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Abstract Details
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Activity Number:
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470
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #302563 |
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Title:
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In Search of Desirable Compounds
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Author(s):
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Adrijo Chakraborty*+ and Kjell Johnson and Abhyuday Mandal
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Companies:
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University of Georgia and Pfizer Inc. and University of Georgia
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Address:
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101 Cedar Street, Athens, GA, 30602-7952,
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Keywords:
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Drug discovery ;
Desirability function ;
Partial least squares ;
Random forest regression ;
Unsupervised learning ;
Dimension reduction
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Abstract:
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In drug discovery, chemists often evaluate a compound's performance across a number of endpoints (efficacy, ADME properties, safety, etc.). A popular way to prioritize compounds is through desirability scoring across the endpoints of choice. In addition to desirability scores, chemists can measure or compute other compound descriptors that are thought to be related to the endpoints of interest. Applying the methods discussed here we identify some important descriptors and efficiently classify the compounds according to their desirability scores. Also, we explore ways to visualize this high-dimensional data that can effectively inform chemists' decisions in synthesizing new compounds
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