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+=========================================
+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