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Cited by 189

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Pharmacophore Fingerprinting. 1. Application to QSAR and Focused Library Design

McGregor, Malcolm J., Muskal, Steven M.·1999·Journal of Chemical Information and Computer Sciences

Abstract

A new method of rapid pharmacophore fingerprinting (PharmPrint method) has been developed. A basis set of 10,549 three-point pharmacophores has been constructed by enumerating several distance ranges and pharmacophoric features. Software has been developed to assign pharmacophoric types to atoms in chemical structures, generate multiple conformations, and construct the binary fingerprint according to the pharmacophores that result. The fingerprint is used as a descriptor for developing a quantitative structure-activity relationship (QSAR) model using partial least squares. An example is given using sets of ligands for the estrogen receptor (ER). The result is compared with previously published results on the same data to show the superiority of a full 3D, conformationally flexible approach. The QSAR model can be readily interpreted in structural/chemical terms. Further examples are given using binary activity data and some of our novel in-house compounds, which show the value of the model when crossing compound classes.

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Metadata

Authors
McGregor, M. J., & Muskal, S. M.
Venue
Journal of Chemical Information and Computer Sciences
Published
1999-03-19
Volume
39
Issue
3
Pages
569-574
ISSN
0095-2338 (ISSN-L)
OpenAlex
W2153609134
Cited by
189
References
1
FWCI
14.29
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Pharmacophore Fingerprinting. 1. Application to QSAR and Focused Library Design. https://app.cochat.ai/papers/10.1021/ci980159j
The paper
McGregor, M. J., & Muskal, S. M. (1999). Pharmacophore Fingerprinting. 1. Application to QSAR and Focused Library Design. Journal of Chemical Information and Computer Sciences. https://doi.org/10.1021/ci980159j

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.