<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>7315</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>904</pageFirst>
    <pageLast>911</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>45</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Computation of temperature-dependent dissociation rates of metastable protein–ligand complexes</title>
    <abstract language="eng">Molecular simulations are often used to analyse the stability of protein–ligand complexes. The stability can be characterised by exit rates or using the exit time approach, i.e. by computing the expected holding time of the complex before its dissociation. However determining exit rates by straightforward molecular dynamics methods can be challenging for stochastic processes in which the exit event occurs very rarely. Finding a low variance procedure for collecting rare event statistics is still an open problem. In this work we discuss a novel method for computing exit rates which uses results of Robust Perron Cluster Analysis (PCCA+). This clustering method gives the possibility to define a fuzzy set by a membership function, which provides additional information of the kind ‘the process is being about to leave the set’. Thus, the derived approach is not based on the exit event occurrence and, therefore, is also applicable in case of rare events. The novel method can be used to analyse the temperature effect of protein–ligand systems through the differences in exit rates, and, thus, open up new drug design strategies and therapeutic applications.</abstract>
    <parentTitle language="eng">Molecular Simulation</parentTitle>
    <identifier type="doi">10.1080/08927022.2019.1610949</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <author>Natalia Ernst</author>
    <submitter>Konstantin Fackeldey</submitter>
    <author>Konstantin Fackeldey</author>
    <author>Andrea Volkamer</author>
    <author>Oliver Opatz</author>
    <author>Marcus Weber</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compmol">Computational Molecular Design</collection>
    <collection role="persons" number="fackeldey">Fackeldey, Konstantin</collection>
    <collection role="persons" number="weber">Weber, Marcus</collection>
    <collection role="projects" number="BB3R">BB3R</collection>
    <collection role="persons" number="ernst">Ernst, Natalia</collection>
  </doc>
  <doc>
    <id>6699</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>126</pageFirst>
    <pageLast>128</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>35</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">In silico Methods - Computational Alternatives to Animal Testing</title>
    <abstract language="eng">A seminar and interactive workshop on “In silico Methods –&#13;
Computational Alternatives to Animal Testing” was held in&#13;
Berlin, Germany, organized by Annemarie Lang, Frank Butt-&#13;
gereit and Andrea Volkamer at the Charité-Universitätsmedizin&#13;
Berlin, on August 17-18, 2017. During the half-day seminar, the&#13;
variety and applications of in silico methods as alternatives to&#13;
animal testing were presented with room for scientific discus-&#13;
sions with experts from academia, industry and the German fed-&#13;
eral ministry (Fig. 1). Talks on computational systems biology&#13;
were followed by detailed information on predictive toxicology&#13;
in order to display the diversity of in silico methods and the&#13;
potential to embrace them in current approaches (Hartung and&#13;
Hoffmann, 2009; Luechtefeld and Hartung, 2017). The follow-&#13;
ing interactive one-day Design Thinking Workshop was aimed&#13;
at experts, interested researchers and PhD-students interested in&#13;
the use of in silico as alternative methods to promote the 3Rs&#13;
(Fig. 2). Forty participants took part in the seminar while the&#13;
workshop was restricted to sixteen participants.</abstract>
    <parentTitle language="eng">ALTEX</parentTitle>
    <identifier type="doi">10.14573/altex.1712031</identifier>
    <enrichment key="PeerReviewed">no</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Annemarie Lang</author>
    <submitter>Lisa Fischer</submitter>
    <author>Andrea Volkamer</author>
    <author>Laura Behm</author>
    <author>Susanna Röblitz</author>
    <author>Rainald Ehrig</author>
    <author>Marlon Schneider</author>
    <author>Lisbet Geris</author>
    <author>Joerg Wichard</author>
    <author>Frank Buttgereit</author>
    <collection role="institutes" number="num">Numerical Mathematics</collection>
    <collection role="institutes" number="compsys">Computational Systems Biology</collection>
    <collection role="persons" number="ehrig">Ehrig, Rainald</collection>
    <collection role="persons" number="susanna.roeblitz">Röblitz, Susanna</collection>
    <collection role="projects" number="BMBF-3DInJoMo">BMBF-3DInJoMo</collection>
  </doc>
</export-example>
