期刊
NATURE CHEMISTRY
卷 11, 期 5, 页码 402-418出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41557-019-0234-9
关键词
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资金
- Swiss National Science Foundation [P2EZP3_168827, P300P2_177833]
- Marie Sklodowska-Curie ITN Protein Conjugates [675007]
- H2020 (TWINN-2017 ACORN) [807281]
- POR Lisboa 2020/FEDER [02/SAICT/2017, Lisboa-01-0145-FEDER-028333]
- MIT-IBM Watson AI Lab
- MIT SenseTime coalition
- Royal Society [UF110046, URF/R/180019]
- ERC StG (TagIt) [676832]
- [743640]
- Swiss National Science Foundation (SNF) [P300P2_177833, P2EZP3_168827] Funding Source: Swiss National Science Foundation (SNF)
- Royal Society [UF110046] Funding Source: Royal Society
- Marie Curie Actions (MSCA) [675007] Funding Source: Marie Curie Actions (MSCA)
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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