Persistent Link:
http://hdl.handle.net/10150/290612
Title:
Applications of artificial intelligence in drug design
Author:
Parrill, Abby Louise, 1970-
Issue Date:
1996
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Computer-aided drug design is a rapidly growing area of research. The design process can proceed from two angles: either the three-dimensional structure of the biological target is known, or it is unknown. Thus the area of computer-aided drug design can be separated into a number of problems. One problem is determining the structure of a biomolecule from experimental data, as is done in chapter 2 for colominic acid polylactone. These studies determined that there are two helical structures consistent with spectral data A second problem is designing a ligand complementary to the three-dimensional structure of the target. Chapters 3 and 4 describe studies leading to the design and evaluation of neuraminidase inhibitors. These studies indicate that several inhibitors studied are competitive inhibitors of the enzyme with better binding affinities than the natural ligand. The final,and potentially most difficult problem, is to infer features about the biological target from compounds known to bind to that target. Chapters 5 and 6 describe model studies and implementation of CLEW, a program to learn rules relating structural features to biological function. Results indicate that learning based on topological features is a useful first iteration in determining the pharmacophore, or three-dimensional arrangement of functionality required for biological activity.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Chemistry, Organic.; Chemistry, Pharmaceutical.; Artificial Intelligence.; Computer Science.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Chemistry
Degree Grantor:
University of Arizona
Advisor:
Dolate, Daniel P.; Gervay, Jacquelyn

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleApplications of artificial intelligence in drug designen_US
dc.creatorParrill, Abby Louise, 1970-en_US
dc.contributor.authorParrill, Abby Louise, 1970-en_US
dc.date.issued1996en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractComputer-aided drug design is a rapidly growing area of research. The design process can proceed from two angles: either the three-dimensional structure of the biological target is known, or it is unknown. Thus the area of computer-aided drug design can be separated into a number of problems. One problem is determining the structure of a biomolecule from experimental data, as is done in chapter 2 for colominic acid polylactone. These studies determined that there are two helical structures consistent with spectral data A second problem is designing a ligand complementary to the three-dimensional structure of the target. Chapters 3 and 4 describe studies leading to the design and evaluation of neuraminidase inhibitors. These studies indicate that several inhibitors studied are competitive inhibitors of the enzyme with better binding affinities than the natural ligand. The final,and potentially most difficult problem, is to infer features about the biological target from compounds known to bind to that target. Chapters 5 and 6 describe model studies and implementation of CLEW, a program to learn rules relating structural features to biological function. Results indicate that learning based on topological features is a useful first iteration in determining the pharmacophore, or three-dimensional arrangement of functionality required for biological activity.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectChemistry, Organic.en_US
dc.subjectChemistry, Pharmaceutical.en_US
dc.subjectArtificial Intelligence.en_US
dc.subjectComputer Science.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineChemistryen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorDolate, Daniel P.en_US
dc.contributor.advisorGervay, Jacquelynen_US
dc.identifier.proquest9713370en_US
dc.identifier.bibrecord.b34360657en_US
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