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PAIN(S) relievers for medicinal chemists: how computational methods can assist in hit evaluation

In this editorial, brought to MedChemNet members by Future Medicinal Chemistry, authors Conrad Stork and Johannes Kirchmair discuss the use of computational methods to assist hit evaluation.

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Modern high-throughput screening technologies allow for the testing of tens of thousands of compounds per day. However, a substantial proportion of the initial hits can be artifacts related to aggregate formation [1], chemical reactivity, photoreactivity, redox activity, metal chelation, interference with assay spectroscopy, membrane disruption, decomposition in buffers and other mechanisms [2–4].

The seminal works by the Shoichet group on aggregators [1] and by Baell and Holloway on pan-assay interference compounds (PAINS) [2] have greatly increased the scientific community's awareness of the pollution of medicinal chemistry and chemical biology literature with ‘bad actors’ and ‘frequent hitters’. Less present in discussions but not of lower significance are impurities and decomposition products as sources of assay interference [15,16].

Recently, the editors-in-chief of nine ACS journals have teamed up to define best practice guidelines for how to identify assay artifacts and reject such hits [4]. Recommendations include the measurement and publication of full concentration response curves as well as the use of reporter-free methods such as surface plasmon resonance.

At this point, it is important to note that frequent hitters are not necessarily bad actors and vice versa. Frequent hitters are compounds which have a higher-than-expected activity rate recorded in historical screening data. Bad actors, on the other hand, are compounds that trigger false assay readouts under specific conditions and therefore often, but by far not always, show a high frequency of false readouts. In addition to some bad actors, frequent hitters also include true promiscuous compounds (sometimes related to privileged scaffolds) that may in fact be of interest in the context of polypharmacology and drug repurposing.

Computational methods can make a significant contribution to the identification of potential bad actors and/or frequent hitters. These computational techniques include rule-based and similarity-based methods, statistical approaches and machine learning. Here, we will briefly discuss the most relevant approaches that are publicly accessible.

Read the full editorial here

Stork C and Kirchmair J. PAIN(S) relivers for medicinal chemists: how computational methods can assist hit evaluationFut. Med. Chem. (Epub ahead of print) doi:10.4155/fmc-2018-0116 (2018)


  1. McGovern SL, Caselli E, Grigorieff N, Shoichet BK. A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screeningJ. Med. Chem. 45(8), 1712–1722 (2002)
  2. Baell JB, Holloway GA. New substructure filters for removal of pan-assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J. Med. Chem. 53(7), 2719–2740 (2010)
  3. Baell JB, Nissink JWM. Seven year itch: pan-assay interference compounds (PAINS) in 2017-utility and limitations. ACS Chem. Biol. 13(1), 36–44 (2017)
  4. Aldrich C, Bertozzi C, Georg GI et al. The ecstasy and agony of assay interference compounds. J. Med. Chem. 60(6), 2165–2168 (2017)
  5. Chakravorty SJ, Chan J, Greenwood MN et al. Nuisance compounds, PAINS filters, and dark chemical matter in the GSK HTS collection. SLAS Discov. 23(6), 532–545 (2018)
  6. Hermann JC, Chen Y, Wartchow C et al. Metal impurities cause false positives in high-throughput screening campaigns. ACS Med. Chem. Lett. 4(2), 197–200 (2013)
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Future Medicinal Chemistry

Journal, Future Science Group

Future Medicinal Chemistry provides a monthly point of access to commentary and debate for this ever-expanding and diversifying community. The journal showcases milestones in pharmaceutical R&D and features expert analysis of emerging research – from the identification of targets, through to the discovery, design, synthesis and evaluation of bioactive agents.

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