ArticleMolecular informatics2022
Block-wise Exploration of Molecular Descriptors with Multi-block Orthogonal Component Analysis (MOCA).
Article in Molecular informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multi-Platform Metabolomics for Fraud Detection in Spices and Botanical Food Supplements: From Fingerprint Acquisition to Decision-Grade Evidence.Molecules (Basel, Switzerland) · 2026Review
- 3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.Journal of computer-aided molecular design · 2025Article
- Metabolomic profiling uncovers diagnostic biomarkers and dysregulated pathways in Parkinson's disease.Frontiers in neurology · 2025Article
- Identification of a Family of Glycoside Derivatives Biologically Active againstPharmaceuticals (Basel, Switzerland) · 2023Article
- Metabolomic Profiling of Angiotensin-II-Induced Abdominal Aortic Aneurysm in LdlrInternational journal of molecular sciences · 2022Article
- Block-wise Exploration of Molecular Descriptors with Multi-block Orthogonal Component Analysis (MOCA).Molecular informatics · 2022Article
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3 authors.
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Abstract
Data tables for machine learning and structure-activity relationship modelling (QSAR) are often naturally organized in blocks of data, where multiple molecular representations or sets of descriptors form the blocks. Multi-block Orthogonal Component Analysis (MOCA), a new analytical tool, can be used to explore such data structures in a single model, identifying principal components that are unique to a single block or joint over multiple blocks. We applied MOCA to two sets of 550 and 300 molecules and up to 9213 molecular descriptors organized in 11 blocks. The MOCA models reveal relationships between the blocks and overarching trends across the whole dataset. Based on the MOCA joint components, we propose a quantitative metric for the redundancy of blocks, useful for a priori block-wise feature selection or evaluation of new molecular representations. The second data set includes 7 ecotoxicological study endpoints for crop protection chemicals, for which we (re-)discovered some general trends and linked them to molecular properties. Using a single MOCA model we estimated the predictive potential of each block and the model-ability of the target block.
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