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      Drug Design, Development and Therapy (submit here)

      This international, peer-reviewed Open Access journal by Dove Medical Press focuses on the design and development of drugs, as well as the clinical outcomes, patient safety, and programs targeted at the effective and safe use of medicines. Sign up for email alerts here.

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      Attractor – a new turning point in drug discovery


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          Drug discovery for complex diseases can be viewed as a challenging problem in which the influence of compounds on dynamic features of disease system should be considered, especially the strategies escaping from the disease attractors. Moreover, escaping from the disease-related attractors has been proved to be a cue for the treatment of the complex diseases. The drug discovery methodology based on the attractor theory indicates new solutions for target identification, drug discovery and drug combination design. The methodology is based on the holism level of the organism and the features of system dynamics, so it has advantages for the classification of complex diseases and drug discovery. Currently, research results of this method have increased, which expand the insight scope for drug discovery. This article introduces the major drug discovery methods in the history of pharmacy development and their characteristics, so as to illustrate the reasons and inevitability of the appearance of attractor method, its position in the history of pharmacy development, and its advantages for drug discovery and design, thereby to prove that the attractor method can indeed become the next major drug development method. In addition, it provides a comprehensive description about the concept of attractor, the pipeline of attractor analysis, the common methods of each process and its research progress, so as to provide a macroscopic framework and optional methods and tools for the follow-up researchers.

          Most cited references41

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            Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks.

            Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes. We introduce Probabilistic Boolean Networks (PBN) that share the appealing rule-based properties of Boolean networks, but are robust in the face of uncertainty. We show how the dynamics of these networks can be studied in the probabilistic context of Markov chains, with standard Boolean networks being special cases. Then, we discuss the relationship between PBNs and Bayesian networks--a family of graphical models that explicitly represent probabilistic relationships between variables. We show how probabilistic dependencies between a gene and its parent genes, constituting the basic building blocks of Bayesian networks, can be obtained from PBNs. Finally, we present methods for quantifying the influence of genes on other genes, within the context of PBNs. Examples illustrating the above concepts are presented throughout the paper.
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              The logical analysis of continuous, non-linear biochemical control networks.


                Author and article information

                Drug Des Devel Ther
                Drug Des Devel Ther
                Drug Design, Development and Therapy
                22 August 2019
                : 13
                : 2957-2968
                [1 ]Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine , Beijing, People’s Republic of China
                Author notes
                Correspondence: Yun WangDepartment of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine , Beijing102488, People’s Republic of ChinaTel +86 1 369 305 8206Fax +86 108 473 8620 Email wangyun@bucm.edu.cn
                © 2019 Hou et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                : 23 May 2019
                : 28 July 2019
                Page count
                Figures: 3, Tables: 3, References: 62, Pages: 12

                Pharmacology & Pharmaceutical medicine
                drug discovery,design,attractor,system dynamics,attractor calculation,state transition


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