Abstract
Data fusion originally referred to the process of combining multi-sensor data from different sources such that the resulting information/model is in some sense better than would be possible when these sources where used individually. In this paper the data fusion concept is extended to molecular drug design. Rather than using data from different sensor sources, different descriptor sets are used to predict activities or responses for a set of molecules. Data fusion techniques are applied in order to improve the predictive (QSAR) model on test data. In this case this type of data fusion is referred to as auto-fusion. An effective auto-fusion functional model and alternative architectures are proposed for a predictive molecular design or QSAR model to model and predict the binding affinity to the human serum albumin.
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Huang, C., Embrechts, M.J., Sukumar, N., Breneman, C.M. (2007). Data Fusion and Auto-fusion for Quantitative Structure-Activity Relationship (QSAR). In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4668. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74690-4_64
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DOI: https://doi.org/10.1007/978-3-540-74690-4_64
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