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Cited by 322Computational Drug Discovery MethodsMachine Learning in Materials ScienceHistory and advancements in chemistry

Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D): Evaluation of Performance

Bender, Andreas, Mussa, Hamse Y., Glen, Robert C., Reiling, Stephan·2004·Journal of Chemical Information and Computer Sciences
DOI 10.1021/ci0498719
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Abstract

A molecular similarity searching technique based on atom environments, information-gain-based feature selection, and the naive Bayesian classifier has been applied to a series of diverse datasets and its performance compared to those of alternative searching methods. Atom environments are count vectors of heavy atoms present at a topological distance from each heavy atom of a molecular structure. In this application, using a recently published dataset of more than 100000 molecules from the MDL Drug Data Report database, the atom environment approach appears to outperform fusion of ranking scores as well as binary kernel discrimination, which are both used in combination with Unity fingerprints. Overall retrieval rates among the top 5% of the sorted library are nearly 10% better (more than 14% better in relative numbers) than those of the second best method, Unity fingerprints and binary kernel discrimination. In 10 out of 11 sets of active compounds the combination of atom environments and the naive Bayesian classifier appears to be the superior method, while in the remaining dataset, data fusion and binary kernel discrimination in combination with Unity fingerprints is the method of choice. Binary kernel discrimination in combination with Unity fingerprints generally comes second in performance overall. The difference in performance can largely be attributed to the different molecular descriptors used. Atom environments outperform Unity fingerprints by a large margin if the combination of these descriptors with the Tanimoto coefficient is compared. The naive Bayesian classifier in combination with information-gain-based feature selection and selection of a sensible number of features performs about as well as binary kernel discrimination in experiments where these classification methods are compared. When used on a monoaminooxidase dataset, atom environments and the naive Bayesian classifier perform as well as binary kernel discrimination in the case of a 50/50 split of training and test compounds. In the case of sparse training data, binary kernel discrimination is found to be superior on this particular dataset. On a third dataset, the atom environment descriptor shows higher retrieval rates than other 2D fingerprints tested here when used in combination with the Tanimoto similarity coefficient. Feature selection is shown to be a crucial step in determining the performance of the algorithm. The representation of molecules by atom environments is found to be more effective than Unity fingerprints for the type of biological receptor similarity calculations examined here. Combining information prior to scoring and including information about inactive compounds, as in the Bayesian classifier and binary kernel discrimination, is found to be superior to posterior data fusion (in the datasets tested here).

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Authors
Bender, A., Mussa, H. Y., Glen, R. C., & Reiling, S.
Affiliations
  • University of Cambridge (GB)
  • Lead Generation Informatics, Drug Innovation & Approval (DI&A), Aventis, 1041 Route 202-206, Bridgewater, New Jersey 08807
ORCID
Venue
Journal of Chemical Information and Computer Sciences
Publisher
American Chemical Society (ACS)
Published
2004-08-20
Volume
44
Issue
5
Pages
1708-1718
ISSN
0095-2338 (ISSN-L)
DOI
10.1021/ci0498719
OpenAlex
W2089589794
Cited by
322
References
31
Citations by year
2026: 3 · 2025: 6 · 2024: 11 · 2023: 11 · 2022: 13 · 2021: 14 · 2020: 6 · 2019: 17 · 2018: 13 · 2017: 12 · 2016: 22 · 2015: 12 · 2014: 14 · 2013: 22 · 2012: 15
FWCI
11.17
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Chemical Information and Computer Sciences (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › History and advancements in chemistry
Keywords
naive Bayesian classifier, information gain feature selection, Tanimoto coefficient, data fusion, monoamine oxidase inhibitors

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