ChemInform Abstract: Four‐Dimensional Quantitative Structure—Activity Relationship Analysis of a Series of Interphenylene 7‐Oxabicycloheptane Oxazole Thromboxane A<sub>2</sub> Receptor Antagonists.

Albuquerque, M.G.; Hopfinger, A. J.; Barreiro, Eliezer J.; Alencastro, Ricardo Bicca de

doi:10.1002/chin.199905249

Cited by 4 publications

(4 citation statements)

References 1 publication

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“…Such an approach allows for the rational design of subsequent chemical modifications as opposed to enumeration and testing of a much larger number of compounds to arrive at the same endpoint. This goal has been achieved by a variety of approaches, including CoMFA, 10 CoMSIA, 11 4D QSAR, 12 Phase QSAR, 13 Hologram QSAR, 14 and StarDrop, 15 although only the latter two are independent of 3D structure.…”

Section: ■ Introductionmentioning

confidence: 99%

Kernel-Based Partial Least Squares: Application to Fingerprint-Based QSAR with Model Visualization

Sherman

Dixon

2013

J. Chem. Inf. Model.

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Numerous regression-based and machine learning techniques are available for the development of linear and nonlinear QSAR models that can accurately predict biological endpoints. Such tools can be quite powerful in the hands of an experienced modeler, but too frequently a disconnect remains between the modeler and project chemist because the resulting QSAR models are effectively black boxes. As a result, learning methods that yield models that can be visualized in the context of chemical structures are in high demand. In this work, we combine direct kernel-based PLS with Canvas 2D fingerprints to arrive at predictive QSAR models that can be projected onto the atoms of a chemical structure, allowing immediate identification of favorable and unfavorable characteristics. The method is validated using binding affinities for ligands from 10 different protein targets covering 7 distinct protein families. Models with significant predictive ability (test set Q(2)> 0.5) are obtained for 6 of 10 data sets, and fingerprints are shown to consistently outperform large collections of classical physicochemical and topological descriptors. In addition, we demonstrate how a simple bootstrapping technique may be employed to obtain uncertainties that provide meaningful estimates of prediction accuracy.

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Section: ■ Introductionmentioning

confidence: 99%

Kernel-Based Partial Least Squares: Application to Fingerprint-Based QSAR with Model Visualization

Sherman

Dixon

2013

J. Chem. Inf. Model.

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“…This is an elegant way to assess a “bioactive” conformation that is not necessarily associated with the local minima of the free molecule and can also account for stereoisomeres and different protonation states of ionizable groups . Work by the Hopfinger’s group ,− inspired development of 5D QSAR accounting for a multiple representation of induced-fit hypotheses , and 6D QSAR that evaluates different solvation models …”

Section: Models Descriptionmentioning

confidence: 99%

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

Varnek

Baskin

2012

J. Chem. Inf. Model.

213

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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“…In this kind of approach, the so-called grid cell occupancy descriptors are used i.e. the occupancy frequencies of the different atom types (any type, nonpolar, polar-positive charge, polar-negative charge, hydrogen bond acceptor, hydrogen bond donor and aromatic) in the cubic grid cells [33,34]. An example of successful application of the RI-4D-QSAR approach is the study of 5'-arylthiourea thymidine analogues, showing inhibitory activity against thymidine monophosphate kinase from M. tuberculsosis (TMPKmt) [35].…”

Section: Directions In Drug Designmentioning

confidence: 99%

Towards Understanding Drugs on the Molecular Level to Design Drugs of Desired Profiles

Latosińska¹,

Latosińska²

2011

Drug Discovery and Development - Present and Future

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ChemInform Abstract: Four‐Dimensional Quantitative Structure—Activity Relationship Analysis of a Series of Interphenylene 7‐Oxabicycloheptane Oxazole Thromboxane A₂ Receptor Antagonists.

Cited by 4 publications

References 1 publication

Kernel-Based Partial Least Squares: Application to Fingerprint-Based QSAR with Model Visualization

Kernel-Based Partial Least Squares: Application to Fingerprint-Based QSAR with Model Visualization

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

Towards Understanding Drugs on the Molecular Level to Design Drugs of Desired Profiles

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ChemInform Abstract: Four‐Dimensional Quantitative Structure—Activity Relationship Analysis of a Series of Interphenylene 7‐Oxabicycloheptane Oxazole Thromboxane A2 Receptor Antagonists.

Cited by 4 publications

References 1 publication

Kernel-Based Partial Least Squares: Application to Fingerprint-Based QSAR with Model Visualization

Kernel-Based Partial Least Squares: Application to Fingerprint-Based QSAR with Model Visualization

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

Towards Understanding Drugs on the Molecular Level to Design Drugs of Desired Profiles

Contact Info

Product

Resources

About

ChemInform Abstract: Four‐Dimensional Quantitative Structure—Activity Relationship Analysis of a Series of Interphenylene 7‐Oxabicycloheptane Oxazole Thromboxane A₂ Receptor Antagonists.