CoChat
Sign Up
Cited by 33Computational Drug Discovery MethodsAdvanced Statistical Methods and ModelsSpectroscopy and Chemometric Analyses

Statistical Modeling of a Ligand Knowledge Base

Mansson, Ralph A., Welsh, Alan H., Fey, Natalie, Orpen, A. Guy·2006·Journal of Chemical Information and Modeling
DOI 10.1021/ci600212t
Open this paper in CoChat — free account

Abstract

A range of different statistical models has been fitted to experimental data for the Tolman electronic parameter (TEP) based on a large set of calculated descriptors in a prototype ligand knowledge base (LKB) of phosphorus(III) donor ligands. The models have been fitted by ordinary least squares using subsets of descriptors, principal component regression, and partial least squares which use variables derived from the complete set of descriptors, least angle regression, and the least absolute shrinkage and selection operator. None of these methods is robust against outliers, so we also applied a robust estimation procedure to the linear regression model. Criteria for model evaluation and comparison have been discussed, highlighting the importance of resampling methods for assessing the robustness of models and the scope for making predictions in chemically intuitive models. For the ligands covered by this LKB, ordinary least squares models of descriptor subsets provide a good representation of the data, while partial least squares, principal component regression, and least angle regression models are less suitable for our dual aims of prediction and interpretation. A linear regression model with robustly fitted parameters achieves the best model performance over all classes of models fitted to TEP data, and the weightings assigned to ligands during the robust estimation procedure are chemically intuitive. The increased model complexity when compared to the ordinary least squares linear model is justified by the reduced influence of individual ligands on the model parameters and predictions of new ligands. Robust linear regression models therefore represent the best compromise for achieving statistical robustness in simple, chemically meaningful models.

Integrity signals

Registry

Crossref · 10.1021/ci600212t

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • ML models, analysis of their key features, and dimensionality reduction represent important tools for building structure-property relationships and mapping chemical space.

    “ML models, analysis of their key features (32,52), and dimensionality reduction (i.e., unsupervised learning) (53-56) represent important tools for building structure-property relationships and mapping chemical space (57).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [54]

  • When most of the data variance can be explained by the first few principal components, they can act as a useful reduced feature set.

    “When most of the data variance can be explained by the first few PCs, they can act as a useful reduced feature set (54,66,67).”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [54]

  • Fey and coworkers augmented sets of CSD ligands with quantum chemical descriptors to further distinguish their properties.

    “Fey and coworkers (53,54) augmented sets of CSD ligands with quantum chemical descriptors to further distinguish their properties.”

    From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [54]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Mansson, R. A., Welsh, A. H., Fey, N., & Orpen, A. G.
Affiliations
  • University of Southampton (GB)
  • University of Bristol (GB)
Corresponding author
Fey, Natalie
ORCID
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2006-11-01
Volume
46
Issue
6
Pages
2591-2600
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/ci600212t
OpenAlex
W2179093498
Cited by
33
References
29
Citations by year
2026: 1 · 2025: 1 · 2024: 4 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 2 · 2019: 3 · 2018: 1 · 2017: 1 · 2016: 1 · 2014: 1 · 2012: 1
FWCI
2.45
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Chemical Information and Modeling (publishedVersion)
  • PubMed (publishedVersion)
  • ANU Open Research (Australian National University) (submittedVersion)
  • Explore Bristol Research (submittedVersion)
  • Bristol Research (University of Bristol) (submittedVersion)
Funders
Engineering and Physical Sciences Research Council
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
  • Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
Keywords
Tolman electronic parameter, molecular descriptors, robust linear regression, least angle regression, partial least squares, principal component regression, LASSO, model robustness, resampling
MeSH terms
Algorithms, Electrons, Ligands, Models, Chemical, Models, Theoretical, Phosphorus, Regression Analysis, Software, Models, Statistical, Linear Models, Nonlinear Dynamics, Knowledge Bases

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review