Analysis of the Internal Representations Developed by Neural Networks for Structures Applied to Quantitative Structure-Activity Relationship Studies of Benzodiazepines

By Alessio Micheli, Alessandro Sperduti, Antonina Starita, A. Bianucci. 2001

76 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Spectroscopy

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Citations in the corpus

Classification of small molecules by two- and three-dimensional decomposition kernels (2007, methodology): “Different statistical learning algorithms for structured data have been successfully applied to QSAR problems, including recursive neural networks (Micheli et al., 2001) and kernel machines (Horva´th et al., 2004; Kashima et al., 2003; Mahe et al., 2005; Menchetti et al., 2005; Swamidass et al.,…”
Graph kernels for chemical informatics (2005, methodology): “…neural network (RNN) approach [Baldi and Chauvin, 1996, Goller and Kuchler, 1996, Sperduti and Starita, 1997, LeCun et al., 1998, Frasconi et al., 1998, Micheli et al., 2001, 2003, Baldi and Pollastri, 2003] can be viewed as a variation of the graphical model approach with Bayesian networks .”
Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity (2005, methodology): “…structured data and applied to molecular structures are inductive logic programming (ILP) (Muggleton, 1992) and graphical models (Pearl, 1988; Lauritzen, 1996; Heckerman, 1998) together with the associated recursive neural networks (Micheli et al., 2001, 2003; Baldi and Pollastri, 2003).”
A renaissance of neural networks in drug discovery (2016, methodology): “…molecular graphs without the use of a precomputed set of molecular descriptors: a ‘ neural device for searching direct correlations between structures and properties of chemical compounds ’ with convolution architecture (see later) [85], recursive neural networks [86], graph machines [87], etc.”
Neural networks for relational learning: an experimental comparison (2011, methodology): “Supervised connectionist models for graph processing have been used in several application domains, including protein structure prediction [Baldi and Pollastri, 2004,Money and Pollastri, 2009], QSAR [Micheli et al., 2001], theorem proving [Goller, 1997], image classification and object localization ”
The Graph Neural Network Model (2009, background): “The neural architectures, which have been suggested for realizing and , include multilayered FNNs [17], [19], cascade correlation [68], and self-organizing maps [20], [69].”