Prediction of off-target drug effects through data fusion

Abstract

We present a probabilistic data fusion framework that combines multiple computational approaches for drawing relationships between drugs and targets. The approach has special relevance to identifying surprising unintended biological targets of drugs. Comparisons between molecules are made based on 2D topological structural considerations, based on 3D surface characteristics, and based on English descriptions of clinical effects. Similarity computations within each modality were transformed into probability scores. Given a new molecule along with a set of molecules sharing some biological effect, a single score based on comparison to the known set is produced, reflecting either 2D similarity, 3D similarity, clinical effects similarity or their combination. The methods were validated within acurated structural pharmacology database (SPDB) and further tested by blind application to data derived from the ChEMBL database. For prediction of off-target effects, 3D-similarity performed best as a single modality, but combining all methods produced performance gains. Striking examples of structurally surprising off-target predictions are presented.

Full text

PREDICTION OF OFF-TARGET DRUG EFFECTS
THROUGH DATA FUSION
EMMANUEL R. YERA, ANN E. CLEVES, and AJAY N. JAIN†
Bioengineering and Therapeutic Sciences, University of California, San Francisco,
San Francisco, CA 94143, USA
†E-mail: ajain@jainlab.org
www.jainlab.org
We present a probabilistic data fusion framework that combines multiple computational approaches
for drawing relationships between drugs and targets. The approach has special relevance to identi-
fying surprising unintended biological targets of drugs. Comparisons between molecules are made
based on 2D topological structural considerations, based on 3D surface characteristics, and based
on English descriptions of clinical effects. Similarity computations within each modality were trans-
formed into probability scores. Given a new molecule along with a set of molecules sharing some
biological effect, a single score based on comparison to the known set is produced, reflecting either 2D
similarity, 3D similarity, clinical effects similarity or their combination. The methods were validated
within a curated structural pharmacology database (SPDB) and further tested by blind application
to data derived from the ChEMBL database. For prediction of off-target effects, 3D-similarity per-
formed best as a single modality, but combining all methods produced performance gains. Striking
examples of structurally surprising off-target predictions are presented.
Keywords: Molecular similarity; Surflex-Sim; Patient Package Inserts; Off-Target Predictions.
1. Introduction
In prior work, we introduced a methodological approach for data fusion which was used to
predict the protein targets of small molecules based on molecular similarity.1Given a test
molecule and a set of small molecules with a known shared biological effect, the method
produces a score corresponding to the likelihood that the test molecule will share the same
activity. We showed that for predicting primary targets (i.e. targets modulating intended
therapeutic effects) the performance advantage of a 3D similarity method over a 2D method
was relatively small, due to the dominating effects of human 2D bias in drug design (i.e.
“me-too” drugs).1,2 However, for predicting secondary targets (i.e. sources of side-effects) 3D
similarity was much more effective than 2D topological comparisons. We also showed that
clinical effects of drugs could be used as a surrogate for biochemical characterization,1making
use of common side effects of muscarinic antagonism as markers for the biochemical protein-
ligand effect. It was possible using 3D chemical similarity to achieve strong separation of likely
muscarinic modulators from those with no evidence of such effects.
In the current work, we expand the analysis to a much larger set of small molecule drugs,
again making use of 2D and 3D chemical similarity computations. Additionally, computations
involving structural similarity are augmented with clinical effects similarity, made possible by
automating the extraction and weighting of relevant textual terms from drug package inserts.
The top row of Figure 1 shows two highly similar first generation sulfonylureas, tolbutamide
and tolazamide, each having highly similar pharmacological effects,3with their therapeutic
benefits deriving from identical mechanisms.4Clinical effects similarity coincides here with
Pacific Symposium on Biocomputing 2014
160
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

Figure 1.
Relationship between molecular similarity methods, the proteins they modulate, and
clinical effects in common.The top row shows two antidiabetics, tolbutamide (first in
class) and tolazamide (follow on) which are very structurally similar, interact with
similar proteins, and have similar clinical effects. The bottom row shows two anti-
epileptic drugs, carbamazepine and levetiracetam, that have different primary targets
but similar clinical effects and 3D molecular similarity. Surflex-Sim's 3D overlay is
shown at the bottom where carbamazepine is colored by green carbons and
levetiracetam is in atom color. Green sticks correspond to regions of significant
hydrophobic similarity and blue/red sticks correspond to regions of significant polar
similarity.
Yera/Cleves/Jain: Ligand Structure/Function 20
Tolbutamide Tolazamide
Levetiracetam
NH
2
N
O
O
Carbamazepine
NH
2
N
O
N
H
N
HN
O
O
OS
N
H
N
H
O
O
O
S
Intended Target:
Kir6.2
Other Target:
PPARγ-RXR
Intended Target:
Kir6.2
Other Target:
PPARγ-RXR
Intended Target:
Nav1.1
Off-Target:
Nav1.5
Intended Target:
Synaptic Vesicle
Glycoprotein 2A
Clinical Terms in Common: diabetes mellitus, blood glucose, and hypoglycemia
Similarity: High 2D and High 3D
Clinical Terms in Common: grand mal and status epilepticus
Similarity: Low 2D but High 3D
Therapeutic Indication: Antidiabetic
Therapeutic Indication: Anticonvulsant
Fig. 1. Relationship between small molecules based on molecular similarity, protein target modulation, and
clinical effects. The optimal 3D superimposition (bottom) indicates high similarity, despite little topological
commonality (green sticks correspond to regions of significant surface shape similarity and blue/red sticks
correspond to regions of significant polar similarity).
high structural 2D and 3D similarity. Next, consider the two structurally dissimilar anticon-
vulsants on the bottom of Figure 1, carbamazepine and levetiracetam. Carbamazepine was
one of the first anticonvulsants (approved in 1968), and its therapeutic benefit is attributed to
stabilizing the inactivated state of voltage-gated sodium channels (Nav1.1).5Levetiracetam is
a newer anticonvulsant, believed to act through interaction with synaptic vesicle glycoprotein
2A (SV2A).6As expected, the two package inserts have clinical effect terms in common due
to shared indications. Given the high 3D structural similarity, our expectation is that these
drugs do in fact share some molecular targets, as will be discussed later.
The present study establishes a computational method to draw relationships between drugs
based on the clinical effects present in Patient Package Inserts (PPI), whose utility for pre-
dicting drug target interactions has been shown previously.7The present study makes three
primary contributions. First, we introduce a method to extract and weight medically relevant
terms from English clinical effects information. Second, we show that drug similarity com-
puted from package inserts is directly correlated with drug similarity computed by molecular
structure comparison. Third, we established that the combination of 2D, 3D, and PPI simi-
larity yielded better off-target predictive performance over any single similarity computation.
Recovery of roughly 40–50% of off-target annotations was possible with false positive rates of
about 1–3%. The approach is generalizable to other computational modalities (e.g. docking of
ligands to protein structures), and it is our hope that broad application of the methods will
aid in identifying unexpected interactions between drugs and biological targets.
Pacific Symposium on Biocomputing 2014
161
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

2. Methods and Data
The following describes the molecular data sets, computational methods, and specific compu-
tational procedures (see http://www.jainlab.org for additional details on software, data, and
protocols).
2.1. Molecular Data Sets
In the present study two molecular data sets are used. The Structural Pharmacology Database
(SPDB) is a deeply curated drug target database that is used as the basis to make predictions.
A set of drug target annotations from ChEMBL that were not annotated in our database were
used as a blind test set.
The details of the SPDB and its relationship to other databases has been extensively de-
scribed elsewhere.1,2,8 It has two features that are particularly important for the present study.
First, “targets” are specific binding sites on proteins or protein complexes. This is a critical
distinction in order to make inferences about small molecule activity based on structural sim-
ilarity. Second, primary targets (those that are believed to be therapeutically beneficial) are
distinguished from secondary targets (which mediate pharmacologically relevant off-target ef-
fects). By making this distinction, it is possible to explicitly quantify performance of methods
for prediction of surprising effects. Of the roughly 1000 drugs within the SPDB, 602 met our
criteria for inclusion based on PPI information (see below). Of the 257 primary and secondary
targets of these 602 drugs, 91 had at least 5 annotated drugs and formed the basis of cross-
validation experiments. These 91 targets were comprised of 83 human proteins, including 28
aminergic GPCRs, 19 ligand and voltage gated ion channels, 13 human enzymes, 7 nucleotide
and short peptide GPCRs, 5 tyrosine kinases, 5 steroid receptors, 3 reuptake transporters, 2
ion transporters, and 1 transcription factor. The remaining 8 targets were bacterial, fungal,
and viral proteins. To test the methodology, we employed ChEMBL version 14, which curates
linkages between chemicals and biological targets.9For each of the 602 drugs, corresponding
ChEMBL compounds were identified based on direct structural equivalence. Equating the 91
SPDB target binding sites to ChEMBL bioactivities was done manually, yielding 65 corre-
sponding ChEMBL targets. Significant bioactivity was defined as Kd, Ki, or IC50 values less
than or equal to 1µM. There were 380 drug-target interactions present in ChEMBL that were
missing from the SPDB matrix of 602 drugs and 91 targets. This set served as a blind test set
and will be referred to as the ChEMBL set in what follows.
2.2. Patient Package Insert Similarity
We employed the well established vector space information retrieval approach10,11 to model
patient package inserts (PPIs). Text documents are modeled as vectors in high dimen-
sional space where each dimension corresponds to a term with an associated weight. Co-
incidence of terms with high weight leads to high computed similarity between documents.
The process to transform PPIs into weighted term vectors requires four steps. First, rele-
vant sections are extracted, including: Indication, Contraindications, Precautions, Adverse
Reactions, Drug Interactions, and Clinical Pharmacology. Second, term lists (up to five
Pacific Symposium on Biocomputing 2014
162
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

words each) are generated, with punctuation and short words like prepositions and ar-
ticles removed. Third, to eliminate artifactual terms and enhance relevance, terms are
identified that are part of two controlled vocabularies: Medical Subject Headings (MeSH,
http://www.ncbi.nlm.nih.gov/mesh) and the low-level Medical Dictionary for Regulatory
Activities (MedDRA, http://www.meddra.org). Last, term weights are assigned based on
information richness (e.g. “generalized seizures” >“seizures”). Word frequencies from the
Google Web 1T 5-gram Corpus (http://www.ldc.upenn.edu/Catalog/index.jsp, catalog num-
ber LDC2006T13) were used to compute term weights, with rare terms producing higher
scores than common ones. For example, “seizures” produced a log odds weighting of 4.74, but
the more specific term “generalized seizures” yielded 6.89. The final output for each drug is a
vector composed of 6,591 term weights (the weight of the term if present and zero otherwise).
From the PPI for carbamazepine, the Indication Section includes: “patients with the following
seizure types: partial seizures with complex symptomatology (psychomotor, temporal lobe).”
The unfiltered bigrams include both sensible ones such as “partial seizures” and useless ones
such as “patients with” with the filtering process eliminating the latter. For carbamazepine,
the two most heavily weighted terms were “failure liver” (8.83) and “syncope and collapse”
(8.62). The term “partial seizures” scored 6.37, with many related terms (e.g. “grand mal”)
scoring similarly.
PPISimilarity (A, B) = Pn
i=1 Ai∗Bi
pPn
i=1 A2
i∗pPn
i=1 B2
i
(1)
Comparison of a pair of drug PPI vectors is quantified using the cosine similarity metric
(Eq. 1). The metric has a range of 0–1, but its units are both arbitrary and counterintuitive.
To employ such values in our data fusion framework, the raw similarity scores were normalized
to p-values by generating a distribution of PPI similarity scores for unrelated molecule pairs.
The unrelated pairs were identified based on having low 2D and low 3D similarity, quantified
as described below with pairwise p-value comparisons ≥0.5 (we have previously shown that
structurally unrelated drug pairs very infrequently share targets1). So, given a PPI similarity
score Sbetween a drug pair, the p-value is simply the proportion of occurrences of Sor
greater in the background set. For example, the raw PPI similarity between carbamazepine
and levetiracetam was 0.286 (see Figure 1), and this corresponded to a p-value of 0.044. The
most heavily weighted terms in the comparison included the following: pancytopenia (6.6),
cytochrome p450 (6.6), grand mal (6.5), antiepileptic drugs (6.5), and partial seizures (6.4).
2.3. Target Prediction using Patient Package Insert Similarity
We have previously reported a framework for data fusion which allows for the integration
of similarity scores into a single value.1Briefly, given a molecule Aand a set a molecules
with a shared biological effect, Bn, the similarity between molecule Aand each molecule Bi
is computed. The similarity scores are normalized to p-values as detailed above by assessing
score magnitude against score from a random background set. The multinomial distribution
is then used to compute the likelihood, M, of observing the set of p-values and of the converse
probabilities, M∗. The log-odds score Lis then computed by taking the log of the ratio of M
Pacific Symposium on Biocomputing 2014
163
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

and M∗and inverting the sign. A detailed discussion of the computation and corresponding 2D
and 3D similarity example can be found in the original publication.1An attractive feature of
our methodology is that it is able to integrate the results of different similarity computations
into a single value. For example, the log-odds calculation for tolazamide interacting with
PPARγ-RXR yields single-modality values of 11.35 for PPI, 7.57 for 3D, and 5.49 for 2D.
Combining the similarity methods gives a stronger prediction compared to using any single
method alone with 3D+2D+PPI log-odds = 23.43.
2.4. Similarity and p-value Computation with Surflex-Sim
The Surflex-Sim 3D molecular similarity method and its use for virtual screening and off-target
prediction has been extensively described in multiple publications.2,8,12,13 Briefly, given two
molecules in specific poses, a value from 0 to 1 is computed that reflects the degree to which
their molecular surfaces are congruent with respect to both shape and polarity. The function
is based on the differences in distances from observer points surrounding the molecules to
the closest points on their surfaces, including both the closest hydrophobic surface points and
the closest polar surface points. So, two molecules that may have very different underlying
chemical scaffolds may exhibit nearly identical surfaces to the observer points. These points
are analogous to a protein binding pocket, which also “observes” ligands from the outside.
Additional details regarding the theory and underlying algorithmic details can be found in the
previously published work. In order to produce a log-odds value for a molecule against a list
of molecules with a shared annotation, 3D similarity values must be computed against each
annotated molecule, and these values must then be transformed into probabilities. Given the
particulars of the conformational sampling density, 3D similarity optimization thoroughness,
and empirical conversion of raw scores to p-values, the overall process required many hours
for each comparison of one molecule to a typical set of annotated molecules.
In the current work, two improvements were made to support large-scale application of the
methods. First, a new mode of pose optimization was developed in which diverse conforma-
tions of molecules are pre-generated prior to molecular comparison. Using this new mode, the
optimal pose for one molecule onto a specific pose of another can be done quickly enough to
process roughly 2 million drug-like molecules per day on a single computing core (compared
with roughly 10,000 previously). Second, rather than using explicit computation of 1000 back-
ground similarity values for each molecule (as previously), we made use of the observation
that these distributions were essentially always normally distributed. Given a molecule pair,
only the particular mean and standard deviation for each need be estimated in order to de-
rive a p-value rather than making use of the full empirical computation. Estimation of the
distributional parameters was accomplished using simple linear regression models that made
use of “molecular imprints” for each molecule.8A molecular imprint is a vector of similarity
values for a particular molecule against a fixed basis set of molecules (one pose each). Such
vectors have precedent in predicting many molecular properties,14,15 and the conformational
pre-search procedure was augmented to produce standard molecular imprints. So, given two
pre-searched molecules, their mutual maximal 3D similarity can be rapidly calculated, and
the p-value conversion is immediately derived from the estimated distributional parameters
Pacific Symposium on Biocomputing 2014
164
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

for each molecule. Taken together, the two improvements allow for typical 3D log-odds com-
putations to be made in a few minutes for a given molecule against a target characterized by
twenty known ligands. To test the accuracy of the faster method, we recomputed the p-values
and log-odds values from our previous work. An all-by-all similarity of the 358 drugs from
the original study yielded a Pearson’s correlation of 0.947 and Kendall’s tau of 0.814, both
highly statistically significant. The full log-odds computation of 358 drugs against 44 targets
yielded a Pearson’s correlation of 0.955 and Kendall’s Tau of 0.761 (again highly statistically
significant).
For 2D molecular similarity computations, which make purely topological comparisons
between molecules, we employed the previously described GSIM-2D method.1,2 This method
is sufficiently efficient that empirical conversion of raw scores into p-values is possible, as
we have previously described.1For this method to yield high similarity, two molecules must
be roughly the same size and contain similar subgraph compositions, especially for those
subgraphs rooted at heteroatoms.
3. Results and Discussion
3.1. Relationship between Structural Novelty and Clinical Effects
Previously, we quantified the effect of me-too drugs by showing that drug pairs with high
2D and high 3D similarity had four times more likelihood of having identical primary and
secondary targets than drugs pairs where one was structurally novel.1Here, this analysis has
been extended to clinical effects by making use of the lexical similarity of package inserts.
Both to establish the relevance of the PPI similarity metric and to quantify the degree to
which structural novelty is related to changes in clinical effects, we computed the pairwise 2D,
3D, and PPI similarity of all 602 drugs. The drug pairs were separated into four categories
based on chemical structural similarity: high 2D and 3D similarity, low 2D but high 3D, high
2D but low 3D, and low 2D and 3D. High similarity included pairs with p-values ≤0.01 and
low similarity were those with p-values ≥0.5.
Figure 2A shows the histogram of the PPI p-value distributions for each of the four struc-
tural categories. It is clear that the “me-too” drug distribution (red line, drug pairs with high
2D and high 3D similarity) is different than the others. Toward the left side of the plot, where
clinical effects similarity was high (PPI p-values ≤0.05), a large fraction of the me-too drug
pairs had highly similar clinical effects. Structurally novel drug pairs (high 3D but low 2D
similarity, green line) exhibited a significantly smaller fraction with highly concordant clin-
ical effects but still showed some relationship between structural similarity and therapeutic
profile. The high 2D and low 3D pairs had little signal (blue), and only a very small portion
of structurally dissimilar drug pairs (low 2D and low 3D, magenta) shared clinically similar
effects. Clearly, drug pairs with very high structural similarity (both by 2D and 3D meth-
ods) were much more likely to have closely shared clinical effects than molecule pairs of any
other category, even those sharing high 3D similarity but low 2D similarity. The converse
observations paralleled these observations. Figure 2B shows the corresponding histograms of
3D and 2D p-value distributions where molecule pair segregation was made based on clinical
effect similarity. The 2D and 3D similarity p-value distributions for drug pairs with high PPI
Pacific Symposium on Biocomputing 2014
165
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

0 0.2 0.4 0.6 0.8 1
Figure 7.
Quantifying the effect of me-too drugs based on PPI similarity.Panel A shows the PPI p-
value distribution of drug pairs that were segregated based on 2D and 3D p-values into
the four bins shown above (number of pairs per quadrant are shown in parentheses).
Drug pairs with high 2D and high 3D have a higher likelihood of having significant
phenotypic effects than molecules with low 2D but high 3D. Panel B shows the 3D p-
values distribution of drug pairs that were segregated based on high and low PPI p-
values.len(high_ppi): 3968
len(low_ppi): 88539
Yera/Cleves/Jain: Ligand Structure/Function 26
Frequency
PPI p-value 3D or 2D p-value
high 2D/high 3D (1065)
2D & 3D p-val ≤ 0.01
low 2D/high 3D (728)
2D p-val ≥ 0.50 & 3D p-val ≤ 0.01
high 2D/low 3D (428)
2D p-val ≤ 0.01 & 3D p-val ≥ 0.50
low 2D/low 3D (79654)
2D & 3D p-val ≥ 0.50
A B
0 0.2 0.4 0.6 0.8 1
2D high PPI
2D low PPI
3D high PPI
3D low PPI
Fig. 2. Relationship between structural similarity and clinical effects similarity.
similarity (red and green lines) showed stronger enrichment for low p-values associated with
high 3D structural similarity. As expected, drug pairs that had low PPI similarity (blue and
brown lines) also had low 3D and 2D structural similarity.
3.2. Internal SPDB Validation: Off Target Effects
An attractive aspect of the log-odds framework is that it allows us to combine different types
of similarity computations into a single value. For each of the 602 drugs in our dataset, we
computed the 2D, 3D, PPI, and combination log-odds scores of interacting with each of the 91
targets that had at least 5 drugs as ligands in the SPDB. In each case, any self/self comparisons
were omitted from the calculations, making this exercise a leave-one-out cross-validation of
the log-odds predictive methodology. The three methods were used independently and in
combination to predict the log-odds of known primary and secondary target interactions.
As we observed in our previous study, primary target predictions were dominated by the
presence of me-too drugs, limiting the differences between any methods (data not shown).
However, for prediction of secondary targets, i.e. those that mediate side-effects, significant
differences appeared. Table 1 summarizes the true-positive rates observed for difference log-
odds computations for secondary target prediction at different score thresholds.
Table 1. SPDB Secondary Target Performance
Log-Odds 3D 2D PPI 3D+2D 3D+PPI 2D+PPI 3D+2D+PPI
0 97 90 96 95 98 97 97
10 43 7 14 55 61 33 64
20 16 0 0 23 26 1 38
Pacific Symposium on Biocomputing 2014
166
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

For all single methods and combinations of methods, the information present in the anno-
tated drugs yielded positive information, evidenced by high true-positive rates at a log-odds
threshold of 0. However, substantial differences among the methods appeared as higher log-
odds thresholds were considered. At a threshold of 10, the 3D similarity approach showed a
much higher retrieval rate than either of the other two single-mode methods. All combinations
of methods showed synergy, with the most effective retrieval occurring with a combination
of all three similarity methods to produce a single log-odds score. Roughly 60% of the true
secondary target annotations could be recovered using the log-odds score from 3D+2D+PPI
similarity computations. Note, however, that true positive rates without the context of false
positive rates can be very misleading. The issue of estimating false positive rates is not straight-
forward though. In our SPDB, a missing annotation between a drug and a target does not
mean that the interaction does not occur. Authentic interactions within our 602 drug/91 target
set may have been published after our curation or have yet to be biochemically characterized.
Nonetheless, we expect that the large majority of unannotated interactions, in fact, represent
true negative data. So, as a surrogate for a measurement of false positive rates for our sim-
ilarity methods, we determined the number of drug/target predictions for interactions that
were unannotated. At log-odds thresholds of 5, 10, and 20, predictions for non-existent SPDB
annotations for both 3D similarity alone and 3D+2D+PPI were 3%, 1%, and 0.2%. These are
upper limits of false positive predictions. As will be described below, the false positive rate
was actually lower since many of the new predictions were validated as true by incorporating
annotations from the ChEMBL database.
3.3. Prediction of New Drug-Target Pairs within ChEMBL
As discussed above, a missing annotation within the SPDB between a drug and a target does
not necessarily mean that the interaction does not occur. For example, the drugs orphenadrine
and mesoridazine showed high 3D log-odds against the muscarinic receptor but the interactions
had been unannotated in the SPDB. Careful inspection of the literature revealed that the
drugs were known to antagonize muscarinic receptors.1Therefore, drug target annotations
that are known but missing from our SPDB can serve as a blind set to test our methodology.
To supplement annotations within the SPDB with a blind set for methodological testing,
we searched ChEMBL and found 380 biochemically characterized drug/target interactions
not present in the SPDB. We then investigated how well the methodology could identify the
new ChEMBL annotations based only upon information within in the SPDB as the basis to
compute the log-odds.
Table 2 shows the proportions correctly predicted at various log-odds using different meth-
ods and combinations. In general, the trends observed for the SPDB leave-one-out experiments
were borne out. Among individual methods, 3D similarity strongly outperformed 2D- or PPI-
Table 2. ChEMBL Prediction Performance
Log-Odds 3D 2D PPI 3D+2D 3D+PPI 2D+PPI 3D+2D+PPI
5 43 14 13 42 41 19 41
10 16 3 3 20 18 8 22
20 2 0 0 3 1 0 4
Pacific Symposium on Biocomputing 2014
167
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

based similarity, with the latter two having similar performance. However, the combination of
the three methods, overall, yielded better performance than 3D alone. At log-odds thresholds
of 10 and 20, using the full combination of methods, the percentage of recovered annotations
within the SPDB test set was 22% and 4%, respectively. This compared with 16% and 2%
using 3D similarity alone, and 3% and 0% using either 2D or PPI similarity alone. The enrich-
ment ratios for the combination approach, using the upper-bound false positive rates discussed
above, corresponded to 22-fold and 40-fold, respectively, at log-odds thresholds of 10 and 20.
Figure 3 shows a typical example of a drug/target interaction not annotated in the
SPDB where the combination similarity approach confidently identified a pharmacolog-
ically relevant target. Sibutramine is an anorexic annotated in the SPDB as a ligand
of the serotonin and norepinephrine reuptake transporters. However, it has been shown
that sibutramine also interacts with the dopamine reuptake transporter and that this in-
teraction contributes to the therapeutic benefit (indicated in the Meridia package insert,
http://www.rxabbott.com/pdf/meridia.pdf). Computing the similarity between sibutramine
and 11 other dopamine reuptake transporter inhibitors (two are shown in Figure Figure 3),
the log-odds were 2.3, 4.2, and 6.9 using 2D, 3D, and PPI, respectively. These predictions
were strengthened by combining all three methods, with corresponding log-odds of 9.4. The
pairwise PPI similarities between sibutramine and bupropion and nefazodone are highly signif-
icant as are the individual 3D similarities. Clinical effects can be sufficient to infer off-targets,
nefazodone
terms: 547
Sibutramine
terms: 464
Primary: 5-HT & Norepinephrine reuptake transporters
Figure 15.
ChEMBL Example: PPI alone can infer off-target effects and combining with 3D makes
stronger inference.
Yera/Cleves/Jain: Ligand Structure/Function 36
2D Sim: 0.085 (p = 0.061)
bupropion
terms: 413
3D Sim: 7.1 (p = 0.045)
Predicted Target 2D 3D PPI 3D+PPI 2D+3D+PPI
Dopamine reuptake
transporter 2.3 4.2 6.9 9.2 9.4
term (212 in common) weight
tooth disorder 8.04
flu syndrome 7.41
abnormal dreams 7.20
emotional lability 6.95
hypomania 6.36
term (250 in common) weight
thinking abnormal 8.07
tooth disorder 8.04
abnormal dreams 7.20
emotional lability 6.95
suicidal ideation 6.23
PPI Sim: 0.420 (p = 0.001) 3D Sim: 7.6 (p = 0.028)
2D Sim: 0.032 (p = 0.506) PPI Sim: 0.432 (p = 0.001)
Cl
N
Cl
N
N
N
N
N
O
O
Cl
HN
O
Fig. 3. ChEMBL example showing that combination similarity effectively predicts a drug target interaction
not covered within the SPDB. Shown are the 2D structures, 3D overlays, and common clinical terms between
sibutramine and two dopamine reuptake transporter inhibitors, bupropion and nefazodone.
Pacific Symposium on Biocomputing 2014
168
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

StructuralNoveltyofChEMBLandSPDB
Fluoxetine Prediction:M3ligand Apomorphine Prediction:D3ligand
ChEMBL
SPDB
q
uency
M3LogOdds
highest2Dsimilarity
NO
D3LogOdds
highest2Dsimilarity
Fre
q
2D: 1.2
3D:7.7
PPI: 3.7
Methadone
2Dp‐val=0.041
3Dp‐val=0.0074
2D: ‐1.1
3D: 7.7
PPI: 1.2
Ropinirole
2Dp‐val=0.210
3Dp‐val=0.00035
2Dp‐val
00.2 0.4 0.6 0.8 1
Fig. 4. A near-neighbor analysis for each of 602 drugs (SPDB in green, ChEMBL in red) based on target
annotations from the SPDB.
but combining similarity methods generally adds confidence to predictions.
Note, however, that the numerical performance on the ChEMBL set was lower than for
the SPDB set in terms of pure true positive recovery rates (see Tables 1 and 2). This stemmed
from an increase in structural diversity for molecules within ChEMBL compared to those
molecules within the SPDB for the target identified by the ChEMBL annotation. To quantify
structural novelty, we performed a nearest-neighbor analysis. For each drug within ChEMBL,
the most similar 2D representative from the SPDB was identified (based on p-value) from
within the collection of drugs having the same target annotation. An analogous leave-one-out
computation was performed for each drug target annotation within the SPDB. Figure 4 shows
a histogram of the distributions of p-values for the ChEMBL (red line) and SPDB (green line)
sets. Within the SPDB set, there were substantially more cases with extremely low p-values
than for the ChEMBL set. The nearest structural neighbor for each ChEMBL test molecule
were generally more divergent. Two examples are highlighted from the ChEMBL set where
the nearest neighbor had poor 2D p-values relative to the much more significant 3D p-values
which provided support for high log-odds scores.
Fluoxetine (blue box) is a selective serotonin reuptake inhibitor which mediates its ther-
apeutic benefit through inhibition of the 5-HT reuptake transporter. The ChEMBL data in-
dicated that fluoxetine also interacts with the muscarinic M3receptor. The nearest-neighbor
molecule sharing this annotation was methadone (2D p-value = 0.041). Considering all of the
muscarinic M3receptor ligands (38 total), the 2D, 3D, and PPI log-odds were 1.2, 7.7, and
3.7 respectively. Combining all of the methods gave a score of 8.2.
Apomorphine (red box) is indicated to treat Parkinson’s disease and its therapeutic benefit
is thought to be primarily due to activating dopamine D2receptors. However, apomorphine
was indicated within ChEMBL to also interact with the dopamine D3receptor (which is also
known to play a role in the beneficial effects for other anti-Parkinsonian drugs). The nearest-
neighbor drug within the D34 ligands was ropinirole (2D p-value = 0.210), which is structurally
distinct in a topological sense in Figure 4. As in the previous case, when considering all 11
dopamine D3ligands, the 3D comparisons provide primary support for a positive log-odds
Pacific Symposium on Biocomputing 2014
169
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

score. The 2D, 3D, and PPI log-odds were -1.1, 7.7, and 1.2 respectively. The combination of
all three comparison types yielded a score of 3.3. Here, the 3D molecular similarity information
was the most reliable predictor.
4. Conclusion
In the present study, we report a means to combine chemical similarity between molecules
with information derived from computing similarity based upon lexical analysis of patient
package inserts (PPI). As expected based on our prior work, drugs that were highly struc-
turally similar (both by 2D and 3D comparison) were much more likely to have significant
overlap of their clinical effects compared to drugs that were structurally different (low 2D
similarity but high 3D similarity). Our prior work illustrated a similar effect with respect to
specifically annotated molecular targets: me-too drugs tend to have nearly identical target
profiles.1The correlation between lexical and chemical similarity also served to validate the
lexical comparison methodology.
We extended a probabilistic data fusion method to include observations from both molec-
ular and clinical effects similarity and reported performance on predicting protein targets of
small molecules. This was done both by leave-one-out cross-validation on our internal database
of drug-target interactions (the SPDB) as well as on a blind test on new interactions present
in ChEMBL. For off-target prediction within the SPDB, 3D similarity was the most effec-
tive single information source. However, combining the methods predicted a larger proportion
of secondary targets than any of the individual methods, while maintaining a similar nomi-
nal false positive rate. On the test against previously unseen ChEMBL drug-target linkages,
again 3D similarity was the single most effective predictor, but gains were derived from com-
bining the different data sources. We note that the method supports the integration of any
method that produces scores relating molecules to targets (e.g. docking), and that inclusion
of additional information sources is likely to produce further benefits. It is also important to
understand that this framework is similar in character to virtual screening methods, in that
while enrichment for compounds with the predicted effects occurs, the actual potencies of the
effects are not predicted. This point is discussed at length in a prior study.16
In contemplating the problem of off-target prediction for drugs, the problem of molecular
design ancestry can confuse the issue of methodological validation. For example, ligands of
aminergic GPCRs offer troublesome test case, owing to the established promiscuity of such
drugs among numerous targets.17 Returning to Figure 1 (bottom), we see the example of lev-
etiracetam, an anticonvulsant believed to have a unique mechanism of action when compared
with most existing anticonvulsants. The established CNS targets of the major classes of an-
ticonvulsant drugs include the GABAAreceptor (for barbiturates such as pentobarbital) and
neuronal voltage-gated sodium channels (for drugs such as carbamazepine and phenytoin).
These drugs have been recently shown to modulate voltage-gated potassium channels as part
of their anti-epileptic effects.18–21 Levetiracetam, having a novel scaffold, has been proposed
to work through an entirely new mechanism of action due to high binding affinity to the
synaptic vesicle protein SV2A (which is not a known therapeutic target of any drug).6,22,23
Our methods strongly predict that levetiracetam is a voltage-gated sodium channel modulator
Pacific Symposium on Biocomputing 2014
170
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.

with 3D log-odds alone of 14.5 (the combination log-odds was 21.4). Levetiracetam has been
shown to inhibit voltage-gated potassium currents,22 leading to the suggestion that this drug,
like other anti-epileptics, acts at least in part through potassium channels. Considering that
many antiepileptics modulate both sodium and potassium channels,23 our prediction supports
the notion that levetiracetam shares a similar mechanism of action, perhaps in addition to
the interaction with SV2A.
Identification of off-target activities of drugs is a difficult problem, particularly in cases
where the drug in question has a non-obvious structural relationship with the known ligands
of a given target. Our hope is that methods that make use of multiple information sources
will help to identify clinically important and unexpected effects.
References
1. E. Yera, A. Cleves and A. Jain, Journal of Medicinal Chemistry 54, 6771 (2011).
2. A. E. Cleves and A. N. Jain, Journal of Computer-Aided Molecular Design 22, 147 (2008).
3. J. Wright and R. Willette, Journal of Medicinal Chemistry 5, 815 (1962).
4. K. Nagashima, A. Takahashi, H. Ikeda, A. Hamasaki, N. Kuwamura, Y. Yamada and Y. Seino,
Diabetes Research and Clinical Practice 66, S75 (2004).
5. D. S. Ragsdale and M. Avoli, Brain Research 26, p. 16 (1998).
6. B. Lynch, N. Lambeng, K. Nocka, P. Kensel-Hammes, S. Bajjalieh, A. Matagne and B. Fuks,
PNAS 101, 9861 (2004).
7. M. Campillos, M. Kuhn, A. Gavin, L. Jensen and P. Bork, Science 321, 263 (2008).
8. A. E. Cleves and A. N. Jain, Journal of Medicinal Chemistry 49, 2921 (2006).
9. A. Gaulton, L. Bellis, A. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey,
D. Michalovich and B. Al-Lazikani, Nucleic Acids Research 40, D1100 (2012).
10. G. Salton and M. J. McGill, Introduction to Modern Information Retrieval (McGraw-Hill, Inc.,
New York, NY, USA, 1986).
11. J. Han, M. Kamber and J. Pei, Data Mining: Concepts and Techniques, Second Edition (Morgan
Kaufmann Series in Data Management Systems), 2 edn. (Morgan Kaufmann, 2006).
12. A. N. Jain, Journal of Computer-Aided Molecular Design 14, 199 (2000).
13. A. N. Jain, Journal of Medicinal Chemistry 47, 947 (2004).
14. A. Ghuloum, R. Carleton and A. Jain, Journal of Medicinal Chemistry 42, 1739 (1999).
15. J. Mount, J. Ruppert, W. Welch and A. Jain, Journal of Medicinal Chemistry 42, 60 (1999).
16. A. Jain and A. Cleves, J Comput Aided Mol Des 26, 57 (2012).
17. M. J. Keiser, B. L. Roth, B. N. Armbruster, P. Ernsberger, J. J. Irwin and B. K. Shoichet,
Nature Biotechnology 25, 197 (2007).
18. C. Zona, V. Tancredi, E. Palma, G. Pirrone and M. Avoli, Canadian Journal of Physiology and
Pharmacology 68, 545 (1990).
19. F. Bloom, D. Kupfer and B. Bunney, Psychopharmacology: The Fourth Generation of Progress
(Raven Press, 1995).
20. M. Nobile and P. Vercellino, British Journal of Pharmacology 120, 647 (1997).
21. A. Ambr´osio, P. Soares-da Silva, C. Carvalho and A. Carvalho, Neurochem. Res. 27, 121 (2002).
22. M. Madeja, D. Georg Margineanu, A. Gorji, E. Siep, P. Boerrigter, H. Klitgaard and E. Speck-
mann, Neuropharmacology 45, 661 (2003).
23. R. Surges, K. Volynski and M. Walker, Therapeutic Advances in Neurological Disorders 1, 13
(2008).
Pacific Symposium on Biocomputing 2014
171
Biocomputing 2014 Downloaded from www.worldscientific.com
by 46.246.28.95 on 12/21/15. For personal use only.