Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review
Arkadiusz Z. Dudek, Tomasz Arodz and Jorge Galvez
Affiliation: University of Minnesota,Division of Hematology, Oncology and Transplantation, 420 Delaware St.SE, MMC 480, Minneapolis, MN 55455, USA.
Keywords: QSAR, molecular descriptors, feature selection, machine learning
Virtual filtering and screening of combinatorial libraries have recently gained attention as methods complementing the high-throughput screening and combinatorial chemistry. These chemoinformatic techniques rely heavily on quantitative structure-activity relationship (QSAR) analysis, a field with established methodology and successful history. In this review, we discuss the computational methods for building QSAR models. We start with outlining their usefulness in high-throughput screening and identifying the general scheme of a QSAR model. Following, we focus on the methodologies in constructing three main components of QSAR model, namely the methods for describing the molecular structure of compounds, for selection of informative descriptors and for activity prediction. We present both the well-established methods as well as techniques recently introduced into the QSAR domain.
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