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diff --git a/public/presentations/in-silico-methods12/in-silico-methods.rst b/public/presentations/in-silico-methods12/in-silico-methods.rst deleted file mode 100644 index 33f5884..0000000 --- a/public/presentations/in-silico-methods12/in-silico-methods.rst +++ /dev/null @@ -1,221 +0,0 @@ -========================================= -In silico methods for toxicity prediction -========================================= - -:Author: Christoph Helma -:Affiliation: in silico toxicology gmbh -:Date: 2012-10-04 - -.. footer:: 2012-10-04 *in silico* toxicology gmbh - -Outline -======= - -.. class:: incremental - -- In silico toxicology methods -- Lazar framework -- Products and services - -In silico methods -================= -.. class:: incremental - -- Systems biology/molecular modeling -- Expert systems -- Data driven techniques - -Systems biology/molecular modeling -==================================== - -Model individual events (e.g. receptor interactions, (de)toxification) of the *adverse outcome pathway* - -.. class:: incremental small green - -- Mechanistic interpretation - -.. class:: incremental small red - -- Calculations/simulations may be very time consuming -- May require a lot of experimental data for parameterization -- Impossible to model mechanisms of complex toxicological endpoints - -Examples: VirtualToxLab/Biograf - -Expert systems -============== - -Formalize expert knowledge about chemicals and toxicity mechanisms and create a software program - -.. class:: incremental small green - -- Mechanistic interpretation - -.. class:: incremental small red - -- Model creation very time consuming -- Many toxicity mechanisms are poorly understood or even unknown -- Error prone and hard to validate (strong tendency towards overfitting) - -Examples: Derek/Lhasa - -Data driven -=========== - -Use all existing data for a particular endpoint and apply machine learning/QSAR algorithms in order to create a prediction model - -.. class:: incremental small green - -- Comparably fast -- Applicable for every endpoint with sufficient experimental data -- Sound validation possible - -.. class:: incremental small red - -- Applicability domain/model quality depends on experimental data -- Mechanistic relevance has to be extracted from models/descriptors/predictions - -Examples: Classical QSARs, Topkat, Multicase, lazar - -Lazy-Structure-Activity Relationships (lazar) -============================================= - -Automated read across predictions - -.. class:: incremental small - -- Find *similar* compounds (=neighbors) with measured activities -- Create a local (Q)SAR model with neighbors as training compounds -- Make a prediction with this model - -.. class:: incremental - -Lazar estimates the confidence (*applicability domain*) for each prediction - -Chemical Similarity -=================== - -Can be based on - -.. class:: incremental - -- Chemical structures -- Chemical properties -- Biological properties -- ... - -.. class:: incremental - -Lazar uses *activity specific similarities* - -Activity specific similarities -============================== - -Consider only *relevant* (i.e. statistically significant) substructures, properties, ... for similarity calculations - -Algorithms for finding relevant substructures (by A. Maunz): - -- Backbone refinement classes (BBRC) -- Latent structure mining (LAST) - -http://lazar.in-silico.ch -========================= - -.. image:: lazar-input.png - :height: 13em - :align: center - -http://lazar.in-silico.ch -========================= - -.. image:: lazar-output1.png - :height: 13em - :align: center - -http://lazar.in-silico.ch -========================= - -.. image:: lazar-output2.png - :height: 13em - :align: center - -http://lazar.in-silico.ch -========================= - -.. image:: lazar-output3.png - :height: 13em - :align: center - -Lazar limitations -================= - -.. class:: incremental - -- Model quality depends on data quality -- Applicability domain depends on learning instances - -in silico toxicology gmbh -========================= - -Open source software and algorithm development - -.. class:: incremental - -- Predictive toxicology and QSAR models -- Toxicological data mining -- Life science webservices and data warehouses - -Why open source? -================ - -.. class:: incremental - -- Clear and unambiguous documentation of implemented algorithms essential for scientific software (also required by many regulatory guidelines) -- Collaboration with partners, projects and external contributors -- Establishment of international standards -- Security of investment - -EU Research projects (FP6/7) -============================ - -.. class:: small - -:Sens-it-iv: Novel testing strategies for in vitro assessment of allergens -:Scarlet: Network on in silico methods for carcinogenicity and mutagenicity -:OpenTox: Open source framework for predictive toxicology -:ToxBank: Integrated data analysis and servicing of alternative testing methods in toxicology -:ModNanoTox: Modelling toxicity behaviour of engineered nanoparticles - -Free products and services -========================== - -:Lazar application: http://lazar.in-silico.ch -:OpenTox Webservices: http://webservices.in-silico.ch -:Source code: https://github.com/opentox - -Issue tracker, documentation, ... - -Commercial products and services -================================ - -.. class:: incremental - -- Lazar "software as a service" (SaaS): secure access for confidential predictions, batch predictions, ... -- Virtual appliances with lazar software for in-house/desktop installation -- Installation services -- Phone and email support - -Commercial products and services -================================ - -.. class:: incremental - -- Virtual toxicity screening of compounds and libraries -- Development of prediction models for new endpoints -- Scientific programming, contract research and consulting - -Contact -======= - -:Web: http://www.in-silico.ch -:Email: helma@in-silico.ch |