Modeling robust QSAR.

Jaroslaw Polanski; Andrzej Bak; Rafal Gieleciak; Tomasz Magdziarz

doi:10.1021/ci050314b

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Modeling robust QSAR.

Jaroslaw Polanski·Andrzej Bak·Rafal Gieleciak·Tomasz Magdziarz

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Abstract

Quantitative Structure Activity Relationship (QSAR) is a term describing a variety of approaches that are of substantial interest for chemistry. This method can be defined as indirect molecular design by the iterative sampling of the chemical compounds space to optimize a certain property and thus indirectly design the molecular structure having this property. However, modeling the interactions of chemical molecules in biological systems provides highly noisy data, which make predictions a roulette risk. In this paper we briefly review the origins for this noise, particularly in multidimensional QSAR. This was classified as the data, superimposition, molecular similarity, conformational, and molecular recognition noise. We also indicated possible robust answers that can improve modeling and predictive ability of QSAR, especially the self-organizing mapping of molecular objects, in particular, the molecular surfaces, a method that was brought into chemistry by Gasteiger and Zupan.

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Algorithms Chemistry Computer Simulation Databases, Factual Models, Chemical Models, Molecular Models, Statistical Models, Theoretical Molecular Conformation Neural Networks, Computer Quantitative Structure-Activity Relationship Software Stochastic Processes Journal Article

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Jaroslaw Polanski, Andrzej Bak, Rafal Gieleciak, Tomasz Magdziarz. Modeling robust QSAR.. https://doi.org/10.1021/ci050314b

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