Chemoinformatics tools for lead discovery

1. Chemoinformatics tools for lead discovery

2. Virtual screening • The huge numbers of molecules available in public and in-house databases means that there is a requirement for tools to rank compounds in order of decreasing probability of activity • Range of methods available, varying in the sophistication and the amount of information that is available • Use of structure-based methods when an X-ray structure for the biological target is available • If this is not the case then must make use of information about (potential) ligands

3. Ligand-Based Methods • Similarity searching • Use when just a single bioactive reference structure is available • 3D pharmacophore searching • Use when it has been possible to carry out a pharmacophore mapping exercise • Machine learning • Use when a fair number of both actives and inactives have been identified

4. Similarity Searching: I • Use of a similarity measure to quantify the resemblance between an active target, or reference, structure and each database structure • The similar property principle means that high-ranked structures are likely to have similar activities to that of the target structure • Similarity searching hence provides an obvious way of following-up on an initial active

5. Similarity searching: II • Many ways in which the similarity between two molecules can be computed • A similarity measure has two components • A structure representation • A similarity coefficient to compare two representations • Most operational systems use similarity measures based on 2D fingerprints and the Tanimoto coefficient

6. Fragment bit-strings (fingerprints) • Originally developed for 2D substructure search • Similarity is based on the fragments common to two molecules • Widely used in both in-house and commercial chemoinformatics systems

7. Similarity coefficients • Tanimoto coefficient for binary bit strings • C bits set in common between Target and Database Structure • T bits set in Target • D bits set in Database structure • Values between zero (no bits in common) and unity (identical fingerprints) • Many other, related similarity coefficients exist: • Tversky, cosine, Euclidean distance …..

8. Combination of search techniques using data fusion: I • Tanimoto/fingerprint measures most common but many other types, e.g., • Computed physicochemical properties • 3D grid describing the molecular electrostatic potential • These reflect different molecular characteristics, so may enhance search performance by using more than one similarity measure • Data fusion or consensus scoring

9. Combination of search techniques using data fusion: II • Combination of different rankings of the same sets of molecules • Two basic approaches • Generate rankings from the same molecule using different similarity measures (similarity fusion) • Generate rankings from different molecules using the same similarity measure but different molecules (group fusion)

10. Reference 2 Reference 3 Groupfusion Reference 1

11. After truncation to required rank Reference 2 Reference 1 Reference 3

12. Fused Group Fusion Final truncated r = 1000 r = 2000 New Active Active found in earlier list

13. Group fusion rules • Useful performance increases, even with just 10 actives, as better coverage of structural space with multiple starting points • Improvement most obvious when searching for heterogeneous sets of active molecules • Best results obtained by • Fusing similarity coefficient values, rather than ranks • Re-ranking using the maximum of the similarity values associated with each molecule • Using the Tanimoto coefficient

14. Turbo similarity searching: I • Similar property principle: nearest neighbours are likely to exhibit the same activity as the reference structure • Group fusion improves the identification of active compounds • Potential for further enhancements by group fusion of rankings from the reference structure and from its assumed active nearest neighbours

15. Turbo similarity searching: II REFERENCE STRUCTURE RANKED LIST NEAREST NEIGHBOURS

16. Experimental details • MDL Drug Data report (MDDR) dataset of 11 activity classes and 102K structures • In all, 8294 actives in the 11 classes, with (turbo) similarity searches being carried out using each of these as the reference structure • ECFP_4 fingerprints/Tanimoto coefficient • MAX group fusion on similarity scores • Increasing numbers of nearest neighbours

17. Numbers of nearest neighbours

18. Upper and lower bound experiments

19. Rationale for upper bound results • The true actives in the set of assumed actives yield significant enhancements in performance • The true inactives in the set of assumed actives have little effect on performance • Taken together, the two groups of compounds yield the observed net enhancement

20. Use of machine-learning methods for similarity searching: I • Turbo similarity searching uses group fusion to enhance conventional similarity searching • Machine learning is a more powerful virtual screening tool than similarity searching • But requires a training-set containing known actives and inactives • Given an active reference structure, a training-set can be generated from • Using the k nearest neighbours of the reference structure as the actives • Using k randomly chosen, low-similarity compounds as the inactives

21. Use of machine-learning methods for similarity searching: II

22. Results: I • Experiments with the MDDR dataset show that group fusion better than machine-learning methods when averaged over all of the classes • However, group fusion inferior for the most diverse datasets (as measured by the mean pair-wise similarities) • Additional searches using 10 MDDR activity classes that are as structurally diverse as possible

23. Results: II

24. Conclusions: I • Fingerprint-based similarity searching using a known reference structure is long-established in chemoinformatics • When small numbers of actives are available, group fusion will enhance performance when the sought actives are structurally heterogeneous

25. Conclusions: II • Can also enhance conventional similarity search, even if there is just a single active, by assuming that the nearest neighbours are also active • Can be effected in two ways • Use of group fusion to combine similarity rankings (overall best approach) • Use of substructural analysis to compute fragment weights (best with highly heterogeneous sets of actives)

26. Soaluntukdipelajari • Tunjukkanperankhemoinformatikdalam QSAR • Data dananalisisdarikhemoinformatik yang banyakdigunakandalam docking molekul • Indekskemiripan (similarity index) banyakdigunakanuntukmendapatkaninformasitentangsenyawabaru yang memilikiaktivitasbiologistinggi. Jelaskansecarasingkatsistemkerjanya • Dalampenemuanobatbaru yang lebihpotensialdari yang sudahdikenal, banyakmemanfaatkankhemoinformatiks. Jelaskandenganbeberapacontoh. • ApaperbedaanpenggunaanKhemoinformatiksdalam QSAR, molecular docking dan similarity searching?