Quantitative pharmacophore models with inductive logic programming
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ILP has been applied to identifying and predicting ligands.
“The types of task ILP has been applied to include identifying and predicting ligands (substructures responsible for medical activity) (Finn et al., 1998; Srinivasan et al., 2006; Kaalia et al., 2016)”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Srinivasan et al., 2006
The Introduction describes ILP-based structure–activity models that can help suggest new molecules, and the Abstract describes predicting a ligand’s medicinal activity. But the paper defines ligands as small molecules, not the activity-related substructures in the parenthetical; it predicts their activity rather than the ligands themselves. The supplied passages give no page numbers. (LLM, conf=0.86)
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Research works printed but not linked: 28
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- Marchand-Geneste, N., Watson, K. A., Alsberg, B., & King, R. D. (2002). A new approach to pharmacophore mapping and QSAR analysis using inductive logic programming. Application to thermolysin inhibitors and glycogen phosphorylase B inhibitors. Journal of Medicinal Chemistry, 45, 399-409. (with corrections in vol 46, p. 653).
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Cited by
Sorted by citations
Inductive Logic Programming At 30: A New Introduction
Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83 · see citing works
“The types of task ILP has been applied to include identifying and predicting ligands (substructures responsible for medical activity) (Finn et al., 1998; Kaalia et al., 2016; Srinivasan et al., 2006), predicting mutagenic activity of molecules and identifying structural alerts for the causes of chemical cancers (Srini…”
Method
- Authors
- Srinivasan, A., Page, D., Camacho, R., & King, R.
- Affiliations
- IBM Research - India (IN)
- University of Wisconsin–Madison (US)
- Aberystwyth University (GB)
- Corresponding author
- Srinivasan, Ashwin
- Venue
- Machine Learning
- Published
- 2006-05-08
- Volume
- 64
- Issue
- 1-3
- Pages
- 65-90
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2043146319
- Cited by
- 15
- References
- 0
- Citations by year
- 2023: 2 · 2022: 1 · 2020: 1 · 2018: 1 · 2016: 1 · 2015: 2 · 2013: 1
- FWCI
- 1.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Machine Learning (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by-nc)
- 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 › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- pharmacophore model, QSAR, inductive logic programming, molecular conformation, logistic regression, naive Bayes, linear regression, kernel regression, statistical relational learning, protease inhibition, thermolysin, drug design
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