Mechanistic Interpretation

Many (Q)SAR and data mining techniques can be used to derive a hypothesis about biological mechanisms. However, it is important to remember that most of these techniques have no knowledge about chemical and biological pro-cesses. Thus, they cannot reason about mechanisms, but they can provide information that is relevant for a mechanistic assessment (e.g., structural alerts, compounds with similar modes of action). This means that a toxicologi-cal researcher has to evaluate only a limited number of possible hypotheses, but expert knowledge is still needed for the identifi cation of mechanisms.

The interpretability of models and individual predictions may depend on several factors:

• Model complexity. Interpretability decreases with model complexity and abstraction level, but complex models are frequently needed to accom-modate for real world situations. It is, however, not always necessary to interpret complete models. The extraction of specifi c information (e.g., relevant substructures/properties) and the inspection of rationales for individual predictions may provide more information for toxicologists that complete models.

• Biological rationale for the algorithm. Most scientists fi nd it easier to interpret models that have a biological rationale and/or resemble their way of thinking about toxicological phenomena. Techniques based on chemical similarities are very useful in this respect because they support the search for analogs and chemical classes. A mechanistic hypothesis (and a critical evaluation of individual predictions) can be obtained from the inspection of relevant features and from the mechanisms of structur-ally similar compounds.

• Visual presentation of the results. Most data mining programs are hard to use for nondata mining experts and have great shortcomings in the visual presentation of their results. End users with a toxicological back-ground should not be confused with data mining/(Q)SAR terminology and with detailed options for algorithms and parameter settings. The

interface should provide instead an intuitive and traceable presentation of the rationales for a prediction together with links for the access of supporting information (e.g., original data, results in other assays, literature).

5.8 CONCLUSION

The most frequent application of data mining in predictive toxicology is the development of (Q)SAR models. The development of (Q)SAR models requires (1) the generation of features that represent chemical structures, (2) the selection of features for a particular end point, (3) the development of a (Q)SAR model, (4) the validation of the model, and (5) the interpretation of the model and of individual predictions.

For each of these tasks, a large number of data mining techniques are available. Selecting and combining suitable algorithms for the individual steps allows us to develop problem - specifi c solutions with capabilities that go beyond standardized solutions. However, it is important to understand the properties and limitations of the applied techniques and to communicate them clearly to the model users.

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CHEMOGENOMICS - BASED DESIGN