King, R. D., Schierz, A. C., Clare, A., Rowland, J., Sparkes, A., Nijssen, S. and Ramon, J., 2010. Inductive queries for a drug designing robot scientist. In: Dzeroski, S., Goethals, B. and Panov, P., eds. Inductive Databases and Constraint-Based Data Mining. Springer, pp. 421-451.
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It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments.
|Item Type:||Book Section|
|Number of Pages:||456|
|Uncontrolled Keywords:||Quantitative Structure Activity Relationships, Robot Scientist, Graph Mining, Inductive Logic Programming, Active Learning.|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Science > Chemistry
Technology > Engineering > General Engineering
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||Dr Amanda C. Schierz LEFT|
|Deposited On:||17 Dec 2010 11:35|
|Last Modified:||07 Mar 2013 15:40|
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