• 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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References

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EXPERIMENTS IN PREDICTING BIODEGRADABILITY

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.

New Trends in Structure‐Biodegradability Relationships

This short review shows that the Boolean backpropagation neural networks are promising tools to model biodegradable structures by means of 11 Boolean structural descriptors.

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 and both models predict the biodegrading categories correctly 90% of the time for the training set and an independent validation set.

Carcinogenesis Predictions Using ILP

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.

Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction

Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.

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.

Feature Construction with Inductive Logic Programming: A Study of Quantitative Predictions of Biological Activity by Structural Attributes

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.

Structural Regression Trees

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.

Relational learning vs. propositionalization: Investigations in inductive logic programming and propositional machine learning

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.