On the best way to cluster NCI-60 molecules
File(s)
Published version
Author(s)
Hernandez-Hernandez, Saiveth
Ballester, Pedro J
Type
Journal Article
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.
Date Issued
2023-03
Date Acceptance
2023-03-06
Citation
Biomolecules, 2023, 13 (3)
ISSN
2218-273X
Publisher
MDPI AG
Journal / Book Title
Biomolecules
Volume
13
Issue
3
Copyright Statement
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000953983800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
Biochemistry & Molecular Biology
clustering
Life Sciences & Biomedicine
model validation
NCI-60 panel
PREDICTION
Science & Technology
SCORING FUNCTIONS
small molecules
VALIDATION
Publication Status
Published
Article Number
498
Date Publish Online
2023-03-08