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90 3 DrugBank Online: A How-to Guide
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(a)
(b)
Figure 3.9 Using the DrugBank Online sequence search. This figure provides an overview
of using the DrugBank Online sequence searching tool. (a) The sequence input form, which
accepts one or more FASTA-formatted nucleic acid or protein sequences. Users can adjust a
subset of BLAST parameters using the entry fields and radio buttons. The filters allow users
to restrict the search to sequences associated with subsets of drugs based on approval
status and to specific types of sequences (target, enzyme, carrier, or transporter; see Section
3.2.2.6). (b) The first result displayed after searching using the human C-X-C chemokine
receptor type 5. Note the hit metrics in the top right and the BLAST output alignment
present below; exact matches are denoted by the one-letter code between sequences, while
similar residues are denoted with a plus symbol (“+”). The bottom table lists the drugs with
which the identified sequence has known interactions in DrugBank.
2. Adjust the BLAST parameters, if desired. Note that not all parameters available
in BLAST, such as the choice of substitution matrix, are available to change. The
“Expectation value” controls the cuto for returning hits; increasing this value
will result in more hits but many more will be only slightly similar to the target
sequence. The default gap opening cost is set at one (as opposed to the normal
BLAST default of 11); this may result in hits with more gaps than otherwise
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expected. For a full explanation of BLAST parameters, see the ocial manual
(https://www.ncbi.nlm.nih.gov/books/NBK279690/).
3. Adjust the “Drug Types” lter. Only sequences associated with drugs of the
selected type will be considered when searching. This is useful if, for example,
you wish to consider only approved drugs, whose protein binding and MoA are
more likely to be known in considerable detail. Alternatively, ltering to all but
approved drugs provides insight on scaolds currently under investigation.
4. Adjust the “Protein Types” lter. Setting this can narrow the search to the most
relevant type of sequence, given the starting query (see Section 3.2.2.6 for a full
explanation of each protein type).
5. Run the search (press the “Search” button).
Continuing the scenario from above, you run a search with your unknown sequence
using the default BLAST parameters, including approved, withdrawn, investigational, and experimental drugs, and limiting the protein types to targets. BLAST
returns 73 matches, the rst of which is shown in Figure 3.9b. Each hit contains
the name of the hit, together with the hit E value, bit score, and alignment length.
Briey, BLAST identies small local matches between query and target sequence,
which it attempts to extend in either direction while obeying set cuto parameters.
The longest such alignment is used to score the hit, and is also provided as part
of the hit itself; in the view here, the alignment is shown on a single line and may
be scrolled to the left or right if it does not fully t within the hit table. Lastly, all
relevant drug–protein interactions that t the protein types lter are included.
Inspecting the hits for the unknown sequence, it is clear that the top hits all belong
to the CXC and CC chemokine receptor families, with E values ranging from e
−33
e
. Indeed, the unknown query is the human C-X-C chemokine receptor type 5
−48
(UniProt ID P32302). Although the next hit, the type 1 angiotensin II receptor, has
a good E value (e
−27
), it represents a clear departure from the cluster of top hits and
will not be considered.
To get a sense of what kinds of chemical scaolds can eectively target CXC/CC
chemokine receptors, we can more closely investigate the hits. There are nine small
molecules in the top hits, which are listed as either antagonist or inhibitor and for
which a full structure complete with chemical classication (provided in the Chem-
ical Taxonomy eld of the Categories section in the relevant drug card) is present in
DrugBank (Figure 3.10a). Although some similarities are apparent across scaolds,
such as a generally extended conformation and the presence of phenyl groups and
amines, the scaolds appear diverse.
It is possible to conduct a rudimentary chemical similarity analysis using
data extracted directly from DrugBank. For all nine structures identied, the
“Substituents” provided as part of the chemical taxonomy were extracted and
used to produce a 9 × 94 matrix of one-hot encoded features. The most common
chemical features across all molecules (present in at least three drugs) are shown
in Figure 3.10b. Conrming the visual inspection of the compounds, the various
nitrogen-containing functional groups make up a large proportion of the results,
together with heteroaromatics, various oxygen-containing groups, and alkyl
uorides.
to
92 3 DrugBank Online: A How-to Guide
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Framycetin Plerixafor
AMD-070
MSX-122
(a)
8
7
6
5
4
Frequency
3
2
1
0
Amine
Hydrocarbon derivative
Organonitrogen compound
Organic nitrogen compound
Organoheterocyclic compound
(b)
Azacycle
Aralkylamine
Carboxylic acid derivative
Heteroaromatic compound
Aromatic heteropolycyclic compound
Vicriviroc
Tertiary amine
Carboxamide group
Amino acid or derivatives
Organic oxygen compound
Organic oxide
Tertiary aliphatic amine
Organooxygen compound
Organopnictogen compound
Aromatic heteromonocyclic compound
Alkyl halide
Alkyl fluoride
Azole
Dialkyl ether
Organofluoride
Carbonyl group
Organohalogen compound
Ketoprofen
Maraviroc
Drug Similarity
–3
–2
–1
0
1
Ether
Piperidine
2
3
Cenicriviroc
2
1
0
–1
–2
(c)
INCB-9471
2
1
0
–1
–2
3
AMD-070
Cenicriviroc
Framycetin
INCB-9471
Ketoprofen
MSX-122
Maraviroc
Plerixafor
Vicriviroc
Figure 3.10 A simple structural analysis of sequence search results.Thisfigureshows an
example of how searching for similar sequences to a putative target can help to inform
compound design. (a) Molecules identified within DrugBank to interact with top hits in a
resulting search (see text for more details). (b) A histogram showing the prevalence of
chemical features in the molecules identified in (A). For ease of visualization, features
present in two or fewer molecules are not included. (c) 3D visualization of the full
constituent feature matrix following PCA projection onto three components. It is clear that
there are two clusters, which may serve as starting points for further analysis.
Furthermore, by using principal component analysis to project the full chemical
feature matrix onto three components, it is possible to visualize the relationships between these drugs (Figure 3.10c). The resulting image reveals separation
between most drugs, though INCB-9471 and vicriviroc are similar, as anticipated based on their structures. There is a single larger cluster of cenicriviroc,
plerixafor, AMD-070, and MSX-122, which is not immediately apparent from
a visual inspection of their structures. Furthermore, all but cenicriviroc interact with the most similar hit in the original BLAST search, C-X-C chemokine
receptor type 4. This suggests that focusing on these structures, and similar ones
to them, might be a reasonable place to start when identifying a new bioactive
compound.
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Although small-scale and highly simplistic, this example provides insight into
how sequence searching may assist in the discovery of targets and the prioritization
of potential scaolds for downstream discovery work.
3.3.3 Extracting DrugBank Datasets for ML
Machine learning (ML) is increasingly applied across healthcare, from the analysis
of imaging data to a myriad of applications within drug discovery pipelines [10, 27].
Although model development in these relatively new and exciting areas remains an
important consideration, we argue that data quality is crucial, as health informatics represents a high-stakes domain [28]. Combined with the generally recognized
“unreasonable eectiveness of data” [29], assuming a data-centric approach [30] to
model training and continuous deployment practices may reap signicant benets
to organizations and patients alike.
Although we do not aim to provide a comprehensive overview of relevant ML
techniques here, we do highlight several ways in which public users may obtain
large, focused datasets for use in building and evaluating their models. These may be
used in model training, in validation of models trained on experimental or in-house
datasets, or in some combination of training and testing. The datasets discussed
below require a free account to access, which academic users may request using a
simple form (available at: https://go.drugbank.com/public_users/sign_up; account
requests require approval, which may take up to two business days to process).
The Advanced Search functionality discussed in Section 3.3.1.2 provides a powerful mechanism for querying and ltering the complete sets of drugs and targets
within DrugBank. The results of a search may be exported as a CSV le, by clicking
on the “Export” button at the top of the search results list (Figure 3.6b). By combining search ltering with a number of display eld selections it is possible to create
a focused custom dataset that can easily be loaded into an ML system or relational
database system as tabular data.
Whole datasets may also be accessed under the “Downloads” tab of the main menu
bar (or by navigating to https://go.drugbank.com/releases/latest). The “Complete
Database” provides a wealth of information for all current DrugBank drugs in XML
format with an associated schema. Relevant attributes for drug discovery include
drug approval status, structural information including classications, experimental and predicted properties, and detailed target and metabolism information; other
information is also provided, which may be useful depending on the desired use case.
Simplied scientic data extracts are available through the headings at the top of
the download page in SDF,CSV, and FASTA formats. These datasets have the advantage of being presorted into various categories, such as those based on the drug type
and approval status. In addition to their potential use in ML applications, these other
formats provide additional compatibility with existing software. Drug structures in
SDF format may serve as the basis for cheminformatic studies or for in silico structural work. Sequences in FASTA format are easily used in comparative methods to
nd similar sequences (e.g. Section 3.3.2.2) or to study target relationships using
phylogenetics.
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A key advantage of the DrugBank datasets is the combination of breadth, depth,
and accuracy, made possible by the combination of automated and curated data
intake within DrugBank (Sections 3.2.1 and 3.2.2.7, Figure 3.1). Studies have consistently demonstrated the importance of data completeness and accuracy in the
performance of a wide variety of ML models (e.g. see [31]). The highly structured
nature of these datasets allows users to easily experiment with the addition of new
features to their models, while our commitment to depth (completeness) and accuracy of data, and the inclusion of valid scientic references (Section 3.2.2.7) ensures
data quality and transparency (accuracy).
3.4 Research Using DrugBank
As the pace of data and studies being released continues to grow at a breakneck rate,
researchers are struggling with time-consuming work and an increasingly competitive environment. In order to stay ahead of the competition, many are turning to
DrugBank for reliable, high-quality data.
Recently, a large team of researchers from Wuhan, Beijing, and Shenzhen
developed a virtual screening tool using DrugBank’s database to help accelerate the
drug discovery process relating to COVID-19 [32]. The team used DrugBank to lter
out FDA-approved drugs as well as stage 3 clinical trial drugs. Several active sites
of viral proteins were then chosen to use as ligand targets for a screening process.
Compounds with high binding anities to these viral proteins were identied
through in silico molecular docking experiments. The results included a number
of drugs that were already being studied as treatments for COVID-19, but also
identied a number of new possible candidates. The list included drugs that are
used to treat HIV, HCV, cancer, and asthma, as well as inuenza virus antagonists.
Through in silico screening such as this, researchers can quickly identify candidate
treatments for emerging diseases such as COVID-19.
Turning to Sweden, a research team there has identied lead drug compounds
using DrugBank to screen against viral targets responsible for COVID-19 [33]. They
compiled lists of approved, investigational, and experimental drugs to screen against
four COVID-19 targets: 3C-like protease (3CLpro), papain-like protease (PLpro),
RNA-dependent RNA polymerase (RdRp), and the spike (S) protein. For this study,
structures of drugs identied as having high binding anities were retrieved from
DrugBank and were computationally docked to these four protein targets. The
compounds were validated through a double-scoring approach using molecular
dynamics and a molecular mechanics-generalized Born surface area (MM-GBSA)
strategy. They found drugs that were already under review in COVID-19 clinical
trials, conrming their methodology. To widen the pool of candidates, they also
screened for compounds that could potentially act on multiple targets. DrugBank
captures many dierent categorizations of drugs (such as approved, investigational,
and experimental; see Section 3.2.2.2) allowing the user to lter down to those most
important to their research. This is an optimal way to repurpose drugs given a vast
database and known targets; researchers can readily identify leading compounds to
accelerate the drug discovery process.
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Another instance where DrugBank helped to speedup drug repurposing strategies involved gene networking and bioinformatic analysis. Researchers from Taiwan
and Indonesia uncovered potential treatments for atopic dermatitis (AD) by integrating genetic and drug information using open data sources [34]. They gathered and
mapped drug target genes to DrugBank and used parameters to lter out potential
candidates based on pharmacological activity, approval status, as well as the presence of clinical and experimental drugs. After running the data, the results showed
dupilumab as an eective treatment for AD. As dupilumab is already approved for
this indication, this nding provided evidence for the accuracy of their methodology.
The researchers found 10 more potential candidates that had preclinical and clinical
trial evidence linking them through genetic interactions with AD.
Another important step in discovery and repurposing studies is the experimental
validation of predicted drugs, however, it is not always performed due to resource
constraints or a variety of other factors. In one illustrative example, researchers from
Argentina trained 1000 linear classiers on random subsets of independent variables (molecular descriptors) to discriminate between known active and inactive
inhibitors of the Plasmodium falciparum protease falcipain-2 [35]. Ensemble learning was used to improve the predictive power over individual models, which was
subsequently applied to the DrugBank and SWEETLEAD [36] databases to identify
putative falcipain-2 inhibitors based on their positive predictive value (PPV). Of the
157 hits, four were tested for in vitro activity against puried falcipain-2. Methacycline, a tetracycline antibiotic, and odanacatib, an abandoned cathepsin K inhibitor
investigated for use in osteoporosis, both inhibited the ability of falcipain-2 to cleave
the peptidic substrate Z-LR-AMC and inhibited P. falciparum growth in culture.
Interestingly, only odanacatib was able to inhibit proteolysis of the physiological
substrate hemoglobin, highlighting the nuance of drug MoA vs. therapeutic eect.
As highlighted in this section, the accessibility of DrugBank’s extensive database
can oer dierent solutions to accelerate the drug discovery pipeline. Researchers
are able to use it for in silico research by identifying potential drug candidates as
well as for repurposing molecules. Our vast range of interconnected information
creates an ideal environment for streamlined drug discovery and continues to be
a strong resource for researchers to use and validate drug prediction strategies.
These examples represent just a few use cases, where DrugBank provided reliable data to help nd treatments for a particular disease, some of which can be
emerging.
3.5 Discussion and Conclusions
The increasing importance of in silico methodology across healthcare, and specifically within the domain of drug discovery, has the potential to revolutionize the
manner in which we deliver care. To fully realize these benets it is necessary to
have complete, well-structured, and accurate data. In this chapter,we have discussed
the DrugBank database, highlighting several key datasets, providing workows to
accomplish common tasks, and discussed several research studies that demonstrate
the value of this data.
96 3 DrugBank Online: A How-to Guide
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The datasets discussed herein represent useful resources for drug discovery work,
but do not represent an exhaustive set of those within DrugBank. Clinical trial data,
as an example, can be used eectively to interrogate drug repurposing opportunities
and identify underserved areas within the scope of druggable targets. Though these
data are available as part of DrugBank Online (Table 3.1), expansion of this dataset
and the construction of tools to assist with its interrogation are current focus areas
for improvement. Similarly, although the pharmacology (and specically pharmacokinetic) information provided in drug cards (see Section 3.2.2.3), is exceptionally
detailed, it is largely in the form of unstructured text. Future eorts to create structured entries from this dataset may assist in the use of these parameters as input to
various algorithms and ML models.
As mentioned in the introduction section, drug target identication is often
conducted through the use of genetic associations, or is strengthened by such
ndings [37]. In general, the integration of genetic information in clinical diagnosis and care, though challenging, remains a source of great interest [38, 39].
The association of genomic changes with an alteration in the safety, ecacy, or
other properties of a drug with respect to the individual is usually referred to as
pharmacogenomics/pharmacogenetics (PGx) [40]. Although the utility of PGx data
in a clinical setting has been demonstrated, the exploration of its use in other elds,
such as drug discovery [41], remains to be fully evaluated. In keeping with this
exciting potential, DrugBank will be investing in expanding our PGx dataset to
empower new discoveries in the area of genomic medicine (see Section 3.2.2.3).
The importance of evolutionary context in drug discovery is largely limited to the
use of homology searching and phylogenetic methods to ensure orthologues of putative targets are present within an animal model of choice [42]. With the recent breakthrough in in silico protein structural prediction [43, 44], increasing power of in silico
structural analysis methods, and a rm emphasis on validated drug–target interactions, it is likely that this conversation will expand from a purely sequence-focused
view to include important structural elements. Assisting users with analytic workows centered around sequence and structural homology represents a fascinating
possibility for future work.
Critically, though there are numerous avenues currently under development to
expand DrugBank’s oering, our existing data and infrastructure already represent
an invaluable resource for the drug discovery community. Although commercial
licensing is available, we provide many important datasets for drug discovery free
of charge; by making these data freely available to researchers, we aim to empower
health informatics research and democratize the research process such that an individual’s ability to discover novel insights is not tied to resourcing. Future eorts to
expand on these data and tools will continue this motivation, and ensure the continued success of health informatics research.
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