Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Scott Boyer"'
Publikováno v:
Regulatory Toxicology and Pharmacology. 71:279-284
Conformal prediction is presented as a framework which fulfills the OECD principles on (Q)SAR. It offers an intuitive extension to the application of machine-learning methods to structure-activity data where focus is on predictions with pre-defined c
Publikováno v:
Journal of Chemical Information and Modeling. 54:837-843
Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets.
Publikováno v:
Annals of Mathematics and Artificial Intelligence. 74:117-132
QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using machine learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testi
Publikováno v:
Journal of Chemical Information and Modeling. 53:2001-2017
State-of-the-art quantitative structure-activity relationship (QSAR) models are often based on nonlinear machine learning algorithms, which are difficult to interpret. From a pharmaceutical perspective, QSARs are used to enhance the chemical design p
Autor:
Scott Boyer
Publikováno v:
Alternatives to Laboratory Animals. 37:467-475
With the ever increasing volume of data available to scientists in drug discovery and development, the opportunity to leverage an increasing amount of these data in the assessment of drug safety is clear. The challenge in an environment of increasing
Publikováno v:
Journal of Chemical Information and Modeling. 49:2559-2563
The present work describes a method that optimizes a compound on the basis of the interpretation of quantitative structure-activity relationship models. The method has been applied to query compounds that have a mutagenicity liability. The substructu
Publikováno v:
Journal of Chemical Information and Modeling. 49:2551-2558
A method for local interpretation of QSAR models is presented and applied to an Ames mutagenicity data set. In the work presented, local interpretation of Support Vector Machine and Random Forest models is achieved by retrieving the variable correspo
Autor:
Romualdo Benigni, Kirk Arvidson, P Dierkes, Chihae Yang, Ann M. Richard, Naomi L. Kruhlak, Raymond A. Kemper, S Aveston, Joseph F. Contrera, James F. Rathman, Scott Boyer, R D Benz, Catrin Hasselgren, Edwin J. Matthews, X Han, J Jaworska
Publikováno v:
Toxicology Mechanisms and Methods. 18:277-295
Genetic toxicity data from various sources were integrated into a rigorously designed database using the ToxML schema. The public database sources include the U.S. Food and Drug Administration (FDA) submission data from approved new drug applications
Autor:
Kamaldeep K. Chohan, Catrin Hasselgren Arnby, M. Paul Gleeson, Claire L. Gavaghan, Andrew M. Davis, Nan Albertson, Stuart W. Paine, Scott Boyer, Cecilia Kankkonen
Publikováno v:
Journal of Computer-Aided Molecular Design. 21:559-573
In-silico models were generated to predict the extent of inhibition of cytochrome P450 isoenzymes using a set of relatively interpretable descriptors in conjunction with partial least squares (PLS) and regression trees (RT). The former was chosen due
Publikováno v:
Journal of chemical information and modeling. 54(6)
Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Secon