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

Library Design

I Historical collections and assay data provide information onhow a set of compounds has faired

I Use (dis)similarity and machine learning to construct newcollections that show similar behavior

I Plus various constraints

I Libraries can be designed for certain target families or specificscreening paradigms

If sufficiently annotated, compound behaviormay be correlated to assay and biology char-acteristics

Library Design

I Historical collections and assay data provide information onhow a set of compounds has faired

I Use (dis)similarity and machine learning to construct newcollections that show similar behavior

I Plus various constraints

I Libraries can be designed for certain target families or specificscreening paradigms

If sufficiently annotated, compound behaviormay be correlated to assay and biology char-acteristics

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 (asimplied by the annotations) will they be active in?

BAO 2.0

Assay Modeling

Prior Work

I BAO annotated datasetsI de Souza et al, 2014; Vempati et al, 2012

I Analyzing HTS datasets using BAOI Zander-Balderud et al, 2015; Schurer et al, 2011

I Semi-automated annotation of assay descriptions using theBAO

I Clark et al, 2014

Workflow

I Extract unique BAO terms and for each term identifyannotated assays

I Extract active compounds from this set of assays

I Compute fingerprint bit distribution

I Use these conditional bit distributions to identify the BAOterms that describe the assay that they are likely to be activein

Dataset Overview

I Extracted 4010 Pubchem AIDs from BARD

I Primary, confirmation, counterscreening assays

I 154M outcomes

I 740K compounds

I Pubchem 881-bit keys using CDK and NCGC implementations

I 192 unique BAO terms

Dataset Overview

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Active Inactive Inconclusive Probe Unspecified

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2 3 4 5 6 7 8 9 10 11 12 13Term Depth

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1 2 3 4 5 6 7 8 9 13Number of BAO Terms

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ssay

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Class Imbalances

Imbalanced classes are problematic, and some ofthe terms with near-balanced classes are not veryspecific (e.g., imaging method)

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VICTOR X2 Multilabel Plate Readerimaging method

radiometry method

SpectraMax 190 Microplate Reader

reporter protein

thermal shift

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9

BAO ID

Cla

ss R

atio

Problem formulationFor a given library of compounds X , we would like to calculate aranked list relevant T of BAO terms that are most likely associatedwith X . Let x ∈ X and t is a BAO term. The list T is an orderedlist based on the following:

argmaxi

∑j

p(ti |xj)

, (1)

where p(ti |xj) is the probability that BAO term ti is associatedwith 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 insteadhave

p(ti |xj) ∝ p(ti )∑k

p(xj |ak)p(ak |ti ), (2)

where ak is a BAO annotated assay.

A Bayesian Approach for Ranking

Note that p(xj |ak) is the sampling function specified over onlyactive compounds in assay ak . In our model, xj is defined asindependent Bernoulli distribution with parameter θ, i.e.,

p(xj |ak) =∏i

θxjii (1− θi )1−xji ,

where xji ∈ {0, 1} is the i-th bit of the PubChem substructuralfingerprint.

Learning BAO terms for a library of compounds amounts toestimating θ, p(ti ), and p(ak |ti ).

Per-Term Activity Classifier

I For a given ontology term Ti , predict whether a compoundwill be active or not

I Model this using Naıve Bayes, where we extract set of activesand inactives from assays annotated with Ti

I Results in a set of models {M1,M2, · · · ,MN},

I For a new compound in library, obtain probability of beingactive for term Ti for all i and take top k terms

I Aggregate top k terms from all compounds in library

I Represents the set of ontology terms defining an assayin which these compounds would likely be active

Test Libraries

I Considered several libraries to test out the approach

I MIPE (1912 compounds) - Approved, investigational drugs,constructed for functional diversity

I LOPAC (1280 compounds) - Diverse library, designed forenrichment of bioactivity

I Natural Products (5000 compounds)

I 1000 member subset of ChEMBL GPCR collection

I 1000 member subset of ChEMBL Kinase collection

Test Libraries

In the Pubchem fingerprint space, the libraries are not very different

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gpcr1gpcr2

kinase1kinase2

lopacm

ipenp

Bit Position

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ized

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quen

cy

Test Libraries - Distance Matrix

lopac

mipe

np

kinase1

kinase2

gpcr1

gpcr2

lopac

mipe np

kinas

e1

kinas

e2gp

cr1gp

cr2

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Euc. Dist.

Prediction Workflow

Bayesian Ranking

I Compute liklihood of allterms for each compound

I Aggregate across library(mean liklihood) and taketop k

Activity Models

I For molecules predictedactive, collect correspondingterms

I Retain the top k mostfrequent terms across thelibrary

We take the top k terms as the set of anno-tations describing an assay in which the librarywill show activity in

Result - Bayesian Ranking

BAO_0000001BAO_0000002BAO_0000003BAO_0000004BAO_0000005BAO_0000010BAO_0000015BAO_0000035BAO_0000045BAO_0000046BAO_0000049BAO_0000050BAO_0000051BAO_0000055BAO_0000057BAO_0000062BAO_0000063BAO_0000070BAO_0000079BAO_0000080BAO_0000100BAO_0000123BAO_0000129BAO_0000130BAO_0000131BAO_0000139BAO_0000142BAO_0000152BAO_0000160BAO_0000164BAO_0000166BAO_0000217BAO_0000218BAO_0000219BAO_0000220BAO_0000221BAO_0000223BAO_0000224BAO_0000225BAO_0000249BAO_0000250BAO_0000251BAO_0000254BAO_0000357BAO_0000363BAO_0000366BAO_0000394BAO_0000405BAO_0000450BAO_0000452BAO_0000453BAO_0000508BAO_0000512BAO_0000513BAO_0000515BAO_0000516BAO_0000572BAO_0000577BAO_0000578BAO_0000591BAO_0000593BAO_0000657BAO_0000682BAO_0000691BAO_0000697BAO_0000698BAO_0000699BAO_0000701BAO_0000705BAO_0000706BAO_0000722BAO_0000850BAO_0000884BAO_0000902BAO_0000903BAO_0000904BAO_0000905BAO_0000906BAO_0000913BAO_0000943BAO_0000982BAO_0001019BAO_0001036BAO_0001046BAO_0001047BAO_0001104BAO_0002001BAO_0002041BAO_0002043BAO_0002090BAO_0002100BAO_0002168BAO_0002176BAO_0002182BAO_0002188BAO_0002196BAO_0002424BAO_0002527BAO_0002528BAO_0002530BAO_0002534BAO_0002656BAO_0002989BAO_0002990BAO_0002991BAO_0002993BAO_0002994BAO_0002995BAO_0002996BAO_0002997BAO_0002998BAO_0003000BAO_0003002BAO_0003003BAO_0003004BAO_0003005BAO_0003006BAO_0003007BAO_0003008BAO_0003009BAO_0003010BAO_0003063BAO_0003064BAO_0003069

lopac

mipe NP

−5e+09

−4e+09

−3e+09

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Avg Prob

Result - Per Term Activity Classifier

bioluminescence

molecular redistribution determination method

direct enzyme activity measurement method

whole cell lysate format

BAO_0000722

lopac

mipe np

kinas

e1

kinas

e2gp

cr1gp

cr2

Rank

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What’s Different Between Libraries?

What’s Different Between Libraries?

Term Depth for the ’Differential’ Terms

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Pitfalls

If sufficiently annotated, compound behavior maybe correlated to assay and biology characteristics

I A very abstract, possibly lossy, view of the effect ofcompounds on biology

I Depends on correct and meaningful annotations

I Annotations terms are context dependent, but this may notbe considered when annotating a dataset

I BAO terms exhibit hierarchical relationships and ignoringthem is simplistic

Acknowledgements

I Qiong Cheng (U. Miami)

I Stephan Schurer (U. Miami)

Source code and slides