Table Of ContentChepelevetal.BMCBioinformatics2012,13:3
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RESEARCH ARTICLE Open Access
Self-organizing ontology of biochemically
relevant small molecules
Leonid L Chepelev1*, Janna Hastings2, Marcus Ennis2, Christoph Steinbeck2 and Michel Dumontier1,3,4
Abstract
Background: The advent of high-throughput experimentation in biochemistry has led to the generation of vast
amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing
techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-
activity relationship research, while providing new challenges, the biggest being the curation, annotation, and
classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources
currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new
useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently
existing chemical classification and annotation systems are not amenable to automated classification, formal and
transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further
limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the
automation of this process, especially for novel chemical entities of biological interest.
Results: To address this, we present a formal framework based on Semantic Web technologies for the automatic
design of chemical ontology which can be used for automated classification of novel entities. We demonstrate the
automatic self-assembly of a structure-based chemical ontology based on 60 MeSH and 40 ChEBI chemical classes.
This ontology is then used to classify 200 compounds with an accuracy of 92.7%. We extend these structure-based
classes with molecular feature information and demonstrate the utility of our framework for classification of
functionally relevant chemicals. Finally, we discuss an iterative approach that we envision for future biochemical
ontology development.
Conclusions: We conclude that the proposed methodology can ease the burden of chemical data annotators and
dramatically increase their productivity. We anticipate that the use of formal logic in our proposed framework will
make chemical classification criteria more transparent to humans and machines alike and will thus facilitate
predictive and integrative bioactivity model development.
Background biological functionality and chemical reactivity patterns,
Over the hundreds of years of biochemical research, thus providing indispensable assistance in understanding
humanity has encountered myriads of chemical entities the molecular nature of metabolism, toxicity, and bioac-
with countless combinations of functional groups that tivity [1,2]. In addition to this, the assignment of indivi-
imparted upon their bearers distinct reactivities and dual chemical entities to a given class within a chemical
properties. According to the structure-activity relation- ontology or hierarchy may facilitate the inference of the
ship (SAR) principle, grouping these entities into struc- potential anticipated roles and properties of these enti-
ture- and property-based classes within a larger ties. This capacity of chemical ontologies may help sup-
chemical ontology (formal logical specification of a che- port the development of the rising systems sciences,
mical hierarchy conceptualization) can enable the reca- such as chemogenomics and systems chemistry [3].
pitulation of or improvements in the prediction of their Despite the numerous efforts to develop automated
chemical classification and ontology construction
*Correspondence:[email protected] approaches (discussed below), this process has so far
1DepartmentofBiology,CarletonUniversity,Ottawa,Canada practically remained firmly within the hands of human
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curators, as exemplified by perhaps the largest chemical skeletons that result in a bioactive interaction, and that
ontology constructed to date - the Chemical Entities of given a sufficiently large dataset of compounds known
Biological Interest (ChEBI) database and ontology [4]. to be bioactive, it is possible to characterize a class of
As more chemical data becomes readily available from functionally active compounds as a collection of classes
large-scale experimentation, ChEBI and numerous simi- with well-defined, consistent structural features. Con-
lar repositories of biological and chemical information cerns for the predictive capacity of such classifications,
(e.g. [5-7]) are increasingly coming under strain due to like the distinction between stereoisomers and a range
lack of human curators, the overwhelming wealth and of chemical properties such as molecular volume that
diversity of the new chemical information, and the may result in disqualifying an otherwise well-suited 2D
boundlessness of the chemical space. Clearly, a means skeleton from a bioactivity-inducing interaction can be
to automate and facilitate these classification efforts is laid to rest by explicitly incorporating these features into
urgently needed if we desire to understand, expose, and class definition. In this work, we demonstrate that given
integrate the entirety of available chemical information. the logical expressivity afforded by the Web Ontology
Manual chemical classification efforts frequently Language (OWL) [10], we can automatically create for-
involve the tedious work of multiple human experts mal logical definitions for a range of topology and prop-
who carefully peruse the relevant chemical literature to erty-based chemical classes which can then self-
gain competence in assigning chemical entities to a assemble into a chemical ontology.
range of classes. Although the reliance of biochemical While manual literature searching and experimental
activity-based classification efforts on the predictions or data analysis is often involved in the classification of
informed estimates of human experts has been largely chemical entities into functional classes, structure-based
relegated to the informal or speculative domain of scien- hierarchy construction is much more well-defined and
tific communications thanks to the development of easily amenable to automation. In constructing struc-
numerous quantitative structure-activity relationship ture-based chemical classes, a modern curator would
(QSAR) elucidation approaches [8], chemical hierarchy familiarize themselves with any existing informal class
construction is still very much a human-dominated definitions, as well as any known chemical class mem-
domain. Chemical hierarchies in common use, such as bers. Based on this expertise, the curator would then
ChEBI and the Medical Subject Headings (MeSH) define a class, create a short textual or pictorial descrip-
organic chemistry hierarchy [9], are structure-based but tion of this class, and add child/parent relationships to
often have the capacity to incorporate chemical classes the other classes, as well as including several representa-
that are based on functional chemical attributes, such as tive entities. In essence, this approach requires the
those bearing a certain pharmaceutical activity (e.g. resulting ontology to consist of a formalisation of expert
ChEBI antibiotics). Although structure- and function- domain knowledge in the area, while providing no
based classification schemes involve somewhat different means for this knowledge to be captured in a machine-
approaches for construction, the basic accepted SAR understandable format. The result of this is a lack of
dogma is that chemical structure and chemical function facile re-use of the work of human curators in classifica-
are inseparably linked, as noted earlier. Therefore, struc- tion and annotation of novel entities and establishing
ture-based classification efforts could be useful in initiat- child/parent relationships for newly added classes when
ing the construction of predictive models of small the ontology grows or is modified. Unfortunately, the
molecule bioactivity. majority of chemical ontologies, such as ChEBI database
The correlation and overlap between chemical topol- and ontology, MeSH chemical classifications, and
ogy-based and biological function-based chemical hier- LIPIDMAPS [11], suffer from this, resulting in artificial
archies is expected since much of biological activity is discipline-specific barriers to research, since none of
enacted through interactions and transformations that these can be easily or automatically integrated. As the
are specific to a particular set of structural features of chemical ontologies grow in size and the range of rela-
small molecules. For instance, most enzymes operate on tionships covered, manual ontology development
a very limited set of chemical structures: carbonyl becomes increasingly difficult due to the multiplying
reductases, operate on carbonyl and alcohol functional number of consistency checks that have to be performed
groups while bacterial transpeptidases bind to and oper- for each new class and relationship added.
ate upon structures that resemble the peptide bond, To date, automated chemical classification has mostly
leading to the potency of beta lactam-based drugs in the belonged to the domain of QSAR studies which involve
inhibition of their operation. While there may be more the characterization of a training set of chemical entities
than one 2D chemical structure that results in an inter- with respect to a particular set of computable or mea-
action with a given enzyme (due to its 3D configura- surable molecular features, as well as the property of
tion), we claim that there is a finite set of 2D chemical interest, such as biochemical activity or biomedical
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potency [8]. The features that are used for training are applicability of the approach for characterizing some
often selected such that the cost of their acquisition is chemical classes (e.g. those requiring cardinality restric-
significantly lower than that of the modelled property. tions, such as dienes). Finally, the work of [18] has
This feature set is then used to construct a predictive resulted in a structure-based chemical ontology that
model using one of many available mathematical proce- employed combinations of chemical substructures of
dures, including the creation of decision trees [12], mul- increasing complexity to produce increasingly specia-
tidimensional parametric fits [13], or three-dimensional lized chemical classes, with the aim of improving the
pharmacophore models [14], to name a few. The resul- structural analysis and pattern identification in sets of
tant models are then verified for their capacity to ade- biologically active compounds.
quately classify molecules into a range of classes of Unfortunately, a framework or approach for fully
interest using a test set of compounds not used in automated construction of chemical ontologies has
model creation. While the resultant models may often never been achieved. In this work, we propose a radi-
achieve a great degree of accuracy, the vast majority of cally novel approach for structure-based chemical classi-
such models are not readily amenable to human under- fication automation by abstracting and automating the
standing or automated incorporation into a chemical majority of chemical curation to construct machine- and
hierarchy. In order to fully appreciate the structural and human-understandable, formally axiomatized chemical
physicochemical molecular features of relevance to a class definitions in OWL. Further, we use these automa-
particular functional or structural classification, post- tically generated chemical classes to enable machine rea-
processing of the resultant mathematical models is often soning over the ontology and demonstrate ontology self-
required. Although we have recently demonstrated the organization, as well as classification of individual che-
conversion of decision tree-based QSAR models into mical entities represented using the Chemical Entity
formally axiomatized OWL chemical ontologies [15], the Semantic Specification formalism [19]. Finally, we
problem of integration of disparate, problem-specific demonstrate that certain functionally-relevant classes
functional chemical classifications into a single chemical can be defined unambiguously using our approach, and
ontology is still largely unsolved. Compounding this integrated into the overall chemical ontology seamlessly.
problem is the fact that chemical class definitions
derived using statistical machine learning approaches Results
are often inherently probabilistic - that is, no class We have reduced the activity of a human curator to sev-
membership requirement is formally stated as a neces- eral steps, namely a) the identification of high-confi-
sary or sufficient condition. In order to derive a truly dence chemical class members, b) identification of
self-organizing chemical ontology, a deterministic classi- consensus chemical patterns that have to be present in
fication in terms of well-defined features and their com- a given compound to qualify for membership in a given
binations is necessary. class, c) creation of formal chemical class definition
While function-based chemical hierarchy construction axioms, and d) chemical classification and class relation-
has not been adequately addressed, methods to generate ship assignments based on the acquired expertise (Fig-
structure-based chemical ontologies have been studied ure 1). The ontology that we aim to generate with the
in the past to some extent. For example in [16], a very framework we have developed is primarily based on, but
early attempt at formalizing chemical classes and their not limited to, chemical structure. We operate on the
relationships for chemical inference has been carried premise that chemical class sub-specialization is enabled
out, but never practically implemented for any large- through chemical structure extension. We claim that for
scale classification exercise. In [17], a chemical ontology each class, a collection of functional groups exist that
has been manually constructed with classes formally are necessary for a molecule to be considered a member
defined as containing molecules that possessed a parti- of this class. This forms the basis for the self-organiza-
cular functional group or a set of functional groups. tion of our automatically generated chemical ontologies.
While this work has been successfully applied for practi-
cal chemical functionality analysis through chemical Automated Derivation of Formal Chemical Class
semantic similarity identification enrichment, the basic Definitions
ontology construction still followed a manual process Given a set of chemical entities known to be members
and was limited to a set of pre-defined functional of a given chemical class, our framework is capable of
groups, insufficient for characterizing a number of more abstracting and generalizing a set of consensus molecu-
complex chemical classes. Furthermore, the lack of a lar sub-graphs and features that are present in all repre-
formal, accessible framework for chemical classification sentative members of this class (see Methods). In
(in the form of OWL) has resulted in limited logical identifying such features, we consider not only the che-
chemical class axiom expressivity, therefore limiting the mical graph that is formed by the overlap of several
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Figure1Anabstractviewofthecurationeffortforstructure-basedchemicalhierarchies.
smallest class representatives, but also the chemical sub- can be written down as valid SMILES or SMARTS in
graphs formed by the fragmentation of class members, multiple ways. Canonicalization of consensus patterns
and atomic features, such as ring membership, aromati- ensures that each pattern has exactly one definition,
city, and connectivity. Therefore, we generate a set of thus allowing for facile integration and comparison of
both, canonicalized SMILES [20] and canonicalized consensus fingerprints from multiple class definition
SMARTS [21] patterns (see Methods) that are present generation procedures.
in a given molecule. Canonicalization of SMILES and The defining features, or chemical structural motifs,
SMARTS patterns is important because the fragmenta- that are present in every member of a given class are
tion of chemical structures and their generalized forms deemed to be necessary for class membership and are
often yields chemical patterns that are equivalent, but termed consensus chemical features and their collection
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may be viewed to constitute a consensus chemical fin- that the inclusion of the nitrogen-linked ester derivative
gerprint for a given chemical class. The consensus che- was erroneous. This was a very fortunate development,
mical features that contain but are not contained in since this identified one possible method to verify the
other consensus chemical features are termed by us consistency and correctness of human curators.
principal characteristic substructures. These substruc- Our second premise is that for one class to be consid-
tures can be used directly by curators for quick valida- ered a child class of another chemical class, it has to
tion of the automatically generated class definition. For contain all the consensus structural features of the par-
instance, for the ChEBI Peptide class (CHEBI:16670), ent class, and some additional features specific to it only
one would expect the principal characteristic substruc- (most likely, but not necessarily, principal characteristic
ture to constitute the amino acid backbone (Figure 2), substructures). Therefore, while the principal character-
which is indeed the case. istic substructures will be useful in establishing the
In the process of determining principal characteristic unique structural identity of a given class, the rest of
substructures, we have identified certain classes for consensus structural features are useful in establishing
which our algorithm could not converge on an expected the class hierarchy (Figure 3). This basic premise applies
substructure set. Surprisingly, this was observed for car- to both, simple consensus structural fingerprints, and
boxylic acid esters, where only the pattern associated the formal logical definitions of chemical classes and
with a carboxylic acid, but without extension to create forms the foundation of the self-organizing nature of
an ester was found. Upon closer investigation, we have the chemical ontology generated by our framework.
discovered that these difficulties in convergence were The next step in self-organizing chemical ontology
exclusively due to errors in the manual curation of generation is the formalization of chemical class defini-
input data. In the case of carboxylic acid esters, the fail- tions based on the consensus structural fingerprints. For
ure of convergence was due to the inclusion of a mole- this, we generate and maintain a single OWL ontology
cule containing RC(= O)ONR pattern instead of the RC that is constantly updated with the unique consensus
(= O)OCR pattern in the training set. Technically, car- chemical substructures that are identified as a result of
boxylic acid esters conform to a pattern RC(= O)OR’, class analysis, using previously reported principles of
where both, R and R’ are aryls or alkyls [22], meaning unique, canonical, and invariable URIs for each
Figure2Someoftheconsensusstructuralfeaturesidentifiedfortheclassofpeptidesusingtheproposedmethodology.Notethatin
thetwoconsensusfragmentsthatarenotcontainedinotherconsensusfragments,thebasicpeptidebackboneisconserved,evenforproline,a
highlyexoticaminoacid,since‘N’inSMARTSdoesnotnecessarilymeananNH3inpractice.
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an option. Consider, for example, the class of Glycols
(also sometimes referred to as Diols), which require the
presenceofaminimumoftwoalcoholfunctionalgroups
in a given molecule. Please note that we state that diols
contain at least two alcohol functional groups, since in
ourapproach,triolsshouldbeidentifiedassimplyasub-
class of diols. The cardinality restriction to at least two
alcoholfunctionalgroupsallowsforthis.Thus,diolscan
be defined as follows. Please also note thatcardinality is
not currently screened for by the framework proposed
here andall the cardinality restrictions in ourautomati-
callygeneratedontologieswereaddedmanually.
Diols
EquivalentTo
’Molecular entity’
and ‘has part’ some ‘[#6]’
and ‘has part’ some ‘[#8]’
and ‘has part’ some ‘[#6][#8]’
Figure3Establishingachemicalclasshierarchythroughdirect and ‘has part’ min 2 ‘[#6][OX2H]’
observationofconsensusstructuralfingerprintsissimilarto
Attributes and molecular descriptors can also be
logicalhierarchyconstruction.Itispossibletoinferparent/child
relationshipsbasedsolelyontheconsensusfeaturesofaclass. handled with such definitions. For instance, we may find
Formallogicaffordsmuchmorepowerfulexpressions. out that a given enzyme catalyzes the oxidation of
organic alcohols that are no heavier than 500 Daltons.
This mixing of chemical sub-graphs and physicochem-
functional group class [19]. The functional groups ical attributes is also possible, using the principles and
defined in this ontology are then used in converting the concepts defined in the CHEMINF ontology [24].
consensus structural fingerprints. For instance, a class of ’Reactive Diols’
‘Organic Alcohols’ is found to contain the consensus EquivalentTo
structural features expressed in SMARTS notation as ’Molecular entity’
[#6] (any carbon atom, aromatic or not), [#8] (any oxy- and ‘has part’ some ‘[#6]’
gen atom), [#6][#8] (carbon and oxygen in a single and ‘has part’ some ‘[#8]’
bond), and [#6][OX2H] (carbon connected to an OH and ‘has part’ some ‘[#6][#8]’
group). This can be translated into the following equiva- and ‘has part’ min 2 ‘[#6][OX2H]’
lent class statement in OWL using the Manchester DL and ‘has attribute’ some (’molecular weight’ and ‘has
query syntax [23], which states that an instance of the value’ [= < 500] and ‘has unit some ‘Dalton’)
class of organic alcohols is exactly defined by being an Using the dataset of diols annotated with their com-
instance of a molecular entity and having at least one putedmolecularweightwiththeconceptsfromtheCHE-
(keyword: some) instance of each of the four functional MINFontology,wewereabletocorrectlyidentifythatall
groups: 14 diols in the ChEBI training set for this class were
’Organic Alcohols’ indeed‘ReactiveDiols’asperthelogicaldefinitionabove.
EquivalentTo
’Molecular entity’ Self-Organizing Chemical Ontology
and ‘has part’ some ‘[#6]’ In order to test the integrative capacity and accuracy of
and ‘has part’ some ‘[#8]’ our chemical classification framework, we have manually
and ‘has part’ some ‘[#6][#8]’ generated a dataset containing 40 ChEBI and 60 MeSH
and ‘has part’ some ‘[#6][OX2H]’ chemical classes, for a total of 100 classes with at least 7
Clearly, this specification is human-readable, and can and at most 20 representatives in each chemical class.
be further enriched by adding annotations on the func- To demonstrate the self-organizing chemical ontology
tional groups involved, including graphical representa- creation from a specific source, we have created a
tions or textual descriptions for the various functional stand-alone self-organizing ontology for each, the ChEBI
groups, in order to facilitate chemical class definition and the MeSH data sets, containing the formal class
understanding. definitions derived from consensus chemical fingerprints
Further, if one wishes to handle cases where more of each class (Figure 4). Upon application of the Pellet
expressivelogicalstatementsarenecessary,thisisalways reasoner to classify these ontologies, we observed self-
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Figure 4 An example of a ‘flat’ chemical ontology with chemical class definitions, within the Protégé 4.1 ontology editor. No
classificationhasbeencarriedout.
organization of the formally axiomatized chemical inferences was carried out to ensure that the inferred
classes into chemical ontologies with a hierarchy (Fig- chemical hierarchy was consistent and coherent from
ures 5 and 6). Each child-parent inference was screened the viewpoint of chemical structure.
by a curator to identify that the automatically generated
ontologies were consistent for both datasets. In this Chemical Classification Accuracy
screening, we have considered the structural viewpoint - For a demonstration of chemical classification, we have
that is, it was permissible for ethylamines, for example, randomly selected a set of 200 chemical entities and
to be classified as the child class of methylamines, since employed the integrated ontology in order to classify
an ethylamine constitutes a structural extension of a each individual compound. Because each chemical
methylamine, and because the training sets obtained for entity could be classified into multiple classes, the
these classes (from MeSH in this case) included among number of class membership inferences (452) exceeded
methylamines chemical entities that were derivatized on the number of tested chemical entities. Within this
the carbon atom that should have otherwise been term- dataset, 91% of chemical entities received inferences
inal (a CH carbon). While we do not make any claims that exactly matched their annotation within the test
3
with respect to the validity of this approach, we note set and additional 8.5% were matched to classes con-
that this sub-classification inference in our work mirrors sistent with their test set-assigned class. For example,
the manually assigned sub-classification in the MeSH if a compound was a member of the alcohols in the
hierarchy of organic chemicals. test set, but actually contained two OH groups, this
Because both ontologies were built using one central compound was correctly identified as belonging to
functional group ontology and the CHEMINF ontology diols or glycols, without the explicit specification of its
for chemical descriptors, we were then able to trivially membership among the alcohols, even though this
import the ChEBI ontology into the MeSH ontology membership is implied.
within Protégé 4.1 [25] by specifying direct ChEBI ontol- Overall, 92.7% of the entity inferences were correct
ogy import within the MeSH ontology, and to apply the (Table 1). Most of the observed errors were in the
Pellet machine reasoner [26] to the resultant ontology classes that necessitated the definition of negations. For
to reconstitute a unified chemical ontology with 100 example, a carboxylic acid is a structural derivative of
chemical classes (Figure 7). Thus, for the first time, we an alcohol, but is strictly speaking not one. From a
were able to automatically and trivially integrate and re- structural perspective, classification of a carboxylic acid
use the manual curation efforts from multiple data as an alcohol is not incorrect. However, from a classical
sources in order to construct a single seamless chemical organic chemistry viewpoint, this is an incorrect classifi-
ontology. Manual analysis of the class relationship cation, and has been noted as such in this work. In
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Figure5MeSHchemicalclasshierarchyautomaticallyinferredusingthePelletreasoner.Pleasenotethatall‘is-a’relationshipsexcept
thoseto‘molecularentity’areinferredautomatically.Pleasenotethatallclassesdealwithorganicchemicals.
addition to this, we have observed that identifying incor- suggested manual refinements to definitions (e.g. glycols,
rect inferences in some cases led to facile identification diols, and alkadienes with cardinality restrictions). An
of adjustment of the training sets to automatically gen- additional table shows the classification results in more
erate a more accurate chemical entity description or detail (see Additional file 1).
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Figure6ChEBIchemicalclasshierarchyautomaticallyinferredusingthePelletreasoner.Pleasenotethatall‘is-a’relationshipsexcept
thoseto‘molecularentity’areinferredautomatically.Pleasenotethatallclassesdealwithorganicchemicals.
The entire classification process for 200 compounds chemical classes, and especially chemical classes of bio-
took 34 minutes and 10 seconds, running on a Core i7- logical relevance, we have created a stand-alone class of
870 machine with 6GB of RAM, executed serially, while chemical entities that are substrates to yeast alcohol
the computational time to classify non-populated ontol- dehydrogenase, using information in the BRENDA data-
ogies was negligible. base [27]. Because this data was experimentally derived
and the reactions indicated were confirmed to occur in
Seamless Integration of Novel Chemical Classes: Enzyme nature, no human curation (beyond eliminating redun-
Substrates dant entries) was necessary for the training set com-
In order to demonstrate the flexibility and facile amen- posed of 23 unique chemical entities. Upon the
ability of our ontology to the incorporation of novel repetition of the exercise described above with this
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Figure7ThechemicalclasshierarchyinferredforanontologythathasresultedfromtheautomatedintegrationoftheMeSHand
ChEBIontologies.Pleasenotethatallclassesdealwithorganicchemicals.
chemical class, we have, unsurprisingly, discovered that work of human curators, thus potentially improving
the resultant class of yeast alcohol dehydrogenase sub- their productivity and making the annotation and classi-
strates was a subclass of alcohols, with the added fication of the rapidly multiplying chemical information
requirement of possessing an oxygen atom connected to more manageable.
one hydrogen atom and a non-aromatic carbon atom. One may argue that curators may also easily define
chemical patterns in order to define a given chemical
Discussion class instead of starting with identifying a diverse set of
Here, we have presented a radically streamlined frame- compounds that would then be used to automatically
work and approach to chemical ontology construction derive the relevant patterns. While this is indeed true,
based on explicit formal semantic specification of che- automated consensus chemical structure identification
mical classes and rooted in chemical graph analysis. By has numerous benefits, including the exhaustive identifi-
eliminating the manual assertion of ancestry relation- cation of primitive structural features that are useful in
ships between the various chemical classes and instead constructing class hierarchies, as well as in the inher-
emphasizing the adequate curation of the limited (as ently objective identification of the structural patterns of
small as five entities) training set of the starting chemi- interest. Manual supplementation of class definitions is
cal class representatives and the automatically generated always possible, as we have shown with class customiza-
logical class definitions, we have greatly simplified the tion, and is sometimes required, in cases where our
