From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming

Mendyk, Aleksander; Güres, Sinan; Jachowicz, Renata; Szlęk, Jakub; Polak, Sebastian; Wiśniowska, Barbara; Kleinebudde, Peter

doi:10.1155/2015/863874

Cited by 19 publications

(6 citation statements)

References 30 publications

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“…In fact, neural networks have a long and fruitful history in drug discovery and design. Because they bear the risk of being easily over‐trained and are perceived as a “black box”, they have often been substituted by other approaches such as SVM models 27,79. As noted by Winkler in a review article from 2004, continuous methodological advances in the field of neural networks alleviate some of the pitfalls and may have much to offer for hit and lead discovery 80.…”

Section: Conclusion: Deep Learning To the Rescue?mentioning

confidence: 99%

Deep Learning in Drug Discovery

Gawehn¹,

Hiss²,

Schneider³

2015

Molecular Informatics

615

398

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Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.

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Section: Conclusion: Deep Learning To the Rescue?mentioning

confidence: 99%

Deep Learning in Drug Discovery

Gawehn¹,

Hiss²,

Schneider³

2015

Molecular Informatics

615

398

View full text Add to dashboard Cite

show abstract

“…The original data set contains six input features while the equation represents the two most important ones. This is an example of feature selection behaviour by rgp, which has been observed in other instances as well (Mendyk et al 2015). Feature selection densifies the effect of crucial inputs in the system and discards the trivial ones in an attempt to capture more information in the model yet making it simpler.…”

Section: Resultsmentioning

confidence: 56%

Transparent computational intelligence models for pharmaceutical tableting process

Khalid

Tuszyński

Kazemi

et al. 2016

Complex Adapt Syst Model

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Purpose Pharmaceutical industry is tightly regulated owing to health concerns. Over the years, the use of computational intelligence (CI) tools has increased in pharmaceutical research and development, manufacturing, and quality control. Quality characteristics of tablets like tensile strength are important indicators of expected tablet performance. Predictive, yet transparent, CI models which can be analysed for insights into the formulation and development process. Methods This work uses data from a galenical tableting study and computational intelligence methods like decision trees, random forests, fuzzy systems, artificial neural networks, and symbolic regression to establish models for the outcome of tensile strength. Data was divided in training and test fold according to ten fold cross validation scheme and RMSE was used as an evaluation metric. Tree based ensembles and symbolic regression methods are presented as transparent models with extracted rules and mathematical formula, respectively, explaining the CI models in greater detail. Results CI models for tensile strength of tablets based on the formulation design and process parameters have been established. Best models exhibit normalized RMSE of 7 %. Rules from fuzzy systems and random forests are shown to increase transparency of CI models. A mathematical formula generated by symbolic regression is presented as a transparent model. Conclusions CI models explain the variation of tensile strength according to formulation and manufacturing process characteristics. CI models can be further analyzed to extract actionable knowledge making the artificial learning process more transparent and acceptable for use in pharmaceutical quality and safety domains.

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“…The original dataset contains six input features while the equation represents the three most important ones. This is an example of feature selection behavior by RGP, which has been observed in other instances as well [14]. Feature selection identifies the effect of crucial inputs in the system and discards the trivial ones in an attempt to capture more information in the model.…”

Section: A Models For Tensile Strengthmentioning

confidence: 71%

From Black-Box to Transparent Computational Intelligence Models: A Pharmaceutical Case Study

Khalid

Tuszyński

Szlęk

et al. 2015

2015 13th International Conference on Frontiers of Information Technology (FIT)

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Pharmaceutical Industry is tightly regulated owing to health concerns. Over the years, the use of computational intelligence (CI) tools has increased in the pharmaceutical research and development, manufacturing, and quality control. CI models for tensile strength of tablets based on the formulation design and manufacturing parameters have been established. Best models exhibit NRMSE of 7.2%. Implicitly, CI tools work in a black -box fashion which makes it difficult to inspect the model. Owing to quality and safety concerns, it is imperative for the pharmaceutical industry and the regulatory authorities to have knowledge of how the CI models work. This work uses data from a galencial tableting study to establish models for the outcome of tensile strength from various CI techniques and makes an attempt to make the models as transparent as possible. Tree based ensembles and symbolic regression methods are presented as transparent models with extracted rules and mathematical formula, respectively, explaining the CI models in greater detail.

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From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming

Cited by 19 publications

References 30 publications

Deep Learning in Drug Discovery

Deep Learning in Drug Discovery

Transparent computational intelligence models for pharmaceutical tableting process

From Black-Box to Transparent Computational Intelligence Models: A Pharmaceutical Case Study

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