Briefings in Bioinformatics, 17(2), 2016, 309–321
doi: 10.1093/bib/bbv042
Advance Access Publication Date: 7 July 2015
Paper

Ahmed Mohamed, Canh Hao Nguyen and Hiroshi Mamitsuka
Corresponding author: Ahmed Mohamed, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan. Tel.: þ81-774-38-3023;
Fax.: þ81-774-38-3037. E-mail: mohamed@kuicr.kyoto-u.ac.jp

Abstract
Research in natural products has always enhanced drug discovery by providing new and unique chemical compounds.
However, recently, drug discovery from natural products is slowed down by the increasing chance of re-isolating known
compounds. Rapid identification of previously isolated compounds in an automated manner, called dereplication, steers
researchers toward novel findings, thereby reducing the time and effort for identifying new drug leads. Dereplication identifies compounds by comparing processed experimental data with those of known compounds, and so, diverse computational resources such as databases and tools to process and compare compound data are necessary. Automating the dereplication process through the integration of computational resources has always been an aspired goal of natural productresearchers. To increase the utilization of current computational resources for natural products, we first provide an overview of the dereplication process, and then list useful resources, categorizing into databases, methods and software tools
and further explaining them from a dereplication perspective. Finally, we discuss the current challenges to automating dereplication and proposed solutions.
Key words: natural products; dereplication; NMR; compound identification

Introduction
Natural products have been a precious resource for drug discovery and lead identification [1–3]. In all, 75% of all Food and Drug
Administration-approved small molecules are either naturalcompounds or derivatives therefrom [4]. The potential of natural products in drug discovery can be attributed to their uniquestructural scaffolds and high complexity, creating diverse biological screening libraries [5]. Besides being attractive drug leads,
the complexity of natural products and high content of stereogenic atoms increase protein binding selectivity [6], allowing
natural products to be used in ligand design, particularly fragment-based drug design [7].
Despite the potential of natural products, there are two main
factors that limit their role in recent drug discovery and lead
identification research: i) time-consuming identification ofactive compounds: The general manner of experimental designfor identifying natural products remained unchanged throughout the past decades. That is, it requires time-consuming purification and inefficient manual interpretation of compound
nuclear magnetic resonance (NMR) spectra by experts.
ii) Repetitive effort for identifying known compounds. While itis estimated that more than 250,000 natural compounds havealready been isolated [8, 9], incorporation of such knowledge toenhance drug discovery is still not fully exploited.
To overcome these two factors, one promising approach isdereplication, which is the early identification of known
compounds without time-consuming manual structure elucidation [10, 11]. Putative compounds are obtained by comparing
preliminary spectral data to spectral databases of known
compounds (This review mainly focuses on NMR spectra, while
methods, software and databases of NMR spectra can be applied

Ahmed Mohamed obtained his MSc in natural products chemistry and is currently a PhD student at the Bioinformatics Center in Kyoto University. His current research focuses on developing software applications for biology and chemistry.
Canh Hao Nguyen is an assistant professor at the Bioinformatics Center, Institute for Chemical Research, Kyoto University, working on machine learning
for graph data, with applications to biological networks.
Hiroshi Mamitsuka is a professor at the Bioinformatics Center, Institute for Chemical Research, Kyoto University, working on machine learning, data mining and application to biology and chemistry.
Submitted: 27 April 2015; Received (in revised form): 27 May 2015
C The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com
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Current status and prospects of computational
resources for natural product dereplication: a review

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Overview of natural products compound
identification
Figure 1 shows compound identification in natural products
without and with dereplication. The standard experimental design for natural product identification starts with purification of
bioactive compounds using bioassay-guided fractionation fromnatural extracts (Figure 1IA, IB). Measured full spectral data of
the purified compounds are manually interpreted for deducing
the compound structure (Figure 1IC, ID), which is then used forliterature inquiry (Figure 1IE). With the increasing chance of isolating known compounds, the time and cost are becoming unacceptable. Dereplication utilizes prior knowledge of previously
isolated compounds for early identification to minimize humanintervention. Ideally, preliminary experimental data, such assource organism, bioactivity and measured spectra, are used tofilter compounds that are either previously reported or lacking
drug-like characteristics.
For researchers to integrate dereplication in their experimental design, they need a full software suite for automatic
NMR processing and analysis that is linked to a reference database for dereplication. The reference database should provide awide coverage of previously isolated natural compounds with
their source organisms and reported/predicted bioactivities. A
database query should be carried out with a sophisticatedmethod for compound matching integrating different types ofspectral information.
Three components are needed to develop a complete dereplication software: i) databases to act as reference libraries, ii)
spectral processing and searching methods to query databases
and iii) software tools for spectral preprocessing and analysis.
We discuss each component, identifying available resources
and their current shortcomings where further research isneeded. In the next section, we introduce available databases,
discussing their coverage, deposited data and relevant query
options. In the fourth section, we describe different methods aswell as software tools for spectral preprocessing and compound
identification.

Databases
The integration of chemoinformtics modeling in drug design
motivated the development of numerous databases listingchemical compounds with their biological and physical properties. Databases relevant to natural products are already reviewed [30–32], while we discuss them here from adereplication perspective. We divide available databases into
general and natural product-specific databases (Tables 2 and 3,
respectively), and score each database with seven criteria thatare important for dereplication: (1) coverage of known naturalcompounds, (2) availability of bioactivity data, (3) availability of

Table 1. Differences between compound identification in natural products research and metabolomics
Comparison criterion

Natural products research

Metabolomics

Reference library size
Quality of reference spectra
Types of spectra
Structural complexity
Sample purity
Spectral comparison
Overall goal

Large (>250,000) [8]
Low [20]
Both proton and carbon-based [22, 23]Complex [25, 26]
Purified or semi-purified compounds [20]Pairwise
Compound identification

Small (few 1,000 s) [21]High [20]Mainly proton-based [24]SimpleComplex biological fluid mixtures [20, 24]
Pairwise or multiple (time-series)
Compound identification and quantification

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to other types of spectra, such as mass spectrometry [MS]).
Early detection of known compounds and their reported and potential biological activities help researchers to focus their efforts
toward novel findings [12]. While the idea of dereplication isdecades old [13], it has gained more attention recently with theincreased sensitivity in analytical instruments [11], whichallows structure elucidation at nanomole scales [14–16]. In addition, coupling of ultrasensitive instrument such as capillary
NMR and high-resolution MS with chromatography allows preisolation compound identification [17–19], which significantly
reduces time and effort.
Despite instrumental advances that are useful for compound
identification, computational tools for dereplication are still at adeveloping stage. Fortunately, natural products and metabolomics share common compound identification techniques,
and they are said to be ‘two sides of the same coin’ [20]. Focusing
on detecting dynamic metabolite changes in biological fluids, research in metabolomics spurred simultaneous development of
accurate computational methods for fast and high-throughput
identification of compounds from complex biological mixtures.
However, the small but significant differences between natural
products and metabolomics prevent the direct cross-utilization ofcomputational resources.
Table 1 shows the differences between compound identification in natural products and metabolomics. From data perspectives, there are particularly three key differences: (1) Natural
products reference libraries are larger in size than those of
metabolomics, increasing the computational demand to searchthrough these libraries, and the lower quality of spectral data
poses concern on the reliability of results. (2) Compound identification in metabolomics relies on ‘landmark’ peak detection[27], often obtainable from proton-based NMR spectra such as
proton nuclear magnetic resonance (1H) and total correlation
spectroscopy [28, 29]. However, owing to structural diversity
and spectral complexity of natural products, the identificationof natural products often requires inclusion of carbon-based
NMR measurements, such as carbon-13 nuclear magnetic resonance (13C) and heteronuclear single quantum coherence spectra [20, 22, 23]. (3) Metabolomics samples are complex biological
mixtures where the goal is to both identify and quantify metabolites. However, quantitative analysis of mixtures is not the current focus of dereplication.
We review the current status of computational resources thatare or could be used as building blocks to automate dereplicationand how they can fit in the current experimental design. We discuss the overlaps and differences in computational demands ofdereplication and compound identification in metabolomics. We
start by a brief overview of the experimental design of dereplication, followed by detailed discussion on three computational aspects of dereplication: databases, methods and software. We
finally conclude with future perspectives.

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Table 2. General chemical databases
Database

Web site (http://)BindingDB [33]
ChEBI [34]ChemBank [35]Chembl [36]ChemIDplus

www.bindingdb.org
www.ebi.ac.uk/chebi/
chembank.broadinstitute.org
www.ebi.ac.uk/chembl/
chem.sis.nlm.nih.gov/
chemidplus/
ChemSpider [37] www.chemspider.com
CSEARCH [38]nmrpredict.orc.univie.ac.at/
NCI
cactus.nci.nih.gov/ncidb2.2/
NIAID ChemDB chemdb.niaid.nih.gov
NMRShiftDB [39] nmrshiftdb.nmr.uni-koeln.de
PubChem [40]pubchem.ncbi.nlm.nih.gov
Reaxys [41]www.reaxys.com/reaxys
SciFinder
scifinder.cas.org
SpecInfo [42]www.wiley-vch.de/stmdata/
specinfo.php
ZINC [43]zinc.docking.org

Coverage

Data content# NPs# Compounds Bioactivity(type)

NA
>25 k
NA
24K
>9 k

>450 k>42 k>800 k>600 k>400 k>660 K >14 M
NA>450 k
NA>250 k>9 k>130 k
NA
>42 k
NA
>30 M
>200 k >10 M
NA
>90 M
3.5 k
>500 kSourceorganism

(protein binding)(all)(all)(all)(all)

Spectral
Programmatic Free?
Score
searchability access
Use Download(all)

















5
5
5
5
2










5
3
5
2







4
5
4
3
1





3



(allergy, infectious
diseases)(all)(all)(all)











>180 k >20 M#NPs: Number of natural product compounds.

Table 3. Natural products-specific databases
Database

Web site (http://)Coverage

Data content# Compounds Bioactivity

AntiBase [44]www.wiley-vch.de/stmdata/>40 k
antibase.php
BACTIBASE [45] bactibase.pfba-lab-tun.org
220
CamMedNP [46] NA
2.5 k
ConMedNP [47] NA
3.2 k
Dictionary of
dmnp.chemnetbase.com>30 kmarine NP
Dictionary of NP dnp.chemnetbase.com>250 kHeteroCycles
www.heterocycles.jp/newlibrary/ >58 k
natural_products/structure
Marinlit
pubs.rsc.org/marinlit/>24 k
NAPROC-13 [48] c13.usal.es>20 k
NPACT [49]crdd.osdd.net/raghava/npact/
1574
640
NuBBE [50]nubbe.iq.unesp.br/portal/
nubbedb.html
PhytAMP [51]phytamp.pfba-lab-tun.org
273
SuperNaturalbioinformatics.charite.de/>350 k[52, 53]
supernaturalTCM
tcm.cmu.edu.tw>20 k
database [54]UDNP [55]pkuxxj.pku.edu.cn/UNPD
230 k

: Limited data.

Sourceorganism

(all)(all)

(all)

(anti-microbial)(all)(all)

(anti-cancer)
(anti-microbial)Spectral
Programmatic Free?
Score
searchability access
Use Download

4













4
3
3
3



3
4





4
3
2
4



(anti-microbial)(all)








4
3

(traditional Chinese medicine)







5







4

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Figure 1. Compound identification in natural products without and with dereplication.

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General databases
We include 15 chemical databases as general databases according to the following criteria: (i) Cover more than 10% of alreadyisolated natural products; around 20,000 compounds. (ii)Contain at least 40,000 entries including both synthetic and natural compounds. (iii) Contain information useful in dereplication, such as bioactivity, source organism or spectra (Table 2).
Regarding coverage, five databases contain more than 10
million entries. General databases provide wide coverage of natural compounds, with 11 databases containing more than
20,000 natural compounds (roughly 10% of already isolated
compounds). Despite their wide coverage, searching is not easy
to use for dereplication because synthetic compounds areamong search candidates. Seven databases have natural compounds annotation, which allows users to limit their search tonatural products only.
Dereplication-relevant data-contents are two: bioactivity
and source organism. Eleven databases include biological activity. PubChem [40], ChEBML [36] and BindingDB [33] databases
contain detailed bioactivity information such as biologicalmechanism and protein targets, which can be used, in conjunction with spectral information, to enhance compound identification [56]. Regarding source organism, only two databases,
ChEBI [34] and Reaxys [41], contain this information.
While spectral searchability is important in dereplication,
searching compounds by spectral data is not the focus of general databases, and only NMRShiftDB [39], CSEARCH [38] andSpecInfo [42] have this ability. Compounds in all 15 general
databases are searchable by similarity of structures or substructures; however, this search has strong limitations for dereplication, where molecular structures are unknown.
There are three ways to access general databases: (1) manual
access, (2) access via database download or (3) programmaticaccess. Twelve databases can be accessed manually for free andnine of them are freely downloadable. Ten databases provide
APIs to access the data though programs, which enable theirintegration to user-customized analysis flows. However, programmatic access has limitations for dereplication because
either necessary data or query options are lacking.

Natural products-specific databases
We raise 15 databases that catalog molecules isolated from natural origins only, excluding those limited to primary metabolites, as those are relevant only to metabolomics (Table 3). In
terms of coverage, nine specific databases exceed 20,000 entries.
Because of the coverage limitation, it is better to use multiple
specific databases for reliable dereplication. Some specific databases have limited coverage because they focus on: i) particularcompound features such as compound class (PhytAMP [51] andBACTIBASE [45]) or bioactivity (NPACT [49]), and ii) particularcompound origins such as compounds from a particular familyof source organisms (CamMedNP[46] and ConMedNP [47]) or
geographic location (NuBBE [50] and TCM [54]).

Despite their limited coverage, specific databases containbioactivity and source organism information, useful in dereplication. Eleven specific databases contain bioactivity data.
Typical examples are NuBBE [50] and NPACT [49], which provide
effective compound concentrations of different bioactivities foreach entry. All specific databases have source organism information, except for SuperNatural [52, 53], NPACT [49] and
NAPROC-13 [48].
Spectral searchability is limited in specific databases
because of the scarcity of spectral data. Only three databases
have spectral searchability, and only one database, NAPROC-13[48], is freely accessible but limited to 13C spectra only.
Regarding database access, 11 specific databases can be
manually searched, seven of which are freely downloadable.
Specific databases are usually in-house developed and all ofthem do not provide programmatic access to the data, limiting
automatic search and integration to other software.

Methods and softwareThis section describes computational methods and softwaretools used as parts of natural product dereplication process.
Table 4 summarizes two main steps of dereplication: spectral
preprocessing, and compound identification. First, spectral preprocessing involves reformatting and denoising of the acquired
spectra to alleviate the instrumental and experimental discrepancies [27, 57]. Second, compound identification uses preprocessed spectra and compares them to a reference database. To
realize automatic and fast dereplication, each step needs to be
carried out efficiently with minimal human intervention. Table
5 lists, to the best of our knowledge, currently available softwaretools for these steps, comparing the tools according tofunctionalities.
Note that while we focus here on software for spectral preprocessing and compound identification, natural product dereplication needs additional tools to manage and visualize
chemical structures and spectra. For example, structures ofchemical compounds are usually represented as SDF or MOL
files, and software tools, such as Open Babel toolbox [69] andChemmineR [70], rcdk [71] and Rcpi [72] R packages, are needed
to handle these files and pass the data to the dereplication software for processing or visualization. For result visualization,
Java and javascript libraries, such as JSpecView [73], JSME [74]and MarvinJS [75], can offer in-browser chemical structure andspectral visualization for Web applications.

Spectral preprocessing
We categorize preprocessing methods into three main steps:
file format conversion, baseline correction and alignment. We
first discuss each step, and demonstrate baseline correction
and alignment on example spectra (1H NMR spectrum of stigmasterol in Figure 2). We finally summarize available softwaretools.
File format conversion
While the acquired spectra are initially stored as proprietarydata formats that are specific to each instrument, convertingthem to a common instrument-independent format ensureseasier data exchange and wider compatibility. For NMR spectra,
JCAMP-DX [76], NMRPipe [65] and Sparky [77] are among themost used file formats for describing spectral information ofsmall molecules. JCAMP-DX [76] provides a simple and humanreadable format, and allows additional labels to describe

Downloaded from https://academic.oup.com/bib/article-abstract/17/2/309/1742712 by KYOTO UNIVERSITY Igaku Toshokan user on 17 June 2019source organism data, (4) searchability over compounds bymeasured compound spectra, (5) programmatic access throughWeb services or application programming interfaces (APIs), (6)free availability to use and (7) free availability to download.
Tables 2 and 3 demonstrate that no available databases satisfy
all seven criteria for an ideal dereplication database. Below, we
discuss these databases in terms of coverage, data content,
spectral searchability and access.

Computational resources for natural product dereplication

Table 4. Analysis flow of spectra from acquisition to compound
identification
SpectralFile format
preprocessing
conversion

experimental conditions and parameters. However, representation of multi-dimensional NMR spectra in JCAMP-DX is notstandardized. NMRPipe [65] and Sparky [77] have been used in
Web applications [78, 79] for their strong standardization andthe ability to represent multi-dimensional NMR spectra.
Current NMR file formats mainly have the following threelimitations for dereplication. First, current file formats do notcontain structures of measured compounds, which prevent assigning spectral peaks to corresponding atoms. We have to include additional files for compound structure and peakassignment information [80, 81], which cannot be linked easily
with spectral files. Second, one-dimensional (1D) NMR and twodimensional (2D) NMR spectral data of the same sample cannot
be linked with each other in current file formats. Third, current
file formats are still insufficient to fully represent measurements and experimental parameters in high-throughput studies [80]. CCPN [82, 83] and STAR [84–86] provide different
formats that can be used for high-throughput studies, but are
tailored for protein NMR experiments. A suitable file format for

313

natural product dereplication is still needed to overcome theabove three limitations.

Baseline correction
Removal of baseline drifting is crucial to remove noise andartifacts resulting from different measurement conditions.
Generally, baseline correction has three steps, baseline recognition, modeling and subtraction. First, baseline recognition
distinguishes peak regions from baseline points, exploiting
the fact that peak regions have higher variation in intensity.
Higher variation regions are detected using spectrum derivatives [87] or wavelet transformation [88–91]. Second, baselinemodeling estimates a curve based on baseline points, by linear
interpolation or non-linear approximations like polynomial
fitting [92, 93], LOcally Weighted Scatterplot Smoothing(LOWESS) and quantile regressions [94–98] and Whittakersmoother [88, 99]. Finally, in baseline subtraction, the estimatedbaseline curve is subtracted from the spectrum, leaving only thepeak signals.
In natural product dereplication, baseline correction is aminor step compared to metabolomics because of two main differences: (i) As dereplication currently focuses on compound
identification rather than quantification, accurate baseline estimation is less significant [20]. (ii) Dereplication is usually performed on purified compounds where spectra are less crowded
than those of biological mixtures. Therefore, simple polynomial
fitting is usually preferred for baseline correction, instead of
more computationally demanding techniques such as LOWESS
and quantile regressions and Whittaker smoother. In our example, the baseline is estimated as a third-order polynomial
function (Figure 2).

Alignment
Alignment of spectra is a process to alleviate the effect of experimental conditions on peak positions by shifting data points
to match a reference spectrum [24, 57]. Spectral alignment andrelevant software tools are already reviewed in detail [57], andso we only describe alignment here briefly. Alignment isperformed for quantitative comparison between multiple spectra of different samples that have similar chemical compositions, and therefore, it is a standard manner for time-series
NMR spectra in metabolomics. Using the same concept, alignment can be applied in dereplication when spectra for different
fractions of the same extract are compared [100]. Figure 2 shows
how alignment removes subtle chemical shift differences in the
spectra of two structurally similar compounds, cholesterol andstigmasterol, increasing the overall similarity between the two
spectra.

Software summary for spectral preprocessing
Table 5 shows that out of 16 currently available software, 3
steps in spectral preprocessing, i.e. file format conversion, baseline correction and alignment, are implemented in 12, 13 and 9
software tools, respectively, meaning that baseline correction is
the most implemented. Six software tools, ACD Labs, Automics[58], Chenomx NMR suite, MestreNova, MVAPACK [62] andPERCH, implement all three steps, of which Automics and
MVAPACK are freely available, making them most useful forspectral preprocessing. Six other tools implement two steps,
and the remaining four tools (all are R packages) specialize in
only one step.

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 JCAMP-DX NMRPipe SparkyBaseline correction 1. Baseline recognition Derivative functions
 Wavelet-based
2. Baseline modeling Polynomial
 Regression
 Smoothing
3. Baseline subtraction
Alignment
 FFT alignment Multiple-dimension
CompoundData reductionv Peak lists
identification
 Peak picking
 Numerical vectors
 Binning Feature extraction
 Sliding window
 PCA
 TreesSpectral comparison v Peak lists
 Tanimoto coefficient Jaccard similarity
 Numerical vectors
 Correlation-based Dot product Pearson’s correlation
 Spearman’s correlation
 Weighted
cross-correlation
 Partial and semi-partial
correlation
 Distance-based Absolute value distance Euclidean distance Trees Tree-based comparison
Database search Identity search Ranking search Interpretative search

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Table 5. Software tools with a potential role in dereplication
Software

Software type

Spectra type

GUI Spectral preprocessing

Compound identification

Free? Score

Baseline
Alignment Peak
Binning Feature
File
Correction
Picking
Extraction
FormatConversion
Desktop
Desktop
R package
R package
DesktopNMR (1D, 2D), MS
NMR
NMR
Any
NMR (1D, 2D)

















Desktop
Desktop
R package
Octave package
Web
Python package
Desktop
R package
Desktop
R package
R package

NMR
NMR (1D, 2D), MSAny
NMR (1D, 2D)
NMR, MSNMR (1D, 2D)
NMR
NMR
NMR (1D, 2D)
NMR (2D)
NMR





















































































4
7
4
3
5
4
4
2
7
4
4
5
2
4
5
2

GUI: Graphical User Interface.

Figure 2. Spectral preprocessing. 1H NMR spectra of cholesterol and stigmasterol, two common and structurally similar natural compounds, are used for demonstration. The raw NMR files were downloaded from HMDB [21] and converted to JCAMP-format DX using Mestrenova. Baseline estimation was performed in R using thirdorder polynomial fitting. The baseline-corrected spectra were stacked, and then aligned using Mestrenova, showing higher similarity (Pearson’s correlation of 0.423)
than before alignment (0.288). A colour version of this figure is available at BIB online: http://bib.oxfordjournals.org.

Compound identification
For compound identification, preprocessed spectra are converted into different representations to be compared againstreference spectra in a computationally efficient manner, to findcompounds with the highest spectral similarity. To carry out
compound identification, three steps are required: data reduction, spectral comparison and searching databases. We explain
each of these three below.

Data reduction
Spectral comparison of raw spectra needs long computation
time because each spectrum has a large number of data points(more than 20,000 points for 1H NMR [24]), where each point has

a position (chemical shift) and an intensity. To reduce computation time, we need methods to reduce data size without substantial loss of information. Data reduction transforms spectral
data into peak lists, numerical vectors or trees. We describe thecharacteristics of each of these three representations.
Peak lists: Spectra are reduced to peak lists by peak picking[101–103], which greatly simplifies the spectra to a handful ofpeak positions and their intensities. Limitations of peak picking
arise if the spectrum contains broad or overlapped peaks, suchas crowded 1H NMR spectra, in which important peaks can be
missed.
Numerical vectors: Spectra can be reduced to numerical vectors of the same size by binning, sliding window or principal
component analysis. First, binning [104–106] divides the spectrum into intervals and the total intensity in each interval is

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ACD Labs
Automics [58]BATMAN [59]ChemoSpec [60]Chenomx
NMR suite
cuteNMR
MestreNova
mSPA [61]
MVAPACK [62]mylims.org [63]Nmrglue [64]Nmrpipe [65]
NMRS [66]PERCH
rnmr [67]speaq [68]Computational resources for natural product dereplication

Spectral comparison
Spectra are compared using a similarity measure that reflects
the structure similarity of the corresponding compounds. The
choice of the similarity measure depends on the data representation, determined by the data reduction method (described in
the previous section). We discuss available similarity measuresfor each representation.
Peak lists: When spectra are reduced to peak lists, which areof different sizes, they are represented as sets. Comparing two
sets of peaks requires two steps: (1) Matching of set members,
to produce one-to-one mappings between peaks of the queryand reference sets. First, a list of matching candidates for eachpeak is narrowed to peaks whose positions lie within a defined
threshold. A threshold can be either a fixed window (hardthresholding), which is chosen manually, or defined statisticallyusing Bayesian [111, 112] and probability-based [113, 114] models (soft thresholding), which are more flexible. Second, matching peaks are chosen from the candidate list by either i)selecting the nearest peak, or ii) maximum bipartite matching[115], which maximizes the number of pairs between peaks ofthe two sets. (2) Measuring the overlap between two sets, whichis computed by set similarity measures, typically Jaccard’s similarity and Tanimoto’s coefficient [116–118].
Numerical vectors: Numerical vectors have the same dimension and a typical way to compare them uses a correlation
or distance-based similarity measure such as inner product[119–121], Euclidean distance [122–124] or difference in absolutevalue [125, 126]. Among the three measures, inner product wasreported to outperform the other two measures [127]. Measures
combining both correlation and distance-based similarities,
such as partial correlation [128] and composite similarity measures [129], have been shown to perform better than a single
measure [130, 131].
Trees: Trees are compared by taking into account both peak
positions (node position) and their hierarchy (children nodes)[109, 110].
Computational efficiency: Applying spectral comparison tolarge databases requires efficient computation of similarity

315

scores. The speed of computing similarity scores for each representation is affected by two factors: (1) number of data points inspectral representation and (2) computational complexity ofcomparing two spectra. First, number of data points varies
between spectra owing to different spectral features or data
reduction parameters. The number of data points in peak lists
and trees depends on the number of peaks, while in numerical
vectors, it is equal to the number of bins. The typical size of natural product compound spectra is tens for peak lists, and 250 for
numerical vectors (chemical shift range: 010 ppm, bin size:
0.04 ppm). Second, the computational complexity is determinedby the number of computational operations for comparing two
spectra of N data points, which is in the order of N2 for peaklists, and N and N logN for numerical vectors and trees, respectively. Theoretically, for a similar number of data points, numerical vectors are the fastest to compare, followed by trees andpeak lists.

Searching databases
Three database search paradigms are useful in dereplication: (1)identity, (2) ranking and (3) interpretative; each search paradigm
produces a different output format [127, 132]. We explain eachparadigm below.
Identity search: Identity search returns a single compoundwith a spectrum that is equivalent to the query spectrum [111,127]. Identity search requires no manual investigation and so it
can be very useful in automating dereplication. However, identity search has two limitations: (1) Searching small-coveragedatabases may return empty results, if the exact spectrum is
not in the database. (2) Setting strict equivalence criteria may
miss spectra that are affected by variations in experimental
conditions or inadequate preprocessing.
Ranking search: Ranking search returns a ranked list of compounds with spectra closest to that of the query by computing
similarity scores the query spectrum and all spectra in the database [125]. By investigating common substructures of highly
similar compounds in the list, we can deduce chemical class orfunctional groups of the query compound. Similarity scores canalso be computed using a subset of the query spectrum, allowing users to focus on distinctive peaks. One limitation for ranking search is that deducing chemical classes and functionalgroups still requires manual investigation, which hampers
automatic dereplication.
Interpretative search: Interpretative search returns a list ofmatching fragments by assigning peaks from the query spectrum to connected fragments of reference compounds [115,133]. The output fragments, which belong to different referencecompounds, can then be combined to deduce the query compound structure and so interpretative search can identify novelcompounds that are not included in the reference database.
Currently, interpretative search is not applicable to 1H spectra
because of the sensitivity of chemical shifts to spatial interactions [115] and because peak overlap prevents spectral peaks
to be assigned to corresponding atoms.

Software summary for compound identification
For data reduction, we focused on three spectral representations:i) peak lists obtained by peak picking, ii) numerical vectors obtained by binning and feature extraction and iii) trees. Table 5shows that peak picking is the most implemented method, available in 13 out of 16 software tools, followed by binning and thenfeature extraction, available in six and two tools, respectively.

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extracted. While binning keeps representative information
about the spectrum, a peak may be split into two bins if the binboundary lies on a peak center, which misrepresents the peak
as shown in Figure 3-2A. So, adaptive binning changes binboundaries to prevent overlap with peak centers [104, 106].
Second, sliding window divides the spectrum into fix-sized but
overlapped intervals [107]. Third, principal component analysis
reduces spectra by transforming the original data space into alower dimension space [108].
Trees: A spectrum is transformed into a tree by assigningpeaks to end nodes through recursively dividing the spectrum
into subspectra at mass centers [109, 110] (Figure 3-3A, B). The
resulting tree has spectra mass centers as branching nodes andpeaks as end (leaf) nodes, which retains information about peak
positions as well as their hierarchy.
Two factors are important in data reduction for natural
product dereplication: i) Type of measured spectra: NMR spectravary in how sharp peaks are and the propensity for peaks tooverlap. Sharp peaks in 13C NMR spectra are unlikely to overlap
and so peak lists are suitable. In contrast, 1H NMR peaks tend toheavily overlap, especially that of complex mixtures and in condensed methylene regions, and so binning or trees are preferred. ii) Spectral comparison measure suitable for the
representation (described in the next section).

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multiplet peaks. Binning produces in a large vector (1532 bin) in the 13C spectrum and a small one in the 1H spectrum (47 bins). Both spectra are reduced to relatively
few nodes when represented as trees. A colour version of this figure is available at BIB online: http://bib.oxfordjournals.org.

No software tools implement tree representation of spectra; however, the pseudocode is available [109]. Automics [58] and
MVAPACK [62] are the only tools implementing the three data reduction methods. rNMR [67], NMRPipe [65] and PERCH have bothpeak picking and binning functionalities.
Spectral comparison methods, such as inner product andpartial correlation, are available in statistical software frameworks, such as R and Matlab.
Finally, among database search paradigms, ranking search is
implemented in spectral databases, such as NMRShiftDB [39]and CSEARCH [38], because chemical class or functional groups
can be deduced by investigating the ranked compound list.

V- Future perspectives
Despite the abundance of computational resources that are useful for dereplication, we need to overcome several challenges torealize the aspired automation. We discuss four proposed solutions to existing challenges that can enhance the speed andquality of natural products dereplication results.

Enriching databases using automated machine leaning
methods
The deficiency of necessary data, namely, measured spectraand source organisms, presents a challenge to the developmentof a dereplication database, being summarized into two points:(1) The scarcity of measured spectra prevents spectral searchability from producing reliable results. (2) The absence of source
organisms data prevents their use to limit dereplication candidates. Two machine learning-derived approaches will provide afast and automated way to add data to databases and complete
missing data: spectral prediction and literature text mining.
First, compound spectra can be predicted from existing spectraon the basis of compound structural similarity [134]. Several
machine learning algorithms have been proposed to predict
NMR spectra [135–137], of which prediction accuracy increases
with training data size [138]. Similar algorithms have also beendeveloped for other types of spectra, such as fragmentation pattern in MS spectra [139–141], ultraviolet spectra (UV) [413] andchromatographic retention index [143–145]. Comparison and accuracy assessment of NMR prediction algorithms are reviewed

in [146–148]. Second, text mining of chemical information [149,150] can automatically extract compound associated data suchas NMR assignments and source organisms from the literature.

Developing software suite from building blocks
The wide use and integration of dereplication to current experimental design is hampered by the unavailability of open-source
software to process NMR spectra, to link and to summarize
information across all submitted spectra. While all steps fordereplication are implemented in software packages (Table 5),
the dereplication process requires the use of different tools andfamiliarity of programming languages. To accelerate dereplication, a software suite combining available software packages
through a unified graphical interface that can be used intuitively by experimental researchers on natural products isneeded.

Integrating different spectral types
Relying on NMR data only for compound identification becomesinsufficient as molecular complexity [151, 152] increases, as
exemplified by fatty acids and peptides. The integration of different spectral data into dereplication can resolve structural
ambiguities in these chemical classes. Several studies in dereplication showed promising results by integrating MS fragmentation with UV spectra [153, 154], and in combination with NMR
spectra [155, 156]. However, current studies have two limitations: (1) Other spectral types, such as chromatographic retention times, can differentiate between compounds that areotherwise similar. While these spectra utilized in metabolomics[61, 128, 157], they are not yet incorporated in dereplication. (2)Similarity scores between query and database compounds arecalculated based on only one spectral type, and candidate structures are then filtered using the other spectra. Calculating similarity scores based on all available spectra is still lacking.

Sorting databases for efficient search
Calculating similarity scores between a query spectrum and a
database containing hundreds of thousands of spectra can becomputationally intensive. Classifying database compounds
using molecular characteristics such as complexity [152, 158],

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Figure 3. Data reduction of spectra. 1H and 13C NMR spectra of camphor, a natural compound, demonstrate the effect of each data reduction method on different typesof spectra. Peak picking reduces the 13C spectrum to a few peaks (2A), but fails with the 1H spectrum (1A), as resonance coupling generates numerous overlappingComputational resources for natural product dereplicationcommon substructures [17, 159] have proved useful in efficientcompound identification [17, 18] and mining of chemical databases [160]. Applying similar strategies to spectral databasespresents promising possibilities.

Key Points

previously isolated natural products compounds,
which increases the chance of isolating new compound, and accelerates drug discovery therefrom.
• Automating dereplication requires the utilization andintegration of diverse computational resources.
• We review the currently available computational resources that are useful in dereplication by categorizingthem into databases, methods and software.
• We conclude by discussing current computational
challenges to automating dereplication with proposedsolutions.

Funding
This work is partially supported by JSPS KAKENHI 24300054.
Ahmed Mohamed would like to thank the Rotary Yoneyama
Memorial Foundation, Inc. for the scholarship awarded tohim.

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