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Computational advances in combating colloidal aggregation in drug discovery

Nature chemistry, 2019-05, Vol.11 (5), p.402-418 [Peer Reviewed Journal]

Springer Nature Limited 2019. ;ISSN: 1755-4330 ;EISSN: 1755-4349 ;DOI: 10.1038/s41557-019-0234-9 ;PMID: 30988417

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Title:
Computational advances in combating colloidal aggregation in drug discovery
Author: Reker, Daniel ; Bernardes, Gonçalo J L ; Rodrigues, Tiago
Subjects: Agglomeration ; Aggregates ; Algorithms ; Colloid chemistry ; Colloids ; Colloids - chemistry ; Colloids - metabolism ; Computation ; Computer applications ; Dependence ; Drug Discovery ; Learning algorithms ; Machine Learning ; Mimicry ; Optimization ; Organic Chemicals - chemistry ; Organic Chemicals - metabolism ; Organic chemistry ; Protein Binding ; Proteins ; Proteins - metabolism ; Small Molecule Libraries - chemistry ; Small Molecule Libraries - metabolism
Is Part Of: Nature chemistry, 2019-05, Vol.11 (5), p.402-418
Description: 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.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 1755-4330
EISSN: 1755-4349
DOI: 10.1038/s41557-019-0234-9
PMID: 30988417
Source: AUTh Library subscriptions: ProQuest Central
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Computational advances in combating colloidal aggregation in drug discovery

Springer Nature Limited 2019. ;ISSN: 1755-4330 ;EISSN: 1755-4349 ;DOI: 10.1038/s41557-019-0234-9 ;PMID: 30988417

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