Frank R. Burden

Molecular identification number for substructure searches

A method for producing molecular identification numbers for hydrogen-depleted organic structures from the eigenvalues of a connectivity matrix is presented. Over 20000 structures have been successfully tested, and the method can also be used produce a unique numbering for the atoms in a structure and to identify which atoms belong to each of the substructures of a disconnected main structure

Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties

Quantitative Structure Property Relationship Modeling of Diverse Materials Properties Tu Le, V. Chandana Epa, Frank R. Burden, and David A. Winkler* CSIRO Materials Science and Engineering, Bag 10, Clayton South MDC 3169, Australia CSIRO Materials Science and Engineering, 343 Royal Parade, Parkville 3052, Australia Monash Institute of Pharmaceutical Sciences, 381 Royal Parade, Parkville 3052, Australia

Fourier Transform Infrared microspectroscopy and chemometrics as a tool for the discrimination of cyanobacterial strains

Abstract Fourier Transform Infrared (FTIR) microspectroscopy, in combination with chemometrics, was investigated as a novel method to discriminate between cyanobacterial strains. In total, 810 absorbance spectra were recorded from one eukaryotic and five cyanobacterial taxa spanning three genera and including two strains of one species, Microcystis aeruginosa . Principal Component Analysis (PCA) based classification techniques such as Soft Independent Modelling of Class Analogy (SIMCA) and K-Nearest Neighbours (KNN) were investigated. Different spectral regions using derivative spectra were investigated to find the best combinations for classification. The highest rate of correct classifications (99–100%) was achieved using first derivative spectra with a spectral region of 1800–950 cm −1 for both the SIMCA and KNN. A dendrogram constructed using averaged spectra of the six taxa studied showed that the two strains of Microcystis aeruginosa exhibited the highest degree of similarity, while the eukaryotic taxon was the most dissimilar from the prokaryotic taxa.

Use of Automatic Relevance Determination in QSAR Studies Using Bayesian Neural Networks

We describe the use of Bayesian regularized artificial neural networks (BRANNs) coupled with automatic relevance determination (ARD) in the development of quantitative structure-activity relationship (QSAR) models. These BRANN-ARD networks have the potential to solve a number of problems which arise in QSAR modeling such as the following: choice of model; robustness of model; choice of validation set; size of validation effort; and optimization of network architecture. The ARD method ensures that irrelevant or highly correlated indices used in the modeling are neglected as well as showing which are the most important variables in modeling the activity data. The application of the methods to QSAR of compounds active at the benzodiazepine and muscarinic receptors as well as some toxicological data of the effect of substituted benzenes on Tetetrahymena pyriformis is illustrated.

Modeling Biological Activities of Nanoparticles

Products are increasingly incorporating nanomaterials, but we have a poor understanding of their adverse effects. To assess risk, regulatory authorities need more experimental testing of nanoparticles. Computational models play a complementary role in allowing rapid prediction of potential toxicities of new and modified nanomaterials. We generated quantitative, predictive models of cellular uptake and apoptosis induced by nanoparticles for several cell types. We illustrate the potential of computational methods to make a contribution to nanosafety.

An Investigation into FTIR Spectroscopy as a Biodiagnostic Tool for Cervical Cancer

Each year in Australia alone, 2500 women develop full invasive cervical carcinoma, and 3S0 of these die of the disease [1]. The current screening method is the Papanicolaou smear test or “Pap smear”, which although widely accepted as a practical method gives up to 20% false negative results [2]. Recently Holmes and Mountford [3] have investigated the potential of NMR spectroscopy in this field, while Wong and Rigas [4] have applied infrared spectroscopy to the analysis of exfoliated cervical cells and shown that it is a promising tool for screening. The aim of the present ongoing study is to assess the usefulness of FTIR as a tool for discriminating between normal cervical cells, cells with differing degrees of dysplasia, and cells that are malignant.

New QSAR Methods Applied to Structure−Activity Mapping and Combinatorial Chemistry

A comparison is made of a number of computationally efficient molecular indices with a view to the screening of very large virtual data sets of molecules. The use of Bayesian regularized neural networks is discussed, and their virtue in eliminating the need for validation sets, and potentially even test sets, is emphasized. The concept of a virtual receptor is introduced, and this is illustrated by the results of screening a database of 40 000 molecules.

Towards chromate-free corrosion inhibitors: structure–property models for organic alternatives

Progressive restrictions on the use of toxic chromate-based corrosion inhibitors present serious technical challenges. The most critical of these is the lack of non-toxic ‘green’ alternatives that offer comparable performance, particularly on corrosion-prone aluminium alloys such as the 2000 and 7000 series. In this study we used computational modelling methods to investigate the properties of a range of small organic, potentially safer inhibitors and their interactions with technologically relevant alloy surfaces. We have generated robust and predictive computational models of corrosion inhibition for a structurally related data set of organic compounds from the literature. Our studies have correlated molecular features of the inhibitor molecules with inhibition and identified those features that have the greatest impact on experimentally determined corrosion inhibition. This information can be used to drive guided decision making for in silico or experimental screening of molecules for their corrosion inhibition efficiency, while considering more carefully their environmental consequences.

Quantitative structure-activity relationship studies using Gaussian processes.

A Gaussian process method (GPM) is described and applied to the production of some QSAR models. These models have the potential to solve a number of problems which arise in QSAR modeling in that no parameters have to be supplied and only one hyperparameter is used in finding the optimal solution. The application of the method to QSAR is illustrated using data sets of compounds active at the benzodiazepine and muscarinic receptors as well as the data set of the toxicity of substituted benzenes to the ciliate, Tetrahymena Pyriformis.

Robust QSAR Models from Novel Descriptors and Bayesian Regularised Neural Networks

Abstract The QSAR method, using multivariate statistics, was developed by Hansch and Fujita, and it has been successfully applied to many drug and agrochemical design problems. As well as speed and simplicity QSAR has advantages of being capable of accounting for some transport and metabolic processes which occur once the compound is administered. Until recently QSAR analyses have used relatively simple molecular descriptors based on substituent constants (e.g., Hammett constants, π, or molar refractivities), physicochemical properties (e.g., partition coefficients), topological indices (e.g., Randic and Weiner indices). Recently several new representations have been devised: atomistic; molecular eigenvalues and BCUT indices derived therefrom; E-state fields; topological autocorrelation vectors; various molecular fragment-based hash codes. These representations have advantages in speed of computation, in more accurately representing molecular properties most relevant to activity, or in being more generally applicable to diverse chemical classes acting at a common receptor, than traditional representations. Historically, linear regression methods such as MLR (multiple linear regression) and PLS (partial least squares) have been used to develop QSAR models. Regression is an “ill-posed” problem in statistics, which sometimes results in QSAR models exhibiting instability when trained with noisy data. In addition traditional regression techniques often require subjective decisions to be made on the part of the investigator as to the likely non-linear relationship between structure and activity, and whether there are cross-terms. Regression methods based on neural networks offer some advantages over MLR methods as they can account for non-linear SARs, and can deal with linear dependencies which sometimes appear in real SAR problems. However, some problems still exist in the development of SAR models using conventional backpropagation neural networks. We have used a specific type of neural network, the Bayesian Regularized Artificial Neural Network (BRANN), in the development of SAR models. The advantage of BRANN is that the models are robust and the validation process, which scales as O(N2) in normal regression methods, is unnecessary. These networks have the potential to solve a number of problems which arise in QSAR modelling 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 will be illustrated.