Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.
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D.R. is a Swiss National Science Foundation Fellow (Grants P2EZP3_168827 and P300P2_177833). G.J.L.B. is a Royal Society URF (UF110046 and URF/R/180019), an iFCT Investigator (IF/00624/2015), and the recipient of an ERC StG (TagIt, Grant Agreement 676832). T.R. and G.J.L.B. acknowledge Marie Sklodowska-Curie ITN Protein Conjugates (Grant Agreement 675007) for funding. T.R. is a Marie Curie Fellow (Grant Agreement 743640). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant Agreement 807281) and POR Lisboa 2020/FEDER (02/SAICT/2017, Grant Agreement Lisboa-01-0145-FEDER-028333) for funding. D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding.