JOURNAL ARTICLE

Natural Language Annotations for Question Answering.

Boris KatzGary C. BorchardtSue Felshin

Year: 2006 Journal:   The Florida AI Research Society Vol: 77 Pages: 303-306

Abstract

A series of 107 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio) thymine (HEPT) with anti-HIV-1 activity as a non-nucleoside reverse transcriptase inhibitor (NNRTI) has been studied. Monte Carlo method has been used as a tool to build up the quantitative structure-activity relationships (QSAR) for anti-HIV-1 activity. The QSAR models were calculated with the representation of the molecular structure by simplified molecular input-line entry system and by the molecular graph. Three various splits into training and test set were examined. Statistical quality of all build models is very good. Best calculated model had following statistical parameters: for training set r(2) = 0.8818, q(2) = 0.8774 and r(2) = 0.9360, q(2) = 0.9243 for test set. Structural indicators (alerts) for increase and decrease of the IC50 are defined. Using defined structural alerts computer aided design of new potential anti-HIV-1 HEPT derivates is presented.

Keywords:
Question answering Computer science Natural language Natural language processing Natural (archaeology) Artificial intelligence Information retrieval Linguistics History Philosophy

Metrics

42
Cited By
2.31
FWCI (Field Weighted Citation Impact)
5
Refs
0.90
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

AI-based Problem Solving and Planning
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence
Speech and dialogue systems
Physical Sciences →  Computer Science →  Artificial Intelligence

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