F
Frank R. Burden
Researcher at Commonwealth Scientific and Industrial Research Organisation
Publications - 67
Citations - 3796
Frank R. Burden is an academic researcher from Commonwealth Scientific and Industrial Research Organisation. The author has contributed to research in topics: Artificial neural network & Quantitative structure–activity relationship. The author has an hindex of 30, co-authored 67 publications receiving 3352 citations. Previous affiliations of Frank R. Burden include Flinders University & Monash University.
Papers
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Journal ArticleDOI
Predicting maximum bioactivity by effective inversion of neural networks using genetic algorithms
TL;DR: This paper proposes one method for solving the problem of predicting the required molecular properties of a more active molecule by using genetic algorithms and explores neural networks potential as a method for solve this problem.
Bayesian Regularization of Neural Networks.
Frank R. Burden,David A. Winkler +1 more
TL;DR: This chapter outlines the equations that define the BRANN method plus a flowchart for producing a BRANN-QSAR model, and some results of the use of BRANNs on a number of data sets are illustrated and compared with other linear and nonlinear models.
Book ChapterDOI
Application of Neural Networks to Large Dataset QSAR, Virtual Screening, and Library Design
David A. Winkler,Frank R. Burden +1 more
Journal ArticleDOI
Predictive Human Intestinal Absorption QSAR Models Using Bayesian Regularized Neural Networks
Mitchell J. Polley,Mitchell J. Polley,Frank R. Burden,Frank R. Burden,David A. Winkler,David A. Winkler +5 more
TL;DR: This work modelled intestinal absorption using several types of molecular descriptors and a non-linear Bayesian regularized neural network and shows very good predictive properties and is able to account for essentially all of the variance in the data that is not due to experimental error.
Journal ArticleDOI
Predicting the complex phase behavior of self-assembling drug delivery nanoparticles.
TL;DR: Computational models for three drug delivery carriers loaded with 10 drugs at six concentrations and two temperatures predicted phase behavior for 11 new drugs and subsequent synchrotron small-angle X-ray scattering experiments validated the predictions.