國立虎尾科技大學 |

Interactions in a drug-target network.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Interactions in a drug-target network./
Author:	Kulkarni, Varsha S.
Description:	1 online resource (123 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781369474008

Interactions in a drug-target network.
Kulkarni, Varsha S.

Interactions in a drug-target network. - 1 online resource (123 pages)

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Highly chemically similar drugs usually possess similar biological activities but small changes in chemistry result in large differences in biological effects. Chemically similar drug pairs showing extreme deviations in activity represent distinctive drug interactions. The presence of these interactions adversely affects prediction of structure and activity associations. Their identification has crucial implications on drug development and innovations. Given the multitude of drugs in an ensemble, pairs possess multilevel distinctiveness in terms of their attributes of structural and activity similarity or variation. The cliff characterization for describing drops in similar activity has received considerable attention, however, it remains quantitatively less refined. In this dissertation, I investigate distinctiveness of drug interactions using a large drug-target network and provide a quantitative rationale for characterization of the pharmacological topography. I consider rises in pairwise similarity and variation in activity of drugs on proteins with chemical similarity (c) to assess levels of distinctiveness. These activity measures are affected by the presence of few drugs (targets) having multiple targets (drugs). I quantify interactions between drugs by considering similarity and variation jointly with c. The probability of distinctiveness is predicted by employing joint probability of structure and activity measures. Intermittent spikes in variation along the axis of c represent canyons in the activity landscape. This new representation accounts for distinctiveness through relative rises in activity measures and offers an enhanced perspective. It provides a mathematical basis for predicting the probability of occurrence of distinctiveness. It identifies the drug pairs at varying levels of distinctiveness and non-distinctiveness. Prediction is validated even if data approximately satisfy the conditions of the formulation. The difference in distinctive interactions emphasizes the importance of studying both measures, and reveals that the choice of measurement can affect the interpretation. Further, I find that minor changes in methods or perturbations of measures can crucially alter the classification of interactions as distinctive. Identification and interpretation of distinctiveness, therefore, gain relevance through methodological specifications. The present analysis of structure and activity provides an in depth modeling and assessment of distinctiveness and the probability of its occurrence. It could potentially influence decision-making in research and development.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369474008Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Interactions in a drug-target network.
LDR:03907ntm a2200373Ki 4500 001 909672
005 20180426091043.5
006 m o u
007 cr mn||||a|a||
008 190606s2016 xx obm 000 0 eng d
020 $a 9781369474008
035 $a (MiAaPQ)AAI10248165
035 $a (MiAaPQ)indiana:14462
035 $a AAI10248165
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Kulkarni, Varsha S. $3 1180568
245 1 0 $a Interactions in a drug-target network.
264 0 $c 2016
300 $a 1 online resource (123 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Adviser: David J. Wild.
502 $a Thesis (Ph.D.) $c Indiana University $d 2016.
504 $a Includes bibliographical references
520 $a Highly chemically similar drugs usually possess similar biological activities but small changes in chemistry result in large differences in biological effects. Chemically similar drug pairs showing extreme deviations in activity represent distinctive drug interactions. The presence of these interactions adversely affects prediction of structure and activity associations. Their identification has crucial implications on drug development and innovations. Given the multitude of drugs in an ensemble, pairs possess multilevel distinctiveness in terms of their attributes of structural and activity similarity or variation. The cliff characterization for describing drops in similar activity has received considerable attention, however, it remains quantitatively less refined. In this dissertation, I investigate distinctiveness of drug interactions using a large drug-target network and provide a quantitative rationale for characterization of the pharmacological topography. I consider rises in pairwise similarity and variation in activity of drugs on proteins with chemical similarity (c) to assess levels of distinctiveness. These activity measures are affected by the presence of few drugs (targets) having multiple targets (drugs). I quantify interactions between drugs by considering similarity and variation jointly with c. The probability of distinctiveness is predicted by employing joint probability of structure and activity measures. Intermittent spikes in variation along the axis of c represent canyons in the activity landscape. This new representation accounts for distinctiveness through relative rises in activity measures and offers an enhanced perspective. It provides a mathematical basis for predicting the probability of occurrence of distinctiveness. It identifies the drug pairs at varying levels of distinctiveness and non-distinctiveness. Prediction is validated even if data approximately satisfy the conditions of the formulation. The difference in distinctive interactions emphasizes the importance of studying both measures, and reveals that the choice of measurement can affect the interpretation. Further, I find that minor changes in methods or perturbations of measures can crucially alter the classification of interactions as distinctive. Identification and interpretation of distinctiveness, therefore, gain relevance through methodological specifications. The present analysis of structure and activity provides an in depth modeling and assessment of distinctiveness and the probability of its occurrence. It could potentially influence decision-making in research and development.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Applied mathematics. $3 1069907
650 4 $a Biostatistics. $3 783654
650 4 $a Pharmaceutical sciences. $3 1180569
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0364
690 $a 0308
690 $a 0572
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Indiana University. $b Informatics. $3 1179553
773 0 $t Dissertation Abstracts International $g 78-08B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10248165 $z click for full text (PQDT)