Journal ArticleDOI
Robust QSAR models using Bayesian regularized neural networks.
Frank R. Burden,David A. Winkler +1 more
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TLDR
Bayesian regularized artificial neural networks (BRANNs) have the potential to solve a number of problems which arise in QSAR modeling such as: choice of model; robustness of models; choice of validation set; size of validation effort; and optimization of network architecture.Abstract:
We describe the use of Bayesian regularized artificial neural networks (BRANNs) in the development of QSAR models. These networks have the potential to solve a number of problems which arise in QSAR modeling such as: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors is illustrated.read more
Citations
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Journal ArticleDOI
The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
TL;DR: A set of simple guidelines for developing validated and predictive QSPR models is presented, highlighting the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and some algorithms that can be used for this purpose.
Journal ArticleDOI
Deep learning for computational chemistry
TL;DR: Deep neural networks have been widely applied in the field of computational chemistry, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction as discussed by the authors.
Journal ArticleDOI
Molecular similarity: a key technique in molecular informatics.
Andreas Bender,Robert C. Glen +1 more
TL;DR: The concept of molecular similarity, its various definitions and uses and how these have evolved in recent years are evaluated and in some cases challenge accepted views and uses are challenged.
Book ChapterDOI
Bayesian regularization of neural networks.
Frank R. Burden,David A. Winkler +1 more
TL;DR: Bayesian regularized artificial neural networks (BRANNs) as mentioned in this paper are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation.
Journal ArticleDOI
Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
TL;DR: This paper clarifies some apparent confusion over the use of the coefficient of determination, R(2), as a measure of model fit and predictive power in QSAR and QSPR modeling and recommends a clearer and simpler alternative method to characterize model predictivity.
References
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Journal ArticleDOI
p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure
Corwin Hansch,Toshio Fujita +1 more
Journal ArticleDOI
Can we learn to distinguish between "drug-like" and "nondrug-like" molecules?
TL;DR: A Bayesian neural network is used to distinguish between drugs and nondrugs and it is proposed to use the models to design combinatorial libraries to supplement standard diversity approaches to library design.
Journal ArticleDOI
2-Arylpyrazolo[4,3-c]quinolin-3-ones: novel agonist, partial agonist, and antagonist of benzodiazepines.
Journal ArticleDOI
Comparison of azabicyclic esters and oxadiazoles as ligands for the muscarinic receptor
Barry Sidney Beecham Pharmaceuticals Orlek,Frank E. Blaney,Frank Brown,Michael S. G. Clark,Michael S. Hadley,John M. Hatcher,Graham J. Riley,Howard Elliott Rosenberg,Harry John Wadsworth,Wyman Paul Adrian +9 more
TL;DR: The design and synthesis of novel azabicyclic methyl esters as ligands for the muscarinic receptor are described, which generally show improved affinity relative to the corresponding methyl ester.
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