Neural Networks in QSAR and Drug Design

By J. Devillers. 1996

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Citations in the corpus

Multi-task Neural Networks for QSAR Predictions (2014, methodology): “Non-Bayesian neural networks have also been applied to QSAR [Devillers, 1996], but often had only a single small hidden layer (presumably to avoid overﬁtting issues that Bayesian versions obviate).”
Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology (2018, background): “tures with relative directions without signicant loss of the original information. Formally, a(l) j;v!s = ReLU W v!s x (l) j;v + b v!s r ji (3) a(l) j;s!v = ReLU W s!v x (l) j;s + b s!v r ji (4) where stands for dot product and for tensor product. Note that, unlike appositional operations, scala”
Frequent substructure-based approaches for classifying chemical compounds (2003, methodology): “This includes techniques based on principle component regression and partial least squares [81], neural networks [5], [27], [57], [85], recursive partitioning [4], [18], [72], phylogenetic-like trees [64], [78], binary QSAR [30], [51], linear discriminant analysis [67], and support vector machines [”
Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks (2001, background): “[1, 2] Neural networks are often the preferred solution if the dependence cannot be expressed by a simple mathematical equation or this equation is unknown and also not important for solving the problem.”
Overview of Clinical Decision Support Systems (2016, background): “Research in neural networks has been going on since the 1940s.(20) Artificial neural networks (ANN) simulate human thinking and learn from examples.”
Classification of small molecules by two- and three-dimensional decomposition kernels (2007, methodology): “…to consider several alternative algorithms for solving the supervised learning problem including linear regression, ridge regression (Hawkins et al., 2001), artificial neural networks (Devillers, 1996; Jalali-Heravi and Parastar, 2000) and support vector machines (SVMs) (Burbidge et al., 2001).”
Can we really do computer-aided drug design? (2011, background): “But how can we further improve the accuracy of predictive methods? The computational algorithms for statistical modelling have grown increasingly sophisticated and now include a wide range of powerful non-linear fitting techniques, such as Random Forests [13], Support Vector Machines [14], Artificia”