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- W1996327711 abstract "This paper is focused on modern approaches to machine learning, most of which are as yet used infrequently or not at all in chemoinformatics. Machine learning methods are characterized in terms of the “modes of statistical inference” and “modeling levels” nomenclature and by considering different facets of the modeling with respect to input/ouput matching, data types, models duality, and models inference. Particular attention is paid to new approaches and concepts that may provide efficient solutions of common problems in chemoinformatics: improvement of predictive performance of structure–property (activity) models, generation of structures possessing desirable properties, model applicability domain, modeling of properties with functional endpoints (e.g., phase diagrams and dose–response curves), and accounting for multiple molecular species (e.g., conformers or tautomers)." @default.
- W1996327711 created "2016-06-24" @default.
- W1996327711 creator A5070041767 @default.
- W1996327711 creator A5090062144 @default.
- W1996327711 date "2012-05-25" @default.
- W1996327711 modified "2023-09-30" @default.
- W1996327711 title "Machine Learning Methods for Property Prediction in Chemoinformatics: <i>Quo Vadis</i>?" @default.
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