Fingerprint-Based Virtual Screening Using Multiple Bioactive Reference Structures
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1 Fingerprint-Based Virtual Screening Using Multiple Bioactive Reference Structures Jérôme Hert, Peter Willett and David J. Wilton (University of Sheffield, Sheffield, UK) Pierre Acklin, Kamal Azzaoui, Edgar Jacoby and Ansgar Schuffenhauer (Novartis Institutes for BioMedical Research, Basel, Switzerland)
2 Outlines Introduction to similarity-based virtual screening Extension to multiple reference structures Comparison of ranking methods Comparison of descriptors Turbo similarity searching Conclusions
3 Virtual screening Virtual screening involves scanning databases of compounds to find molecules that may exhibit some bioactivity of interest, so as to prioritise a screening programme
4 Similarity searching (1) Use of similarity measure to determine the degree of similarity between an active reference structure and each structure in the database Similarity property principle means that high-ranked structures are likely to have a similar activity to that of the reference structure
5 Similarity searching (2) How can existing methods be used when several diverse structures are available?
6 Project overview (1) Given a set of molecules of known activity, how can they be used to rank a database in order of decreasing probability of their exhibiting that activity? The various approaches have been evaluated in simulated virtual screening experiments on the MDDR database (ca. 102K molecules) using a range of different activity classes
7 Project overview (2) Use just 2D fingerprints (of various sorts) Use of the Tanimoto coefficient (will not consider the effect of different similarity coefficients)
8 Comparison of Methods Three distinct approaches have been investigated Single fingerprint methods Data fusion methods Substructural analysis methods
9 Single Fingerprint Methods (1) Two methods derived from the Stigmata approach Modal approach Weighted approach Modal / weighted fingerprint used as a query, with the Tanimoto coefficient being used to score molecules in the database
10 Single Fingerprint Methods (2) Training set of actives: mol 1: mol 2: mol 3: mol 4: mol 5: Modal at 40%: Weighted:
11 Data Fusion Methods (1) Combination of different rankings of the same sets of molecules with the expectation of improving decision This basic idea has been used previously with considerable success (also consensus scoring) by generating different rankings from the same molecule, using different similarity measures
12 Data Fusion Methods (2) Here, the different rankings come from different molecules but use the same, Tanimoto-based similarity measure Fusion of ranks Fusion of scores
13 Data Fusion Methods (3) Two fusion rules investigated SUM : N i = 1 s i MAX : Max ( ) s i
14 Substructural Analysis Methods (1) Classic substructural analysis (SSA): Training-set containing actives and inactives Weights calculated for each bit from the trainingset using a weighting scheme Sum of the weights for each bit present in a compound gives the score
15 Substructural Analysis Methods (2) Approximate substructural analysis without known inactives: Reference structures as training-set actives Approximate the training-set by the entire database Use of an appropriate weighting scheme that does not make explicit use of information about the inactives R1: log A T j j N N A T
16 Substructural Analysis Methods (3) Binary Kernel Discrimination (BKD): ( ) N d i ( ), j d i j i, j λ = 1 λ K, λ λ is a smoothing parameter that is optimised using the training-set active and inactive compounds Compounds are scored by: L A ( j) = i actives K K λ λ i inactives ( i, j) ( i, j)
17 Substructural Analysis Methods (4) Approximate BKD without known inactives Reference structures as training-set actives Set of 100 randomly chosen compounds from the database as training-set inactives (cf SSA approximation)
18 Experimental Details MDL Drug Data Report (MDDR) Database 11 activity classes selected 10 sets of 10 randomly chosen compounds from each activity 3 fingerprints investigated: 988-bit Unity, 1052-bit BCI, 2048-bit Daylight Results are average recalls at 5% over the 10 different trials
19 Results (1) Initial Experiments Best threshold for modal approach is 40% Best combination for data fusion is the combination of scores using the MAX fusion rule The three types of fingerprint give broadly comparable results
20 Results (2) The basic results obtained with the various methods have been compared with the average and the maximum of all individual, single-molecule similarity searches
21 Results (3) 70 Average recall at 5% (%) Modal Weighted SSA Data fusion BKD Single Max Single Mean
22 Comparison Of Descriptors Four different types of 2D descriptor investigated using the two best approaches from the initial part of the study Structural keys 1052-bit BCI fingerprints Hashed fingerprints 988-bit Unity fingerprints 2048-bit Daylight fingerprints 2048-bit Avalon fingerprints (internal Novartis system) Circular substructures ECFP_2, ECFP_4, FCFP_2, FCFP_4 Pharmacophore vectors
23 Pharmacophore Vectors: Similog Similog keys Atom typing scheme based on four properties: hydrogen-bond donor, hydrogen-bond acceptor, bulkiness and electropositivity Atom triplets of strings encoding absence and presence of properties, plus distance encoding form a DABE key Vector contains a count for each of the 8031 possible DABE keys
24 Pharmacophore Vectors: CATS CATS vectors Based on five atom types: hydrogen-bond acceptor, hydrogen-bond donor, positive, negative, lipophilic Correlation vector representation calculated by C V R T d = 1 A A A i= 1 j = 1 δ T ij, d
25 Results 70 BKD Data Fusion Average Recall at 5% (%) BCI Daylight Unity Avalon ECFP_2ECFP_4FCFP_2FCFP_4 Similog CATS
26 Turbo Similarity Searching (1) Similarity property principle: nearest neighbours are likely to exhibit the same activity as the reference structure Data fusion of multiple bioactive compounds is an effective way of improving the identification of active compounds
27 Turbo Similarity Searching (2) Reference structure Ranked list Nearest neighbours
28 Probability of being active vs Rank Probability of being active (%) Rank
29 Results Average recall at 5% (%) SS TSS-5 TSS-10 TSS-20 TSS-50 TSS-100 TSS-200
30 General conclusions This work has demonstrated two effective ways of using multiple active structures in 2D similarity searching (BKD and Data fusion) This work has also demonstrated the general effectiveness of the circular substructure descriptors (ECFP_4 in particular) Turbo similarity searching is a simple way of increasing the cost effectiveness of similarity searching
31 Acknowledgements Novartis Institutes for BioMedical Research for funding CINF Division for funding MDL Information Systems Inc. for the provision of the MDDR database Barnard Chemical Information Ltd., Daylight Chemical Information Systems Inc., the Royal Society, Tripos Inc., Scitegic Inc. and the Wolfson Foundation for software and laboratory support.
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