Introduction to Artificial Neural Network (ANN) as a Predictive Tool for Drug Design, Discovery, Delivery, and Disposition

Puri, Munish; Solanki, Aum; Padawer, Timothy; Tipparaju, Srinivas M.; Moreno, Wilfrido; Pathak, Yashwant

doi:10.1016/b978-0-12-801559-9.00001-6

Cited by 48 publications

(30 citation statements)

References 25 publications

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“…ANNs functioning mimics that of biological neurons, the networks consist of many layers allowing input reception and processing and output delivery. This technique can be used for solving classification or regression problems 18 . To build the second part of glycolysis in ANNs, different types of software are employed: RStudio (Version 1.1.456), an open-source integrated development environment for R 19 and two packages: NeuralNet (Version 1.44.2) and Nnet (Version 7.3–12) 20 , 21 .…”

Section: Methodsmentioning

confidence: 99%

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Lo-Thong

Charton

Cadet³

et al. 2020

Sci Rep

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Materials and methods Second part of glycolysis experimental data. Experimental data of PGAM, ENO and PPDK activities and pathway flux (J obs) are obtained from plots of a previous study 8. The free online software WebPlotDigitizer (Version 4.1, https ://autom eris.io/WebPl otDig itize r/) is used to extract data from plots. These data are available in Tables S1 and S2. Artificial neural networks (ANNs). ANNs functioning mimics that of biological neurons, the networks consist of many layers allowing input reception and processing and output delivery. This technique can be used for solving classification or regression problems 18. To build the second part of glycolysis in ANNs, different types of software are employed: RStudio (Version 1.1.456), an open-source integrated development environment for R 19 and two packages: NeuralNet (Version 1.44.2) and Nnet (Version 7.3-12) 20,21. Complex pathway SImulator (COPASI) metabolic networks. A first metabolic network of the studied pathway was kindly provided by the authors of a previous study 8. This model is developed on GEPASI 22 , an old software for metabolic pathway modeling, replaced by COPASI since 2002. The second part of the glycolysis is also modeled by using the open source software called COPASI (Version 4.24) 23. This software is used for metabolic network design, analysis and optimization. The resulting metabolic networks are based on the use of enzyme properties (kinetic parameters and mechanism-based rate equations). Ethics approval and consent to participate. Not applicable. Methodology Black-white-and grey-box approach procedure. To conduct the present study, a specific methodol

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Section: Methodsmentioning

confidence: 99%

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Lo-Thong

Charton

Cadet³

et al. 2020

Sci Rep

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“…Moreover these algorithms can be trained to distinguish active drugs from decoys that do not have known drug activity [278]. Artificial neural networks (ANNs) have been used in drug discovery as a powerful predictive tool for non-linear systems [279]. For example, ANNs were used to construct the QSAR of a set of known aldose reductase inhibitors and biological activities of new molecules were predicted based on the QSAR [280].…”

Section: Reviewmentioning

confidence: 99%

Computational methods in drug discovery

Leelananda

Lindert

2016

Beilstein J. Org. Chem.

470

309

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The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

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“…Machine learning is a family of computational methods that allows an algorithm to program itself using large sets of examples. Because the computer “learns” from these example sets of existing data, a system can become highly adept at processing and analyzing large data sets to track variables and produce estimates at a rate that would not be possible for humans or traditional statistical methods [21]. Taken together, data from remote monitoring sensors is combined with individual animal identification, referenced observations and production data, and then integrated in algorithms to provide credible information and alerts regarding pig welfare, health and productivity [22,23,24,25].…”

Section: Precision Livestock Farming: An Overviewmentioning

confidence: 99%

Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners

Benjamin

Yik

2019

Animals

141

132

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The burgeoning research and applications of technological advances are launching the development of precision livestock farming. Through sensors (cameras, microphones and accelerometers), images, sounds and movements are combined with algorithms to non-invasively monitor animals to detect their welfare and predict productivity. In turn, this remote monitoring of livestock can provide quantitative and early alerts to situations of poor welfare requiring the stockperson’s attention. While swine practitioners’ skills include translation of pig data entry into pig health and well-being indices, many do not yet have enough familiarity to advise their clients on the adoption of precision livestock farming practices. This review, intended for swine veterinarians and specialists, (1) includes an introduction to algorithms and machine learning, (2) summarizes current literature on relevant sensors and sensor network systems, and drawing from industry pig welfare audit criteria, (3) explains how these applications can be used to improve swine welfare and meet current pork production stakeholder expectations. Swine practitioners, by virtue of their animal and client advocacy roles, interpretation of benchmarking data, and stewardship in regulatory and traceability programs, can play a broader role as advisors in the transfer of precision livestock farming technology, and its implications to their clients.

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Introduction to Artificial Neural Network (ANN) as a Predictive Tool for Drug Design, Discovery, Delivery, and Disposition

Cited by 48 publications

References 25 publications

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Computational methods in drug discovery

Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners

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