Bayesian Neural Networks for Aroma Classification.

Klocker, Johanna; Wailzer, Bettina; Buchbauer, Gerhard; Wolschann, Peter

doi:10.1002/chin.200305221

Cited by 4 publications

(4 citation statements)

References 1 publication

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“…The most popular machine learning methods involving Bayesian learning are Bayesian regression, Bayesian neural networks, , and Gaussian processes . The advantages of Bayesian learning algorithms have been demonstrated in recent QSAR studies involving Bayesian neural networks − and Gaussian processes. − …”

Section: Learning Approaches: Modes Of Statistical Inference and Mode...mentioning

confidence: 99%

Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?

Varnek

Baskin

2012

J. Chem. Inf. Model.

215

170

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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).

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Section: Learning Approaches: Modes Of Statistical Inference and Mode...mentioning

confidence: 99%

Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?

Varnek

Baskin

2012

J. Chem. Inf. Model.

215

170

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“…The calculated descriptors included molecular attributes, molecular indices, atom counts, and VAMP parameters (Cronin and Schultz, 2001;Dessalew, 2007). The TSAR methodology assumes that a suitable sampling of these structural descriptors provides all the information needed for understanding their biological properties (Klocker et al, 2002;Kovatcheva et al, 2003).…”

Section: Data Set Preparation and Data Reductionmentioning

confidence: 99%

Quantitative structure activity relationship (QSAR) of N 6-substituted adenosine receptor agonists as potential antihypertensive agents

et al. 2010

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Quantitative structure activity relationship studies are indubitably of great importance in modern chemistry. In pursuit of better antihypertensive agents, QSAR studies were performed on a series of 48 N 6 -substituted adenosines analogs. The models were developed using multiple linear regression (MLR) and partial least square (PLS) with many descriptors like electronic, topological, and lipophilic. QSAR models were evaluated for statistical significance and predictive power. The MLR and PLS generated comparable results with good predictive ability and all the other statistical values such as r, r 2 , r cv 2 , F, and s values were 0.92, 0.84, 0.83, 61.74, 0.24, respectively, for MLR and r cv 2 and statistical significance values of 0.82 and 0.80, respectively, for PLS were satisfactory. The model indicates that the Verloop L, total lipole, VAMP nuclear energy, plays an important role in determining the antihypertensive activity of N 6 -substituted adenosine receptor agonists. The model developed in the present study will be useful in the design of more potent N 6 -substituted adenosine receptor agonists as antihypertensive agent.

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“…TSAR is an integrated analysis package for the interactive investigation of quantitative structure-activity relationships (Klocker et al, 2002;Kovatcheva et al, 2003). It provides the functions required to carry out any QSAR investigation whether in pharmaceutical or other areas of application.…”

Section: Introductionmentioning

confidence: 99%

QSAR study on aminophenylbenzamides and acrylamides as histone deacetylase inhibitors: An insight into the structural basis of antiproliferative activity

Dessalew

2007

Med Chem Res

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Histone deacetylases have emerged as important drug target with a multitude of therapeutic potentials for their inhibitors. With the purpose of designing new chemical entities with enhanced inhibitory potencies against histone deacetylases, a two-dimensional (2D) quantitative structure-activity relationship (QSAR) study was carried out on aminophenylbenzamides and acrylamide derivatives as inhibitors of these deacetylases. The developed model was validated by standard QSAR parameters and through a detailed structural analysis of how it reproduces and explains the differences in the experimentally known activity data. The model showed a good correlative and predictive ability, with a cross-validated correlation coefficient of 0.594. The conventional and predictive correlation coefficients were found to be 0.725 and 0.577 for the antiproliferative activity of aminophenylbenzamides and acrylamide series, respectively. The study indicated that their antiproliferative activity is largely explained by the steric factors of the substituents, highlighting the role of the size and shape of the inhibitor in forming effective binding interactions with histone deacetylase. The models could be usefully employed to design inhibitors with greatly enhanced potency and selectivity.

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Bayesian Neural Networks for Aroma Classification.

Cited by 4 publications

References 1 publication

Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?

Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?

Quantitative structure activity relationship (QSAR) of N 6-substituted adenosine receptor agonists as potential antihypertensive agents

QSAR study on aminophenylbenzamides and acrylamides as histone deacetylase inhibitors: An insight into the structural basis of antiproliferative activity

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