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Experiments in Predicting Biodegradability

Abstract
This paper is concerned with the use of AI techniques in ecology. More specifically, we present a novel application of inductive logic programming (ILP) in the area of quantitative structure-activity relationships (QSARs). The activity we want to predict is the biodegradability of chemical compounds in water. In particular, the target variable is the half-life for aerobic aqueous biodegradation. Structural descriptions of chemicals in terms of atoms and bonds are derived from the chemicals' SMILES encodings. The definition of substructures is used as background knowledge. Predicting biodegradability is essentially a regression problem, but we also consider a discretized version of the target variable. We thus employ a number of relational classification and regression methods on the relational representation and compare these to propositional methods applied to different propositionalizations of the problem. We also experiment with a prediction technique that consists of merging upper and lower bound predictions into one prediction. Some conclusions are drawn concerning the applicability of machine learning systems and the merging technique in this domain and the evaluation of hypotheses.
Type
Conference Contribution
Type of thesis
Series
Citation
Blockeel H., Dzeroski S., Kompare B., Kramer S., Pfahringer B. & Van Laer W.(2004). Experiments in Predicting Biodegradability. Applied Artificial Intelligence, 18(2), 157-181.
Date
2004
Publisher
Taylor & Francis
Degree
Supervisors
Rights
This is an author’s version of an article published in journal: Applied Artificial Intelligence. Copyright © 2008 Taylor & Francis.