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Nat Chem. 2019 May;11(5):402-418. doi: 10.1038/s41557-019-0234-9. Epub 2019 Apr 15.

Computational advances in combating colloidal aggregation in drug discovery.

Author information

1
Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA. reker@mit.edu.
2
Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. reker@mit.edu.
3
MIT-IBM Watson AI Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. reker@mit.edu.
4
Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK.
5
Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal.
6
Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal. tiago.rodrigues@medicina.ulisboa.pt.

Abstract

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.

PMID:
30988417
DOI:
10.1038/s41557-019-0234-9
[Indexed for MEDLINE]

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