Browsing by Author "Wale, Nikil"

Now showing 1 - 4 of 4
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    Acyclic Subgraph based Descriptor Spaces for Chemical Compound Retrieval and Classification
    (2006-03-20) Wale, Nikil; Karypis, George
    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.
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    Indirect Similarity based Methods for Effective Scaffold-Hopping in Chemical Compounds
    (2007-10-15) Wale, Nikil; Watson, Ian A.; Karypis, George
    Methods that can screen large databases to retrieve a structurally diverse set of compounds with desirable bioactivity properties are critical in the drug discovery and development process. This paper presents a set of such methods that are designed to find compounds that are structurally different to a certain query compound while retaining its bioactivity properties (scaffold hops). These methods utilize various indirect ways of measuring the similarity between the query and a compound that take into account additional information beyond their structure-based similarities. The set of techniques that are presented capture these indirect similarities using approaches based on analyzing the similarity network formed by the query and the database compounds. Experimental evaluation shows that most of these methods substantially outperform previously developed approaches both in terms of their ability to identify structurally diverse active compounds as well as active compounds in general.
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    Methods for Effective Virtual Screening and Scaffold-Hopping in Chemical Compounds
    (2007-04-04) Wale, Nikil; Karypis, George; Watson, Ian A.
    Methods that can screen large databases to retrieve a structurally diverse set of compounds with desirable bioactivity properties are critical in the drug discovery and development process. This paper presents a set of such methods, which are designed to find compounds that are structurally different to a certain query compound while retaining its bioactivity properties (scaffold hops). These methods utilize various indirect ways of measuring the similarity between the query and a compound that take into account additional information beyond their structure-based similarities. Two sets of techniques are presented that capture these indirect similarities using approaches based on automatic relevance feedback and on analyzing the similarity network formed by the query and the database compounds. Experimental evaluation shows that many of these methods substantially outperform previously developed approaches both in terms of their ability to identify structurally diverse active compounds as well as active compounds in general.
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    Target Identification for Chemical Compounds using Target-Ligand Activity data and Ranking based Methods
    (2008-10-29) Wale, Nikil; Karypis, George
    Drug discovery is an expensive process. It has been estimated that a new drug compound that is introduced in the market after FDA approval carries a cost of approximately $800 million from the conception of target implicated for a disease to successful identification of chemical entity or drug that is successful in human trials. There is a need to cut the cost of developing new drugs (to bring overall cost lower for the producers and consumers alike) by identifying promising candidate targets as well as compounds and to tackle problems such an toxicity, lack of efficacy in humans, and poor physical properties in the early stages of drug discovery. In order to achieve this objective, in recent years the development of computational techniques that identify all the likely targets for a given chemical compound has been an active area of research. Identification of all the potential targets for a chemical compound provides insights into its potential toxicity, helps in repositioning it, and also provides insights into the behavior and relation among targets themselves from the perspective of small molecules. In this paper we address this problem of target identification in the context of small molecule. We present a set of techniques whose goal is to rank or prioritize the targets in the context of a given chemical compound such that most targets that have a potential to show activity to this compound appear higher in the ranked list. These methods are motivated by recent advances in category ranking and protein secondary structure prediction approaches and utilize target-ligand activity data to prioritize targets. Our extensive experimental evaluation shows that most of the methods developed in this work are either competitive or substantially outperform previously developed approaches to solve the above problem in drug discovery.