ReviewNature chemistry2019
Computational advances in combating colloidal aggregation in drug discovery.
Review in Nature chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Quantitative prediction of siRNA complexation by ionizable drugs enables their codelivery in nanoparticles.Science advances · 2026Article
- A Chemical-Genetic Interaction Matrix Reveals Drug Mechanism and Genetic Architecture.bioRxiv : the preprint server for biology · 2026Article
- Quantum-machine-assisted drug discovery.npj drug discovery · 2026Review
- Assessing biological activities of polyphenols in the context of 4R - challenges of a ubiquitous compound class with complex modes of action.Frontiers in pharmacology · 2026Review
- Discovery and optimization of a guanylhydrazone-based small molecule to replace bFGF for cell culture applications.Biochemistry and biophysics reports · 2025Article
- Mitigating Molecular Aggregation in Drug Discovery With Predictive Insights From Explainable AI.Angewandte Chemie (International ed. in English) · 2025Article
- Advancing promiscuous aggregating inhibitor analysis with intelligent machine learning classification.Briefings in bioinformatics · 2025Article
- Article
- Tackling assay interference associated with small molecules.Nature reviews. Chemistry · 2024Review
- Fragment-based drug nanoaggregation reveals drivers of self-assembly.Nature communications · 2023Article
- Rigidity-Induced Controlled Aggregation of Binary Colloids.ACS omega · 2023Article
- Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds.Journal of medicinal chemistry · 2023Article
- Evaluation guidelines for machine learning tools in the chemical sciences.Nature reviews. Chemistry · 2022Review
- Article
- Gains from no real PAINS: Where 'Fair Trial Strategy' stands in the development of multi-target ligands.Acta pharmaceutica Sinica. B · 2021Review
- Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of nsp13 helicase.The Biochemical journal · 2021Article
- Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression.ACS central science · 2021Article
- Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy.Molecules (Basel, Switzerland) · 2021Review
- Impact of sphingosine and acetylsphingosines on the aggregation and toxicity of metal-free and metal-treated amyloid-β.Chemical science · 2020Article
- Therapeutics-how to treat phase separation-associated diseases.Emerging topics in life sciences · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
30988417What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.