ArticleBiomolecules2023
On the Best Way to Cluster NCI-60 Molecules.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Chemical space visualization at scale: a survey of end-to-end pipelines and dataset-size archetypes.Journal of cheminformatics · 2026Review
- Best practices to cluster large molecular libraries.bioRxiv : the preprint server for biology · 2026Article
- Regio- and Stereocontrolled-Synthesis of a Heterocycle Fragment Collection Using Palladium Catalyzed C-H Arylation.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Article
- UMAP-based clustering split for rigorous evaluation of AI models for virtual screening on cancer cell lines.Journal of cheminformatics · 2025Article
- Spatial mapping of the brain metabolome lipidome and glycome.Nature communications · 2025Article
- Gout management: Patent analytics and computational drug design explores URAT1 inhibitors landscape.PloS one · 2025Article
- Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow.Journal of cheminformatics · 2024Article
- Efficient clustering of large molecular libraries.bioRxiv : the preprint server for biology · 2024Article
- Unraveling Protein-Metabolite Interactions in Precision Nutrition: A Case Study of Blueberry-Derived Metabolites Using Advanced Computational Methods.Metabolites · 2024Article
- Chemical Multiverse and Diversity of Food Chemicals.Journal of chemical information and modeling · 2024Article
- A practical guide to machine-learning scoring for structure-based virtual screening.Nature protocols · 2023Review
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Authors and funding
2 authors.
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Abstract
Machine learning-based models have been widely used in the early drug-design pipeline. To validate these models, cross-validation strategies have been employed, including those using clustering of molecules in terms of their chemical structures. However, the poor clustering of compounds will compromise such validation, especially on test molecules dissimilar to those in the training set. This study aims at finding the best way to cluster the molecules screened by the National Cancer Institute (NCI)-60 project by comparing hierarchical, Taylor-Butina, and uniform manifold approximation and projection (UMAP) clustering methods. The best-performing algorithm can then be used to generate clusters for model validation strategies. This study also aims at measuring the impact of removing outlier molecules prior to the clustering step. Clustering results are evaluated using three well-known clustering quality metrics. In addition, we compute an average similarity matrix to assess the quality of each cluster. The results show variation in clustering quality from method to method. The clusters obtained by the hierarchical and Taylor-Butina methods are more computationally expensive to use in cross-validation strategies, and both cluster the molecules poorly. In contrast, the UMAP method provides the best quality, and therefore we recommend it to analyze this highly valuable dataset.
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