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Pan-Assay Interference Compounds (PAINS) - what they are & why they matter

posted 10 Mar 2015 by Oakley Cox

PAINS compounds are a real issue which we faced as part of developing our ChemiReg application - here are some thoughts from Oakley Cox, a Chemistry DPhil student at the University Of Oxford.

Pan-Assay Interference Compounds (PAINS) are defined by their ability to show activity across a range of assay platforms and against a range of proteins. The most common causes of PAINS activity are metal chelation, chemical aggregation, redox activity, compound fluorescence, cysteine oxidation or promiscuous binding. Many PAINS have multiple functionalities, causing different types of interference and resulting in in vitro and in vivo activity.

Why are they a problem?

PAINS have been known for a number of years, and have been well documented in the past.1,2 Computational filters exist to remove known PAINS from chemical libraries and an experienced medicinal chemist will be quickly able to identify a PAINS-type structure. Even so, the scientific literature is plagued by publications containing ‘selective, potent chemical probes’ with clear PAINS-like structures (see Figure 1).

Compounds reported in the literature as bioactives, but which contain PAINS-like functionalities

Figure 1: Compounds reported in the literature as bioactives, but which contain PAINS-like functionalities.3-7

The pressure to produce impactful science on academics seems to be the most likely cause of PAINS publications. Biologists with little or no chemistry training are, understandably, unlikely to spot suspect structures, but know the inclusion of a small molecule ligand significantly improves the impact of their research. The prevalence of such research also indicates reviewers are not aware of the problem.

The problem is further compounded by the methods employed by academics to discover small molecule ligands. Commercial libraries are bought by research groups for convenience, meaning the same areas of chemical space are repeatedly explored. Computational PAINS filters are far from comprehensive and vendors still include many PAINS-type structures in their catalogues. Many researchers will knowingly screen PAINS to find valid starting points, citing the prevalence of PAINS features in approved drugs (around 7%) as evidence to back their strategy. Yet many of these drugs are special cases and were not developed using a modern screening triage.

How can the scientific community overcome the problem?

Every year, funders money is wasted following-up futile starting points or obtaining pointless intellectual property. Better awareness is needed of PAINS-like behaviour. Full characterisation and publication of PAINS if and when they are detected, akin to the work completed by Dahlin et al. earlier this year,8 would be a big step forwards. The results would quickly generate better understanding and improve cheminformatic filters.

Perhaps the most effective way to weed out PAINS publications would be to put pressure on reviewers and journal editors to spot problematic structures. It is relatively straightforward to spot PAINS (see Figure 2) and it would not be unreasonable for reviewers to ask for more rigorous evidence for an inhibitor to be described as selective and potent. At this point, it is important to emphasise the difference between rejecting a compound based on scientific evidence rather than simply dismissing a compound because it appears to be PAIN-like.

How a PAINS compound can be identified

Figure 2: How a PAINS compound can be identified.3

If you’re a researcher with an exciting new hit, how can you be sure it’s not a PAIN? Rigorous structure activity relationship (SAR) studies highlight the role of different parts of the compound for binding. Activity cliffs and nanomolar in vitro activity are good indicators of a genuine inhibitor. Synthesis of upward of 100 analogues would paint an accurate picture of a binding site as well as lead to improvements in both activity and selectivity. It may seem a daunting undertaking, but careful and well-planned SAR exploration can be both effective and attainable in an academic setting.

Oakley is a DPhil student studying at the Structural Genomics Consortium, University of Oxford. He is supervised by Dr Paul Brennan in the Target Discovery Institute (TDI) and is co-supervised by Prof Frank von Delft at Diamond Light Source. Read more here


  1. Walters, W. P.; Stahl, M. T.; Murcko, M. A. Drug Discovery Today 1998, 3, 160.
  2. Baell, J. B.; Holloway, G. A. J. Med. Chem. 2010, 53, 2719.
  3. Baell, J. B. ACS Med. Chem. Lett. 2015, Ahead of Print.
  4. Xin, M.; Li, R.; Xie, M.; Park, D.; Owonikoko, T. K.; Sica, G. L.; Corsino, P. E.; Zhou, J.; Ding, C.; White, M. A.; Magis, A. T.; Ramalingam, S. S.; Curran, W. J.; Khuri, F. R.; Deng, X. Nat. Commun. 2014, 5, 4935.
  5. Chen, F.; Liu, J.; Huang, M.; Hu, M.; Su, Y.; Zhang, X.-k. ACS Med. Chem. Lett. 2014, 5, 736.
  6. Evelyn, C. R.; Duan, X.; Biesiada, J.; Seibel, W. L.; Meller, J.; Zheng, Y. Chem. Biol. (Oxford, U. K.) 2014, 21, 1618.
  7. Nicolaes, G. A. F.; Kulharia, M.; Voorberg, J.; Kaijen, P. H.; Wroblewska, A.; Wielders, S.; Schrijver, R.; Sperandio, O.; Villoutreix, B. O. Blood 2014, 123, 113.
  8. Dahlin, J. L.; Nissink, J. W. M.; Strasser, J. M.; Francis, S.; Higgins, L.; Zhou, H.; Zhang, Z.; Walters, M. A. J. Med. Chem. 2015, Ahead of Print.
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