Corpus ID: 2462127

u EXPERIMENTS IN PREDICTING BIODEGRADABILITY

@inproceedings{Blockeel2004uEI,
  title={u EXPERIMENTS IN PREDICTING BIODEGRADABILITY},
  author={Hendrik Blockeel and Sa{\vs}o D{\vz}eroski and Boris Kompare and Stefan Kramer and Bernhard Pfahringer},
  year={2004}
}
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 

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SHOWING 1-10 OF 33 REFERENCES
EXPERIMENTS IN PREDICTING BIODEGRADABILITY
TLDR
This paper presents a novel application of inductive logic programming (ILP) in the area of quantitative structure-activity relationships (QSARs) and employs a number of relational classification and regression methods on the relational representation and compared to propositional methods applied to different propositionalizations of the problem. Expand
Prediction of biodegradability of organic chemicals by an artificial neural network
Abstract A commercial Artificial Neural Network (ANN) program was used to predict the biodegradability of organic chemicals. Good predictions were obtained for mono- and di-substituted benzenes. TheExpand
New Trends in Structure‐Biodegradability Relationships
Problems related to structure-biodegradation models are discussed. They deal with the homogeneity of the data sets, the selection of an adequate statistical method, and the choice of the molecularExpand
Predictive model for aerobic biodegradability developed from a file of evaluated biodegradation data
A file of evaluated biodegradation data was used to develop a model for predicting aerobic biodegradability from chemical substructures. Chemicals initially were divided into three groups: (a)Expand
Carcinogenesis Predictions Using ILP
TLDR
The use of the ILP system Progol is described to obtain SARs relating molecular structure to cancerous activity in rodents from data from the U.S. National Toxicology Program, which are comparable in accuracy to those from expert chemists, and more accurate than most state-of-the-art toxicity prediction methods. Expand
Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction
TLDR
This work considers the problem of predicting the mutagenic activity of small molecules: a property that is related to carcinogenicity, and an important consideration in developing less hazardous drugs, and compares the predictive power of the logical theories constructed against benchmarks set by regression, neural, and tree-based methods. Expand
Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.
TLDR
The machine learning program GOLEM from the field of inductive logic programming was applied to the drug design problem of modeling structure-activity relationships and obtained understandable rules that characterized the chemistry of favored inhibitors in terms of polarity, flexibility, and hydrogen-bonding character. Expand
Feature Construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity by Structural Attributes
TLDR
The use of ILP programs is examined, not for obtaining theories complete for the sample, but as a method of discovering new attributes that could then be used by methods like linear regression, thus allowing for quantitative predictions and the ability to use structural information as background knowledge. Expand
Structural Regression Trees
TLDR
Structural Regression Trees (SRT) is presented, a new algorithm which integrates the statistical method of regression trees into Inductive Logic Programming and can be applied to a class of problems most other ILP systems cannot handle. Expand
Relational learning vs. propositionalization: Investigations in inductive logic programming and propositional machine learning
TLDR
This research attacked the mode confusion problem by developing a modeling framework that automates the very labor-intensive and therefore time-heavy and expensive process of modeling human interaction with mode-based systems. Expand
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