What can your library do for you?
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The design of chemical libraries is usually informed by pre-existing characteristics and desired features. On the other hand, assesing the prospective performance of a new library is more difficult. Importantly, a given screening library is often screened in a variety of systems which can differ in cell lines, readouts, formats and so on. In this study we explore to what extent pre-existing libraries can shed light on the relation between library activity and assay features. Using an ontology such as the BAO, it is possible to construct a hierarchy of annotations associated with an assay. Based on this annotation hierarchy we can then ask how likely are molecules associated with a specific annotation, to be identified as active. To allow generalization we consider substrucural features, as represented by a structural key fingerprint, rather than whole molecules. We employ a Bayesian framework to quantify the the association between a substructural feature and a given assay annotation, using a set of NCGC assays that have been annotated with BAO terms. We discuss our approach to training the Bayesian model and describe benchmarks that characterize model performance relative to the position of the annotation in the BAO hierarchy. Finally we discuss the role of this approach in a library design workflow that includes traditional design features such as chemical space coverage and physicochemical properties but also takes in to account screening platform features.
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What can your library do for you?
- 1. What can your library do for you? Rajarshi Guha, Dac-Trung Nguyen, Alexey Zhakarov, Ajit Jadhav NIH NCATS ACS Fall Meeting 2016, Philadelphia August 21, 2016
- 2. Library Design Historical collections and assay data provide information on how a set of compounds has faired Use (dis)similarity and machine learning to construct new collections that show similar behavior Plus various constraints Libraries can be designed for certain target families or specific screening paradigms If sufficiently annotated, compound behavior may be correlated to assay and biology char- acteristics
- 3. Library Design Historical collections and assay data provide information on how a set of compounds has faired Use (dis)similarity and machine learning to construct new collections that show similar behavior Plus various constraints Libraries can be designed for certain target families or specific screening paradigms If sufficiently annotated, compound behavior may be correlated to assay and biology char- acteristics
- 4. Two Questions How likely are compounds, associated with a given annotation, identified as active? Given a new set of compounds, what sets of assay conditions (as implied by the annotations) will they be active in?
- 5. BAO 2.0
- 7. Prior Work BAO annotated datasets de Souza et al, 2014; Vempati et al, 2012 Analyzing HTS datasets using BAO Zander-Balderud et al, 2015; Sch¨urer et al, 2011 Semi-automated annotation of assay descriptions using the BAO Clark et al, 2014
- 8. Workflow Extract unique BAO terms and for each term identify annotated assays Extract active compounds from this set of assays Compute fingerprint bit distribution Use these conditional bit distributions to identify the BAO terms that describe the assay that they are likely to be active in
- 9. Dataset Overview Extracted 4010 Pubchem AIDs from BARD Primary, confirmation, counterscreening assays 154M outcomes 740K compounds Pubchem 881-bit keys using CDK and NCGC implementations 192 unique BAO terms
- 10. Dataset Overview 1e+02 1e+05 1e+08 Active Inactive Inconclusive Probe Unspecified Numberofoutcomes 0 200 400 0 2 4 6 log Num. Compound Density(assays) Outcome Active Inactive 0 300 600 900 2 3 4 5 6 7 8 9 10 11 12 13 Term Depth NumberofTerms 0 1000 2000 1 2 3 4 5 6 7 8 9 13 Number of BAO Terms NumberofAssays
- 11. Class Imbalances Imbalanced classes are problematic, and some of the terms with near-balanced classes are not very specific (e.g., imaging method) qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqq qqq qqqqqq q q qqq q q q qq q q qq q q q q VICTOR X2 Multilabel Plate Reader imaging method radiometry method SpectraMax 190 Microplate Reader reporter protein thermal shift 0 3 6 9 BAO ID ClassRatio
- 12. Problem formulation For a given library of compounds X, we would like to calculate a ranked list relevant T of BAO terms that are most likely associated with X. Let x ∈ X and t is a BAO term. The list T is an ordered list based on the following: argmax i j p(ti |xj ) , (1) where p(ti |xj ) is the probability that BAO term ti is associated with compound xj . From Bayes’ rule, we have p(ti |xj ) = p(xj |ti )p(ti ) p(xj ) or p(ti |xj ) ∝ p(xj |ti )p(ti ). Given that BAO terms are annotated at the assay level, we instead have p(ti |xj ) ∝ p(ti ) k p(xj |ak)p(ak|ti ), (2) where ak is a BAO annotated assay.
- 13. A Bayesian Approach for Ranking Note that p(xj |ak) is the sampling function specified over only active compounds in assay ak. In our model, xj is defined as independent Bernoulli distribution with parameter θ, i.e., p(xj |ak) = i θ xji i (1 − θi )1−xji , where xji ∈ {0, 1} is the i-th bit of the PubChem substructural fingerprint. Learning BAO terms for a library of compounds amounts to estimating θ, p(ti ), and p(ak|ti ).
- 14. Per-Term Activity Classifier For a given ontology term Ti , predict whether a compound will be active or not Model this using Na¨ıve Bayes, where we extract set of actives and inactives from assays annotated with Ti Results in a set of models {M1, M2, · · · , MN}, For a new compound in library, obtain probability of being active for term Ti for all i and take top k terms Aggregate top k terms from all compounds in library Represents the set of ontology terms defining an assay in which these compounds would likely be active
- 15. Test Libraries Considered several libraries to test out the approach MIPE (1912 compounds) - Approved, investigational drugs, constructed for functional diversity LOPAC (1280 compounds) - Diverse library, designed for enrichment of bioactivity Natural Products (5000 compounds) 1000 member subset of ChEMBL GPCR collection 1000 member subset of ChEMBL Kinase collection
- 16. Test Libraries In the Pubchem fingerprint space, the libraries are not very different 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00 gpcr1gpcr2kinase1kinase2lopacmipenp Bit Position NormalizedFrequency
- 17. Test Libraries - Distance Matrix lopac mipe np kinase1 kinase2 gpcr1 gpcr2 lopac m ipe np kinase1 kinase2 gpcr1 gpcr2 0 1 2 3 4 Euc. Dist.
- 18. Prediction Workflow Bayesian Ranking Compute liklihood of all terms for each compound Aggregate across library (mean liklihood) and take top k Activity Models For molecules predicted active, collect corresponding terms Retain the top k most frequent terms across the library We take the top k terms as the set of anno- tations describing an assay in which the library will show activity in
- 19. Result - Bayesian Ranking BAO_0000001 BAO_0000002 BAO_0000003 BAO_0000004 BAO_0000005 BAO_0000010 BAO_0000015 BAO_0000035 BAO_0000045 BAO_0000046 BAO_0000049 BAO_0000050 BAO_0000051 BAO_0000055 BAO_0000057 BAO_0000062 BAO_0000063 BAO_0000070 BAO_0000079 BAO_0000080 BAO_0000100 BAO_0000123 BAO_0000129 BAO_0000130 BAO_0000131 BAO_0000139 BAO_0000142 BAO_0000152 BAO_0000160 BAO_0000164 BAO_0000166 BAO_0000217 BAO_0000218 BAO_0000219 BAO_0000220 BAO_0000221 BAO_0000223 BAO_0000224 BAO_0000225 BAO_0000249 BAO_0000250 BAO_0000251 BAO_0000254 BAO_0000357 BAO_0000363 BAO_0000366 BAO_0000394 BAO_0000405 BAO_0000450 BAO_0000452 BAO_0000453 BAO_0000508 BAO_0000512 BAO_0000513 BAO_0000515 BAO_0000516 BAO_0000572 BAO_0000577 BAO_0000578 BAO_0000591 BAO_0000593 BAO_0000657 BAO_0000682 BAO_0000691 BAO_0000697 BAO_0000698 BAO_0000699 BAO_0000701 BAO_0000705 BAO_0000706 BAO_0000722 BAO_0000850 BAO_0000884 BAO_0000902 BAO_0000903 BAO_0000904 BAO_0000905 BAO_0000906 BAO_0000913 BAO_0000943 BAO_0000982 BAO_0001019 BAO_0001036 BAO_0001046 BAO_0001047 BAO_0001104 BAO_0002001 BAO_0002041 BAO_0002043 BAO_0002090 BAO_0002100 BAO_0002168 BAO_0002176 BAO_0002182 BAO_0002188 BAO_0002196 BAO_0002424 BAO_0002527 BAO_0002528 BAO_0002530 BAO_0002534 BAO_0002656 BAO_0002989 BAO_0002990 BAO_0002991 BAO_0002993 BAO_0002994 BAO_0002995 BAO_0002996 BAO_0002997 BAO_0002998 BAO_0003000 BAO_0003002 BAO_0003003 BAO_0003004 BAO_0003005 BAO_0003006 BAO_0003007 BAO_0003008 BAO_0003009 BAO_0003010 BAO_0003063 BAO_0003064 BAO_0003069 lopac m ipe N P −5e+09 −4e+09 −3e+09 −2e+09 −1e+09 Avg Prob
- 20. Result - Per Term Activity Classifier bioluminescence molecular redistribution determination method direct enzyme activity measurement method whole cell lysate format BAO_0000722 lopac m ipe npkinase1kinase2 gpcr1 gpcr2 Rank 1 2 3 4 5
- 21. What’s Different Between Libraries?
- 22. What’s Different Between Libraries?
- 23. Term Depth for the ’Differential’ Terms q q q q q q q q qq q q q q q q q q q q q q q q q q q q qqq qqq qq qq q q qqqq q q 3 4 5 6 7 8 3 4 5 6 7 8 Term Depth (LOPAC) TermDepth(MIPE) q q q q q q q q q q q q q q q q q q q q q q q q q q q q qqq qqq qq qq q q q qqq q q 3 4 5 6 7 8 3 4 5 6 7 8 Term Depth (NP) TermDepth(MIPE)
- 24. Pitfalls If sufficiently annotated, compound behavior may be correlated to assay and biology characteristics A very abstract, possibly lossy, view of the effect of compounds on biology Depends on correct and meaningful annotations Annotations terms are context dependent, but this may not be considered when annotating a dataset BAO terms exhibit hierarchical relationships and ignoring them is simplistic
- 25. Acknowledgements Qiong Cheng (U. Miami) Stephan Sch¨urer (U. Miami) Source code and slides