://nerdd.univie.ac.at/hitdexter3/ (2024).
Lilly Rules. https://github.com/IanAWatson/Lilly-Medchem-Rules (2024).
NIBR substructure filters for hit finding and triaging. https://github.com/rdkit/rdkit/tree/master/Contrib/NIBRSubstructureFilters (2024).
RDKit NIBR substructure filters for hit finding and triaging. https://www.rdkit.org/ (2024).
Badapple. https://datascience.unm.edu/tomcat/badapple/badapple (2024).
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).
Blaschke, T., Feldman, C. & Bajorath, J. Prediction of promiscuity cliffs using machine learning. Zenodo https://zenodo.org/record/4013954 (2020).
OCHEM model for frequent hitter prediction in AlphaScreen assays. https://ochem.eu/article/125278 (2024).
ChemAgg. https://admet.scbdd.com/ChemAGG/index/ (2024).
DeepSCAMs. https://github.com/tcorodrigues/DeepSCAMs (2024).
Isometric Stratified Ensembles (ISE). https://pikairos.eu/download/aggregation_classification/ (2024).
InterPred. https://sandbox.ntp.niehs.nih.gov/interferences/ (2024).
OCHEM Luciferase Advisor model. https://ochem.eu/model/697 (2024).
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).
LiabilityPredictor. https://liability.mml.unc.edu/ (2024).
Guha, R. et al. Exploratory analysis of kinetic solubility measurements of a small molecule library. Bioorg. Med. Chem. 19, 4127–4134 (2011).
Chen, C. et al. Fragment-based drug nanoaggregation reveals drivers of self-assembly. Nat. Commun. 14, 8340 (2023).
Hann, M. M. Molecular obesity, potency and other addictions in drug discovery. MedChemComm 2, 349 (2011).
Azzaoui, K. et al. Modelling promiscuity based on in vitro safety pharmacology profiling data. ChemMedChem 2, 874–880 (2007).
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).
Peters, J.-U. et al. Can we discover pharmacological promiscuity early in the drug discovery process? Drug Discov. Today 17, 325–335 (2012).
Hu, Y. & Bajorath, J. Compound promiscuity: what can we learn from current data? Drug Discov. Today 18, 644–650 (2013).
Waring, M. J. Lipophilicity in drug discovery. Expert Opin. Drug Discov. 5, 235–248 (2010).
Schneider, P. & Schneider, G. Privileged structures revisited. Angew. Chem. Int. Ed. 56, 7971–7974 (2017).
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).
Hopkins, A. L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682–690 (2008).
Anighoro, A., Bajorath, J. & Rastelli, G. Polypharmacology: challenges and opportunities in drug discovery: miniperspective. J. Med. Chem. 57, 7874–7887 (2014).
Bajorath, J. Origins and progression of the polypharmacology concept in drug discovery. Artif. Intell. Life Sci. 5, 100094 (2024).
Evans, B. E. et al. Methods for drug discovery: development of potent, selective, orally effective cholecystokinin antagonists. J. Med. Chem. 31, 2235–2246 (1988).
Feng, B. Y. & Shoichet, B. K. A detergent-based assay for the detection of promiscuous inhibitors. Nat. Protoc. 1, 550–553 (2006).
Reker, D., Bernardes, G. J. L. & Rodrigues, T. Computational advances in combating colloidal aggregation in drug discovery. Nat. Chem. 11, 402–418 (2019).
Coan, K. E. D. & Shoichet, B. K. Stability and equilibria of promiscuous aggregates in high protein milieus. Mol. Biosyst. 3, 208 (2007).
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).
Coan, K. E. D. & Shoichet, B. K. Stoichiometry and physical chemistry of promiscuous aggregate-based inhibitors. J. Am. Chem. Soc. 130, 9606–9612 (2008).
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).
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).
Repac Antić, D., Parčina, M., Gobin, I. & Petković Didović, M. Chelation in antibacterial drugs: from nitroxoline to cefiderocol and beyond. Antibiotics 11, 1105 (2022).
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).
Shoichet, B. K. Interpreting steep dose-response curves in early inhibitor discovery. J. Med. Chem. 49, 7274–7277 (2006).
McGovern, S. L., Helfand, B. T., Feng, B. & Shoichet, B. K. A specific mechanism of nonspecific inhibition. J. Med. Chem. 46, 4265–4272 (2003).
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).
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).
Feng, B. Y. et al. Small-molecule aggregates inhibit amyloid polymerization. Nat. Chem. Biol. 4, 197–199 (2008).
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).
Singh, J., Petter, R. C., Baillie, T. A. & Whitty, A. The resurgence of covalent drugs. Nat. Rev. Drug Discov. 10, 307–317 (2011).
Kwiatkowski, N. et al. Targeting transcription regulation in cancer with a covalent CDK7 inhibitor. Nature 511, 616–620 (2014).
Ang, K. K. H. et al. Mining a cathepsin inhibitor library for new antiparasitic drug leads. PLoS Negl. Trop. Dis. 5, e1023 (2011).
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).
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).
Copeland, R. A. Evaluation of Enzyme Inhibitors in Drug Discovery: a Guide for Medicinal Chemists and Pharmacologists (Wiley, 2013).
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).
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).
Simeonov, A. et al. Fluorescence spectroscopic profiling of compound libraries. J. Med. Chem. 51, 2363–2371 (2008).
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).
Simeonov, A. et al. Quantitative high-throughput screen identifies inhibitors of the Schistosoma mansoni redox cascade. PLoS Negl. Trop. Dis. 2, e127 (2008).
Baljinnyam, B., Ronzetti, M. & Simeonov, A. Advances in luminescence-based technologies for drug discovery. Expert Opin. Drug Discov. 18, 25–35 (2023).
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).
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).
Cheng, K. C.-C. & Inglese, J. A coincidence reporter-gene system for high-throughput screening. Nat. Methods 9, 937–937 (2012).
Hasson, S. A. et al. Chemogenomic profiling of endogenous PARK2 expression using a genome-edited coincidence reporter. ACS Chem. Biol. 10, 1188–1197 (2015).
Lang, L. & Teng, Y. in Clinical and Preclinical Models for Maximizing Healthspan Vol. 2138 (ed. Guest, P. C.) 159–166 (Springer, 2020).
Bender, A. et al. Evaluation guidelines for machine learning tools in the chemical sciences. Nat. Rev. Chem. 6, 428–442 (2022).
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.
Author information
Authors and Affiliations
Contributions
All authors researched data for the article, contributed substantially to discussion of the content, wrote the article, and reviewed and edited the manuscript before submission.
Corresponding author
Ethics declarations
Competing interests
C.S. and J.K. are developers of the Hit Dexter machine learning models for frequent hitter prediction.
Peer review
Peer review information
Nature Reviews Chemistry thanks Jose L. Medina-Franco, Alexander Tropsha and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tan, L., Hirte, S., Palmacci, V. et al. Tackling assay interference associated with small molecules. Nat Rev Chem 8, 319–339 (2024). https://doi.org/10.1038/s41570-024-00593-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41570-024-00593-3
- Springer Nature Limited