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

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 in-troduce 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 ex-tension to existing vector-based kernel functions to take into ac-count 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 pre-viously 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.