SAR vs QSAR or “is QSAR different from SAR”

1. SAR vs QSARor “is QSAR different from SAR” Joanna Jaworska Procer & Gamble, Brussels, Belgium and Nina Jeliazkova IPP, Bulgarian Academy of Sciences, Sofia, Bulgaria

2. SAR is supposed to be not quantitative concept SAR is based on the notion of “similarity” : “Similar compounds have similar activity” “Dissimilar compounds have dissimilar activity” QSAR aims to derive a quantitative model of the activity SAR vs. QSARhow could we say there is no difference ?

3. What “similarity” means? A philosophers’ view and implications to the toxicology; Are the basic tenets of SAR true ? What do similarity measures measure ? How does the similarity measure relate to QSAR modeling ? SAR vs. QSARRoadmap

4. exploiting the similarity concept is a sign of immature science (Quine) “it is ill defined to say “A is similar to B” and it is only meaningful to say “A is similar to B with respect to C” Similarity : philosophers’ view implications for toxicology : A chemical “A” cannot be similar to a chemical “B” in absolute terms but only with respect to some measurable key feature

5. Numerical Values Chemical Grouping by Similarity Similarity between structures Selected similar compounds Similarity between points ?

6. Structural similarity • Does not imply always similarity in activity • Martin et al. 2002 J.Med.Chem 45,4350-58 • Does not always imply similarity in descriptors • Kubinyi, H., Chemical Similarity and Biological activity (with permission of the author)

7. Structurally similar compounds can have very different properties

8. Example: Y.Martin et al ( 2002) Do structurally similar molecules have similar biological activity ? • Set of 1645 chemicals with IC50s for monoamine oxidase inhibition • Daylight fingertips 1024 bits long ( 0-7 bonds) • Using Tanimoto coeff with a cut off value 0f 0.85 only 30 % of actives were detected Cutoff values % of actives detected % False positives J. Med. Chem. 2002,45,4350-4358

9. How else to measure chemical similarity ? • Describe chemical compounds with a set of numerical values ( fingerprints, diverse descriptors, field values, etc.) • Set up some measure between values (Euclidean distance, Tanimoto distance, Carbo similarity index, etc.) What do we actually measure ? And how it is related to the activity ?

10. What do we measure ? The distance between numerical representations of chemical compounds A few warnings: • The numerical representation is not unique • The numerical representation includes only part of all the information about the compound • A distance measure reflects “closeness” only if the data holds specific assumptions (next slide - example)

11. Data set 2 Data set 1 Distances - example by Euclidean distance we will decide that the red point is closer to the data set 2, while a human will note that it belongs to the data set 1. • Distances give results which are not always expected intuitively • Be aware of the assumptions behind distances (e.g. Euclidean distance gives good results with normally distributed data in orthogonal space)

12. How do we represent a chemical compound ? Fingerprints, Descriptors (more than 3000 available), electron density, various fields, etc. All representations lose information. We should ensure this information is not important. How?

13. Finding important information • A problem not unique to (Q)SAR • Lot of methods available • Most popular (e.g. PCA ) not the best Possible solution : look for the most discriminative information (example: descriptors which provide best discrimination between active and inactive compounds)

14. Two common things to this point: Both methods use numerical representation of chemical compounds; Both methods need to decide which representation to use; SAR vs. QSARhow could we say there is no difference ? One more difference : “SAR is a qualitative not a quantitative relationship” Is this true indeed?

15. Similarity and Activity • Proximity with respect to descriptors does not necessary mean proximity with respect to the activity (example) • This is only true if a linear relationship holds between descriptors and activity (examples) • The linear relationship is only a special case, given the complexity of biochemical interactions. Its use should be justified in every specific case • Structural similarity should be used with care (examples)

16. “Neighbourhood principle” • Molecules in the same local region (“neighbourhood”) of a descriptor space tend to have similar values of a desired property • Contradictory evidence exists : both supporting and rejecting

17. Similar activity values Activity Neighbourhood in the descriptor space Descriptor “Neibourhood principle” Analysis Depends on the relationship between the descriptors and activity !!!

18. “Neighbourhood principle” Lessons • In order to apply the “neighbourhood principle” the TYPE of the relationship between descriptor and activity should be known; • The “neighbourhood principle” is genuine only if the relationship is LINEAR; • The linear relationship is only a simple special case, given the complexity of biochemical interactions. Its use SHOULD BE JUSTIFIED in every specific case.

19. SAR vs QSAR • SAR is based on the “similarity” principle; • The principle is assumed, but in the reality it is not always true; • Similarity of structures • Similarity of descriptors • The authenticity depends on the type of the relationship between descriptors (numerical representation of chemicals) and activity; • The type of the relationship should be known (or derived)

20. Three common things to this point: Both methods use numerical representation of chemical compounds; Both methods need to decide which representation to use; Both methods need to derive the relationship between numerical representation (descriptors, etc.) and activity. SAR vs. QSARhow could we say there is a difference ?

21. Thank you! When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind: it may be the beginning of knowledge, but you have scarcely advanced to the stage of science. William Thomson, Lord Kelvin