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Integrative Modeling Strategies for Predicting Drug Toxicities at the eTOX Project
Author(s) -
Sanz Ferran,
Carrió Pau,
López Oriol,
Capoferri Luigi,
Kooi Derk P.,
Vermeulen Nico P. E.,
Geerke Daan P.,
Montanari Floriane,
Ecker Gerhard F.,
Schwab Christof H.,
Kleinöder Thomas,
Magdziarz Tomasz,
Pastor Manuel
Publication year - 2015
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201400193
Subject(s) - computer science , documentation , cheminformatics , quantitative structure–activity relationship , upgrade , fingerprint (computing) , interface (matter) , predictive modelling , machine learning , data science , risk analysis (engineering) , systems engineering , artificial intelligence , data mining , bioinformatics , engineering , medicine , bubble , maximum bubble pressure method , parallel computing , biology , programming language , operating system
Early prediction of safety issues in drug development is at the same time highly desirable and highly challenging. Recent advances emphasize the importance of understanding the whole chain of causal events leading to observable toxic outcomes. Here we describe an integrative modeling strategy based on these ideas that guided the design of eTOXsys, the prediction system used by the eTOX project. Essentially, eTOXsys consists of a central server that marshals requests to a collection of independent prediction models and offers a single user interface to the whole system. Every of such model lives in a self‐contained virtual machine easy to maintain and install. All models produce toxicity‐relevant predictions on their own but the results of some can be further integrated and upgrade its scale, yielding in vivo toxicity predictions. Technical aspects related with model implementation, maintenance and documentation are also discussed here. Finally, the kind of models currently implemented in eTOXsys is illustrated presenting three example models making use of diverse methodology (3D‐QSAR and decision trees, Molecular Dynamics simulations and Linear Interaction Energy theory, and fingerprint‐based QSAR).