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Acyclic Subgraph-based Descriptor Spaces for Chemical Compound Retrieval and Classification

Nikil Wale and George Karypis
IEEE International Conference on Data Mining (ICDM), 2006
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Abstract
In recent years the development of computational techniques that build models
to correctly assign chemical compounds to various classes or to retrieve potential
drug-like compounds has been an active area of research. These techniques are
used extensively at various phases during the drug development process.
Many of the best-performing techniques for these tasks, utilize a descriptor-based
representation of the compound that captures various aspects of the underlying
molecular graph's topology.

In this paper we introduce and describe algorithms for efficiently generating a new
set of descriptors that are derived from all connected acyclic fragments
present in the molecular graphs. In addition, we introduce an extension to existing
vector-based kernel functions to take into account the length of the fragments present
in the descriptors.

We experimentally evaluate the performance of the new descriptors in the context of
SVM-based classification and ranked-retrieval on 28 classification and retrieval
problems derived from 17 datasets. Our experiments show that for both the classification
and retrieval tasks, these new descriptors consistently and statistically outperform
previously developed schemes based on the widely used fingerprint- and Maccs keys-based
descriptors, as well as recently introduced descriptors obtained by mining and analyzing
the structure of the molecular graphs.

Research topics: AFGen | Bioinformatics | Cheminformatics | Data mining