Computational advances in combating colloidal aggregation in drug discovery

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. Biochemical and cellular assays are often plagued by false positive readouts elicited by nuisance compounds. A significant proportion of those compounds are aggregators. This Review discusses the basis for colloidal aggregation, experimental methods for detecting aggregates and analyses recent progress in computer-based systems for detecting colloidal aggregation with particular emphasis on machine learning [In the online version of this Review originally published, the graphical abstract image was incorrectly credited to ‘Reven T.C. Wurman / Alamy Stock Photo’ this has now been corrected].