Robust QSAR Models Using Bayesian Regularized Neural Networks

Burden, Frank R.; Winkler, David A.

doi:10.1021/jm980697n

Cited by 235 publications

(218 citation statements)

References 27 publications

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“…Baskin, Palyulin, & Zefirov, 2009;Sussillo & Barak, 2013) since a number of methodologies facilitating the interpretation of model outcomes have been developed (I. Baskin, Ait, Halberstam, Palyulin, & Zefirov, 2002;Burden & Winkler, 1999;Guha, Stanton, & Jurs, 2005). Also, it has to be pointed out that, just like other modelling techniques, ANN can be used together with GA-based feature selection algorithm in order to remove redundant variables during the model building process.…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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There is an increasing recognition that nanomaterials pose a risk to human health, and that the novel engineered nanomaterials (ENMs) in the nanotechnology industry and their increasing industrial usage poses the most immediate problem for hazard assessment, as many of them remain untested. The large number of materials and their variants (different sizes and coatings for instance) that require testing and ethical pressure towards non-animal testing means that expensive animal bioassay is precluded, and the use of (quantitative) structure activity relationships ((Q)SAR) models as an alternative source of hazard information should be explored.(Q)SAR modelling can be applied to fill the critical knowledge gaps by making the best use of existing data, prioritize physicochemical parameters driving toxicity, and provide practical solutions to the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of (Q)SAR technologies to ENMs toxicity prediction, and summarizes the published nano-(Q)SAR studies and outlines the challenges ahead for nano-(Q)SAR modelling. It provides a critical review of (1) the present status of the availability of ENMs characterization/toxicity data, (2) the characterization of nanostructures that meets the need of (Q)SAR analysis, (3) the summary of published nano-(Q)SAR studies and their limitations, (4) the in silico tools for (Q)SAR screening of nanotoxicity and (5) the prospective directions for the development of nano-(Q)SAR models.

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Section: Support Vector Machines (Svm)mentioning

confidence: 99%

(Q)SAR modelling of nanomaterial toxicity: A critical review

et al. 2015

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“…As the biofilm coverage is linearly related to the GFP fluorescence, [20] we used the logarithm of the fluorescence at the dependent variable property being modelled, as is usual practice in these types of machine learning models. The complexity of the neural network models was controlled using Bayesian regularization that employs either a Gaussian prior (BRANNGP) or a sparsity-inducing Laplacian prior (BRANNLP) [33][34][35][36] . The maximum of the Bayesian evidence for the model was used to stop the training of the neural network.…”

Section: Limit Of Detection Considerationsmentioning

confidence: 99%

“…The BRANNLP neural network also removes less relevant descriptors from the model to a degree determined by the sparsity setting selected. Details of the three modelling algorithms have been published previously [32][33][34] . No outliers were removed from the models unless noted.…”

Section: Limit Of Detection Considerationsmentioning

confidence: 99%

Modelling and Prediction of Bacterial Attachment to Polymers

Epa

Hook

Chang

et al. 2013

Adv Funct Materials

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Infection by pathogenic bacteria on implanted and indwelling medical devices during surgery causes large morbidity and mortality worldwide. Attempts to ameliorate this important medical issue have included development of antimicrobial surfaces on materials, 'no touch' surgical procedures, and development of materials with inherent low pathogen attachment.The search for new materials is increasingly being carried out by high throughput methods.Efficient methods for extracting knowledge from these large data sets are essential. We used data from a large polymer microarray exposed to three clinical pathogens to derive robust and predictive machine-learning models of pathogen attachment. The models could predict pathogen attachment for the polymer library quantitatively. The models also successfully 22222222224222 predicted pathogen attachment for a second-generation library, and identified polymer surface chemistries that enhance or diminish pathogen attachment.

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“…The mathematics of Bayesian regularization is challenging and is not repeated here as it is described in numerous publications. [7][8][9][11][12][13][14][15][44][45][46][47] …”

Section: Sparse Learning Methodsmentioning

confidence: 99%

“…Our team has developed effective computational design and modelling techniques over the past 20 years, [7][8][9][10][11][12][13][14][15] and we have employed them to generate substantial scientific and commercial impact, culminating in more than 20 patents. These methods have been used to design and optimise green pesticides [16][17][18][19][20] in collaboration with Du Pont and Schering Plough, have discovered new peptides and small molecules to control stem cells and cancers, [21][22][23][24][25][26][27] are accelerating the development of biomaterials for implantation and stem cell culture, [28][29][30][31][32][33] have provided new scientific insight into the potential adverse properties of nanomaterials, [34][35][36][37][38] have yielded clinical candidates for Australian SMEs and international companies, and were instrumental in the discovery of antibiotics [39,40] with a novel mode of action for the biotechnology spin off company, Betabiotics.…”

Section: The Threat and Promise Of The Vastness Of Chemical Spacementioning

confidence: 99%

Adrien Albert Award: How to Mine Chemistry Space for New Drugs and Biomedical Therapies

Winkler

2015

Aust. J. Chem.

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It is clear that the sizes of chemical, 'drug-like', and materials spaces are enormous. If scientists working in established therapeutic, and newly established regenerative medicine fields are to discover better molecules or materials, they must find better ways of probing these enormous spaces. There are essentially five ways that this can be achieved: combinatorial and high throughput synthesis and screening approaches; fragment-based methods; de novo molecular design, design of experiments, diversity libraries; supramolecular approaches; evolutionary approaches. These methods either synthesise materials and screen them more quickly, or constrain chemical spaces using biology or other types of 'fitness functions'. High throughput experimental approaches cannot explore more than a minute part of chemical space. We are nevertheless entering into an era that is data dominated. High throughput experiments, robotics, automated crystallographic beam lines, combinatorial and flow synthesis, high content screening, and the 'omics' technologies are providing a flood of data, and efficient methods for extracting meaning from it are essential. This paper describes how new developments in mathematics have provided excellent, robust computational modelling tools for exploring large chemical spaces, for extracting meaning from large datasets, for designing new bioactive agents and materials, and for making truly predictive, quantitative models of the properties of molecules and materials for use in therapeutic and regenerative medicine. We describe these broadly applicable modelling tools and provide examples of their application to serum free stem cell culture, pathogen resistant polymers for implantable devices, new markers and biological mechanisms derived from mathematical analyses of gene array data, and pharmacokinetically important physicochemical properties of small molecules. We also discuss biologically conserved peptide motifs as a design framework for small molecule drugs and give examples of the application of this concept to drug design.

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Robust QSAR Models Using Bayesian Regularized Neural Networks

Cited by 235 publications

References 27 publications

(Q)SAR modelling of nanomaterial toxicity: A critical review

(Q)SAR modelling of nanomaterial toxicity: A critical review

Modelling and Prediction of Bacterial Attachment to Polymers

Adrien Albert Award: How to Mine Chemistry Space for New Drugs and Biomedical Therapies

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