Workflow



Open3DQSAR's workflow always begins importing a set of molecular structures with the respective biological activities through the import type=SDF and import type=DEPENDENT keywords. Afterwards, one may calculate MIFs through the calc_field keyword, import them from different sources through the import keyword, or a combination of the two.
Once all MIFs have been gathered, Open3DQSAR allows to include all of them or just a selection, in order to evaluate their impact on the model. A choice of the objects to be included in the model can also be made, especially regarding the opportunity to include them in the training set or in an external test set. The latter option will make possible to accomplish external predictions once a model has been obtained.
Open3DQSAR can perform a variety of chemometric analyses on imported MIFs, ranging from standard variable pretreatment to more advanced variable selection procedures.
Available pretreatment operations include:One should be aware that pretreatment operations will impact on all objects included in the training set; so, if a "true" test set which has never seen the model is desired, objects should be moved to the training set at an early stage, i.e. before any pretreatment operation is carried out apart from zeroing or setting a cut-off value.

Subsequently, once an initial PLS model has been obtained, one can challenge its predictive performance against an external test set or by internal cross-validation, using the leave-one-out, leave-two-out and leave-many-out paradigms. Furthermore, the robustness of the model can be ascertained through the progressive scrambling procedure previously described by Clark and Fox [2].

The predictive power of a model can usually be improved by applying appropriate variable selection procedures. A number of them have been implemented in Open3DQSAR, namely:
Apart from potentially improving the predictivity of a model, a very important consequence of variable selection is a diminished complexity of the resulting PLS pseudo-coefficient contour maps which significantly aids visual interpretation of 3D-QSAR models.


REFERENCES

  1. Kastenholz, M. A.;  Pastor, M.; Cruciani, G.; Haaksma, E. E. J.; Fox, T. J. Med. Chem. 2000, 43, 3033-3044.   DOI
  2. Clark, R. D.; Fox, P. C. J. Comput.-Aided Mol. Des. 2004, 18, 563-576.   DOI
  3. De Aguiar, P. F.; Bourguignon, B.; Khots, M. S.; Massart, D. L.; Phan-Than-Luu R. Chemometrics Intell. Lab. Syst. 1995, 30, 199-210.   DOI
  4. Baroni, M.; Costantino, G.; Cruciani, G.; Riganelli, D.; Valigi, R.; Clementi, S. Quant. Struct-Act. Relat. 1993, 12, 9-20.   DOI
  5. Pastor, M.; Cruciani, G.; Clementi, S. J. Med. Chem. 1997, 40, 1455-1464.   DOI
  6. Baroni, M.; Clementi, S.; Cruciani, G.; Costantino, G.; Riganelli, D. J. Chemometr. 1992, 6, 347-356.   DOI
  7. Centner, V.; Massart, D. L.; de Noord, O. E.; de Jong, S.; Vandeginste, B. M.; Sterna, C. Anal. Chem. 1996, 68, 3851-3858.   DOI
  8. Gieleciak, R.; Polanski, J. J. Chem. Inf. Model. 2007, 47, 547-556.   DOI
  9. Grohmann, R.; Schindler, T. J. Comput. Chem. 2008, 29, 847-860.   DOI

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May 31. 2015 20:39:42

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