Applications of rule-induction in the derivation of quantitative structure-activity relationships.
Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable. The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness. Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed. The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.
A-Razzak
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Infolink Decision Services Ltd., London, U.K.
Glen
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RC
D000465:Algorithms; D000927:Anticonvulsants / Q000737:chemistry; D000962:Antimalarials / Q000737:chemistry; D001185:Artificial Intelligence; D002316:Cardiotonic Agents / Q000737:chemistry; D012984:Software; D013329:Structure-Activity Relationship; D005680:gamma-Aminobutyric Acid / Q000737:chemistry
D000927:Anticonvulsants; D000962:Antimalarials; D002316:Cardiotonic Agents; D005680:gamma-Aminobutyric Acid