Vol. 25 no. 17 2009, pages 2251–2255
Data and text mining
A survey of across-target bioactivity results of small
molecules in PubChem
Lianyi Han, Yanli Wang∗and Stephen H. Bryant∗
National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
Received on February 12, 2009; revised on May 20, 2009; accepted on June 16, 2009
Advance Access publication June 23, 2009
Associate Editor: Alfonso Valencia
This work provides an analysis of across-target bioactivity results
in the screening data deposited in PubChem. Two alternative
approaches for grouping-related targets are used to examine
a compound’s across-target bioactivity. This analysis identifies
compounds that are selectively active against groups of protein
targets that are identical or similar in sequence. This analysis also
identifies compounds that are bioactive across unrelated targets.
Statistical distributions of compounds’ across-target selectivity
provide a survey to evaluate target specificity of compounds by
deriving and analyzing bioactivity profile across a wide range of
biological targets for tested small molecules in PubChem. This work
enables one to select target specific inhibitors, identify promiscuous
compounds and better understand the biological mechanisms of
target-small molecule interactions.
Contact: firstname.lastname@example.org; email@example.com
Bringing a new drug to market is a time consuming and costly
process that is also governed by complex regulatory procedures. It
is a 100 million dollar challenge typically results in failure (Riggs,
2004). As the drug discovery process gets more expensive as it
proceeds with time, it is very important to select drug candidates
efficiently and to exclude those with undesired characteristics that
of the earlier stages of drug development is hit selection employing
High-throughput Screening (HTS) technology. With rapid advances
in instrumental automation, combinatorial chemistry and assay
technologies, hundreds of thousands of compounds can be tested
in a matter of days (Burbaum and Sigal, 1997; Hann and Oprea,
2004). However, the effectiveness of the HTS technology is usually
compromised by hits that fail in later stages of drug development,
especially in time consuming and costly clinical trials, due to
unsatisfactory pharmacokinetic properties, poor pharmacodynamic
positives are across-target compounds that can act non-selectively
on unrelated targets and cause unwanted effects or adverse drug
efforts need to be taken during hit selection to identify compounds
with undesired physiochemical properties or unwanted bioactivities
∗To whom correspondence should be addressed.
towards certain targets, in order to exclude them from the lead
One straightforward way to verify the target specificity of a
compound is through a data mining process via literature and/or
extensive expert knowledge. Another approach is to design a
however it is usually aimed at a limited number of targets and a
small number of compounds at the decision-making point when
the hit is ready to be progressed to a lead (Azzaoui et al., 2007;
Whitebread et al., 2005). Furthermore, an assay panel is often
designed to include only pre-selected targets with known biological
relationships. While such an approach is useful to identify or verify
the expected biological activity against related targets, it is not ideal
to discover ‘unexpected’ or off-target effects. Additionally, such
screening data are mostly proprietary. Thus, it remains a challenge
to obtain a comprehensive biological activity profile of a small
molecule for investigating its target selectivity and specificity.
In 2005, the NIH Molecular Library Roadmap Initiative
(Zerhouni, 2003) funded a nation-wide screening center network
(Austin et al., 2004) to perform industrial scale HTS screening
tests for a large collection of compounds. All of the biological
activity data produced by this HTS campaign over the past
3 years are now publically available through the PubChem Bioassay
database (http://pubchem.ncbi.nlm.nih.gov) at the National Center
of Biotechnology Information.As of March 1, 2009, over 42 million
of biological activity information, generated by the unprecedented
effort led by the National Institutes of Health, provides quantitative
have been tested in a single or multiple bioassays contained in
PubChem. The PubChem resource provides for the first time,
the opportunity for researchers to freely access screening data.
It also opens great challenges to analyze this complex chemical
biology data and to explore drug-target specificity and interaction
mechanisms for rapid and efficient discovery of drug leads (Gribbon
and Sewing, 2005; Macarron, 2006; Marshall, 1987). The growth
rate of biological test results in PubChem has accelerated as the
NIH Molecular Library Program (MLP) enters its second phase for
developing chemical probes. The need to develop means to analyze
and evaluate the rich and complex bioactivity results in PubChem
has become imperative.
This work is aimed at data mining of the biological test
results generated by the NIH MLP initiative by analyzing the
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
L.Han et al.
target specificity properties of small molecules. We propose two
approaches to derive and analyze the across-target bioactivity of a
set of compounds. One is based on distinct targets, while the other
is based on target cluster to discover their target selectivity and
promiscuity. By examining and comparing the biological activity
of the small molecules, one may identify compounds with desired
selectivity for a given protein target or identify compounds that
exhibit promiscuity. The intent of the article is to provide a timely
survey on the bioactivities of newly identified bioactive compounds
and provide additional insights into some well-studied compounds.
2.1 BioAssay and compound collection in the
across-target compound bioactivity analysis
As of March 1, 2009, there were 660 single target bioassays deposited
in PubChem, which contained biological activity outcomes, identified as
‘active’ versus ‘inactive’ status specified by the assay result provider, as
well as detailed screening results for the respective protein targets. This
analysis utilized the activity outcome as specified by each bioassay data
depositor. For screening assays, active and inactive statuses were defined
based on the percentage of inhibition from test at a single concentration.
For confirmatory assays, active and inactive statuses were defined based
on EC50/IC50 values, derived from dose response curves following testing
with multiple concentrations. The biological activities of 588918 small
molecules toward 267 unique protein targets were reported in these bioassay
records, and results were found for a total of 103518 compounds. These
from the PubChem database. In a case when contradicting test results
were observed in a bioassay, such results were treated as inconclusive
bioactivity outcome and excluded from this analysis. As a result, 10957
compound-target pairs demonstrating inconclusive results were excluded
from a total of 42349644 results. In addition, by taking advantage of the
property profiling bioassays recently deposited in PubChem, compounds
identified as active in the dithiotreitol (DTT) profiling bioassay (AID: 1234)
and luciferase inhibition profiling bioassay (AID: 1269, 1379, 411 and
773) were excluded from test results of bioassays employing similar assay
2.2 Non-redundant target-based compound bioactivity
In this analysis, the across-target compound activities were analyzed by
comparing their biological test results across distinct protein targets. As
can be tested in different bioassays. In this case, the test results were grouped
based on target sequence identity. As a result, a total of 588918 compounds
resulting from 660 bioassays were divided into 267 groups in such a way
that each group represents the combined test results for one unique protein
target. The number of distinct targets for each compound was summarized,
2.3 Target cluster-based compound bioactivity
insights into compound activity across-target families. Protein target clusters
were derived using a single linkage clustering method based on sequence
similarity. The BLAST (Altschul et al., 1997) program was employed to
compare and calculate sequence similarities among all of the 267 distinct
protein target sequences under this study. A BLAST E-value threshold of
0.001 was used as sequence similarity cutoff to draw boundaries between
target clusters. Following that, the 588918 compounds resulting from
660 bioassays were divided into target cluster groups so that each group
represented the combined test results for one protein target cluster. Test
results were grouped and analyzed in a similar way as in the target
PubChem bioassays were characterized based on how the activity outcome
was derived. Often a series of bioassays were performed for the same
biological target system and results were reported as separate bioassay
records, whereas analysis of bioactivity outcome was first done based on
a single concentration test in the primary screening, and was then confirmed
based on a follow up multiple concentration-response test. Usually, only a
small fraction, 0–10% of the compounds were confirmed as ‘active’ in the
later multiple concentration experiment.As the confirmatory bioassays most
likely have a lower false positive rate and suggest a more accurate activity
measurement, both target and target cluster-based analysis were carried out
based on test results reported through confirmatory bioassays, in addition to
the analysis using all of the available screen results for providing general
background information. In this study, 341 assays out of a total of 660 were
confirmatory assays, whereas a total of 331702 unique compounds were
tested in these confirmatory assays, involving 165 distinct protein targets.
Analysis using a subset of biological test results
3 RESULTS AND DISCUSSION
3.1 Identification of compounds with specific and
In this study, each compound was analyzed by enumerating
their distinct targets over their demonstrated biological activity.
Distribution of bioactivity among the compounds and the
corresponding targets derived from the test results for the 267
distinct targets was analyzed and is shown in Figure 1. The x-axis
indicates the number of targets and the y-axis gives the frequency of
compounds that are active across a given number of distinct targets.
There are two datasets shown in the Figure 1: 1> statistics for
active compounds based on all bioassays (including primary,
confirmatory); 2> statistics for active compounds based on con-
firmatory bioassays. For both datasets, a substantial fraction of
the compounds (57.7% and 73.5%, respectively) demonstrated
single target activity. We also observed a significant fraction
of compounds exhibiting across-target activity, with the number
of compounds dropping exponentially as the number of targets
and those derived from all bioassays, it suggested that the number
of compounds active against multiple targets reduced significantly
in the confirmatory assays, by 5–10-fold on average. This may be
attributed to the fact that a substantial number of the hits in the
primary bioassays turned out to be negative in the confirmatory
screenings. It should also be noted that not every hit in the primary
screening was necessarily subjected to confirmatory test.
It is essential to examine the data completeness when analyzing
compound target selectivity (Mestres et al., 2008). To further
evaluate the suggested target selectivity, the profile of tested
targets was also analyzed for each bioactive compound as
shown in Figure 2a and b, which utilizes all screening (primary
+ confirmatory) results as well as confirmatory results alone.
Figure 2 shows that the majority of the target specific compounds
have been tested in many targets. For example, over 50% of the
selective compounds have been tested in more than 70 distinct
targets (Fig. 2a), which suggests a higher potential of target
A survey of across-target bioactivity
Fig. 1. Distributions of compound activity among 267 targets.
Fig. 2. (a) Completeness of compound tested among primary and
confirmatory targets. (b) Completeness of compound tested among
selectivity for such compounds. This should be of interest for
chemical probe analysis or lead compound development, though
the target specificity and selectivity of individual compounds needs
to be investigated further.
This analysis enables one to identify potential target specific
compounds, examine their potency, and evaluate target selectivity
by considering all of the screening results provided in PubChem.
These analyses used to be time consuming as a laborious search of
various bioactivity information was required.
Fig. 3. Distributions of compound activity among 116 target clusters.
3.2 Identification of compounds with specific and
across-target cluster bioactivity
broad interest to investigate the underlying biological mechanisms.
In the case when a compound hits closely related protein targets,
it might be due to the significant similarities among the protein
target sequences, or due to the highly conserved 3D structures
around the binding pockets of the target proteins. On the other
hand, if a compound shows multiple activities across unrelated
targets, it might be interesting to investigate the possible causes
behind its promiscuity. Thus, it is desirable to further explore the
biological relationship of the protein targets, and to distinguish
compounds responding to biologically related targets from those
which interact with unrelated protein targets presumably through
different molecular mechanisms. Towards this end, protein targets
in the PubChem BioAssay database were compared and clustered.
Protein targets can be clustered based on 3D structure comparison to
This is, however, impractical in the current analysis due to the lack
of known experimental structural data for certain targets. Thus, in
this study, the target clusters were derived based on the sequence
In this analysis, the 267 non-redundant targets were clustered
based on their sequence similarity measured by the BLAST
(Altschul et al., 1997) algorithm. As a result, 116 target clusters
were derived, which include protein families such as kinase,
phosphatase, protease and G protein-coupled receptor. Similar to
compound activity across the derived target clusters were obtained
for two datasets as shown in Figure 3. This analysis shows that
>50% of the compounds are active only against targets similar
in sequences. It also shows that there is a substantial number of
compounds revealing activity across non-related or distantly related
protein targets. Comparison of the results given in Figures 1 and 3
resembled each other. Compounds showing selectivity to a single
but otherwise inactive in other target clusters. One example of this
would be a group of compounds showing activity across several
members of one protease family including Complement factor C1s,
Factor XIa, Factor XIIa,Thrombin, Kallikrein-related peptidase and
Cathepsin G as shown in Figure 4.
L.Han et al.
Fig. 4. A PubChem Heatmap display showing a cluster of compounds
together with their biological test results across a group of related protein
targets. Clusters of compounds (represented as PubChem Compound
identifier ‘CID’) were derived based on 2D structure similarity and shown
vertically. Clusters of BioAssays (represented as PubChem BioAssay
identifier ‘AID’) were derived based on the sequence similarity of the
tested targets and shown horizontally, where the GenBank identifiers of the
corresponding protein targets are listed at the bottom of the heatmap view.
molecule for the corresponding target, with ‘active’ results denoted by red
color, and ‘inactive’ results denoted by blue color.
There were relatively few compounds that showed activity across
a wide range of target clusters. While there were 43753 compounds
observed with multiple target activity, only 10202 compounds were
found to be active across multiple target clusters, and no compounds
dataset. When looking into such compounds and the corresponding
protein clusters, it becomes apparent that one cause for the across-
target activity is the structure conservation among distantly related
protein families. For instance, it is known that some members of
the cysteine protease family share low-sequence similarities. By
examining the experimental crystal structures for certain members
of this protein family, however, highly conserved 3D structures are
observed around the binding pocket. Thus, it is not surprising that
a number of compounds showing activity for both Cathepsin B and
Cathepsin G based on screening results in PubChem.
This across-target cluster analysis also revealed compounds
that show activity towards non-biologically related proteins. One
such compound is myricetin (PubChem CID:5281672), a flavonoid
that is commonly found in natural food source. An examination
using the PubChem biological test results suggests that this
compound is identified as an inhibitor of several proteins such as
aldehyde dehydrogenase, Leishmania Mexicana Pyruvate Kinase,
H etc., with a strong potency (IC50 <10 uM). Such observation
using PubChem bioactivity data agrees well with reports in the
literature where the inhibition activity and possible mechanism of
action of this small molecule have been widely discussed (Feng
et al., 2008; Lee et al., 2007; Lu et al., 2006; McGovern et al.,
2002; Ryan et al., 2003; von Moltke et al., 2004; Wu et al., 2008).
contains a number of compounds that are either drugs on the market,
been reported in the literature. In this case, the biological results
in PubChem can be readily compared with previously reported
bioactivity data using the PubMed links in PubChem, which are
either provided by depositors, authors of articles or through MeSH
annotation. However, there is a large portion of compounds in the
MLSMR collection that either has not been well characterized, or
the biological activity information has not been reported in scientific
journals. In this analysis, 42246 compounds, which account for
96.5% of the total 43753 compounds identified with across-target
activities, do not have any references provided to PubMed articles.
The current analysis may provide insights into the biological
activities for those compounds identified by the NIH Molecular
PubChem is a public and open data repository system.
The information content within PubChem was contributed by
investigators from many organizations. Biological test results in
the BioAssay database are diverse, and the criteria employed when
determining bioactivity outcome varies depending on the scientific
rationale chosen by each individual investigator. Data analysis
using PubChem biological results is conceivably affected by the
accuracy of such test results. Our analysis shows that a portion
of compounds are observed with single target activity when tested
against a wide range of protein targets, which suggests that most of
the investigators employed conservative thresholds when assigning
With the abundance of information on bioactivities of small
molecules recently made available through PubChem, it has been
a challenging task to mine the biological test results for drug
selectivity evaluation and compound promiscuity identification
by analyzing the biological test results in PubChem. To the
best of our knowledge, this work provides the first analysis on
target specificity and promiscuity for a large library of bioactive
compounds recently identified by the NIH Molecular Library
Program. Statistical distribution of compound target selectivity was
obtained and presented as a survey on across-target bioactivities of
as well as across-target or across target cluster activity were
identified and reported. These data suggest that the proposed
approach for across-target activity analysis can be an efficient way
for selecting target specific compounds and identifying promiscuous
compounds using the biological results in PubChem. The current
analysis and survey may provide insights into a compound’s
bioactivity against previously undiscovered target. Although this
set of candidate compounds for researchers to further investigate
their target selectivity and the mechanisms of observed promiscuity,
if any. Furthermore, statistical models can be developed based on
the accumulated knowledge for in silico analysis and prediction of
As is the norm for the NIH Molecular Library Program, primary
HTS screenings for a large compound library are usually performed
first. Based on the outcome of this screening, a small subset of
compounds, usually no more than a few hundreds, are cherry picked
A survey of across-target bioactivity Download full-text
at a series of concentrations. The survey in this study shows that
a significant fraction of those selected compounds demonstrated
activity against multiple targets or multiple target clusters.Though it
is beneficial for the research community to obtain a comprehensive
activity profile of a small molecule regardless of previously reported
activity, the identification of these compounds as done in the current
analysis may provide guidance for compound selection and target
specificity evaluation when planning further tests, especially tests at
later stages in chemical probe development.
The PubChem BioAssay resource provides unprecedented
opportunities for large-scale bioactivity analysis. Such analysis
can be used to facilitate hit selection, off-target evaluation and to
small molecules and their targets, thus, to aid the discovery and
development of chemical probes and novel drugs.
We acknowledge the editorial assistance of the NIH Fellows
Funding: Intramural Research Program of the National Institutes of
Health, National Library of Medicine.
Conflict of Interest: none declared.
database search programs. Nucleic Acids Res., 25, 3389–3402.
Austin,C.P. et al. (2004) NIH Molecular Libraries Initiative. Science, 306, 1138–1139.
Azzaoui,K. et al. (2007) Modeling promiscuity based on in vitro safety pharmacology
profiling data. Chem. Med. Chem., 2, 874–880.
Burbaum,J.J. and Sigal,N.H. (1997) New technologies for high-throughput screening.
Curr. Opin. Chem. Biol., 1, 72–78.
Chem. Biol., 4, 197–199.
Gribbon,P. and Sewing,A. (2005) High-throughput drug discovery: what can we expect
from HTS? Drug Discov. Today, 10, 17–22.
Hann,M.M. and Oprea,T.I. (2004) Pursuing the leadlikeness concept in pharmaceutical
research. Curr. Opin. Chem. Biol., 8, 255–263.
Lee,K.W. et al. (2007) Myricetin is a novel natural inhibitor of neoplastic cell
transformation and MEK1. Carcinogenesis, 28, 1918–1927.
Lu,J. et al. (2006) Inhibition of Mammalian thioredoxin reductase by some flavonoids:
implications for myricetin and quercetin anticancer activity. Cancer Res., 66,
Macarron,R. (2006) Critical review of the role of HTS in drug discovery. Drug Discov.
Today, 11, 277–279.
Marshall,G.R. (1987) Computer-aided drug design. Annu. Rev. Pharmacol. Toxicol., 27,
McGovern,S.L. et al. (2002)Acommon mechanism underlying promiscuous inhibitors
from virtual and high-throughput screening. J. Med. Chem., 45, 1712–1722.
Mestres,J. et al. (2008) Data completeness–the Achilles heel of drug-target networks.
Nat. Biotechnol., 26, 983–984.
Riggs,T.L. (2004) Research and development costs for drugs. Lancet, 363, 184.
Ryan,A.J. et al. (2003) Effect of detergent on ‘promiscuous’inhibitors. J. Med. Chem.,
Seidler,J. et al. (2003) Identification and prediction of promiscuous aggregating
inhibitors among known drugs. J. Med. Chem., 46, 4477–4486.
von Moltke,L.L. et al. (2004) Inhibition of human cytochromes P450 by components
of Ginkgo biloba. J. Pharm. Pharmacol., 56, 1039–1044.
Whitebread,S. et al. (2005) Keynote review: in vitro safety pharmacology profiling: an
Wu,D. et al. (2008) D-Alanine:D-alanine ligase as a new target for the flavonoids
quercetin and apigenin. Int. J. Antimicrob. Agents, 32, 421–426.
Zerhouni,E. (2003) Medicine. The NIH Roadmap. Science, 302, 63–72.