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Original Paper

A Graph Convolutional Network–Based Method for
Chemical-Protein Interaction Extraction: Algorithm Development
Erniu Wang1*, MS; Fan Wang1*, PhD; Zhihao Yang1*, PhD; Lei Wang2*, PhD; Yin Zhang2*, PhD; Hongfei Lin1*,
PhD; Jian Wang1*, PhD
1

College of Computer Science and Technology, Dalian University of Technology, Dalian, China

2

Beijing Institute of Health Administration and Medical Information, Beijing, China*

all authors contributed equally

Corresponding Author:
Zhihao Yang, PhD
College of Computer Science and Technology
Dalian University of Technology
No 2, Linggong Road
Ganjingzi District
Dalian,China
Phone: 86 13190114398
Email: yangzh@dlut.edu.cn

Abstract
Background: Extracting the interactions between chemicals and proteins from the biomedical literature is important for many
biomedical tasks such as drug discovery, medicine precision, and knowledge graph construction. Several computational methods
have been proposed for automatic chemical-protein interaction (CPI) extraction. However, the majority of these proposed models
cannot effectively learn semantic and syntactic information from complex sentences in biomedical texts.
Objective: To relieve this problem, we propose a method to effectively encode syntactic information from long text for CPI
extraction.
Methods: Since syntactic information can be captured from dependency graphs, graph convolutional networks (GCNs) haverecently drawn increasing attention in natural language processing. To investigate the performance of a GCN on CPI extraction,
this paper proposes a novel GCN-based model. The model can effectively capture sequential information and long-range syntactic
relations between words by using the dependency structure of input sentences.
Results: We evaluated our model on the ChemProt corpus released by BioCreative VI; it achieved an F-score of 65.17%, whichis 1.07% higher than that of the state-of-the-art system proposed by Peng et al. As indicated by the significance test (P<.001),
the improvement is significant. It indicates that our model is effective in extracting CPIs. The GCN-based model can better capture
the semantic and syntactic information of the sentence compared to other models, therefore alleviating the problems associatedwith the complexity of biomedical literature.
Conclusions: Our model can obtain more information from the dependency graph than previously proposed models. Experimental
results suggest that it is competitive to state-of-the-art methods and significantly outperforms other methods on the ChemProt
corpus, which is the benchmark data set for CPI extraction.
(JMIR Med Inform 2020;8(5):e17643) doi: 10.2196/17643
KEYWORDS
chemical-protein interaction; graph convolutional network; long-range syntactic; dependency structure

Introduction
Biomedical literature has grown significantly with thedevelopment of biomedical technology, which contains a large
amount of valuable chemical-protein interactions (CPIs). CPI
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extraction plays an important role in various biomedical tasks
such as drug discovery, medicine precision, and knowledge
graph construction [1]. With the rapidly increasing volume ofbiomedical literature, it becomes time-and-resource–consuming
to extract CPIs from biomedical literature manually. There are
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some computational methods that have been successfully
proposed for automatic biomedical relation extraction [2-6].
However, most previous studies focused on the extraction of
drug-drug interactions, protein-protein interactions, andchemical-disease interactions; only a few attempts weredeveloped to extract CPIs [7].
The BioCreative VI ChemProt shared task [8] created the
ChemProt data set, which is used in the development of CPI
extraction methods. The current CPI extraction systems can begenerally divided into two categories: the traditional machinelearning–based methods and the neural network–based methods.
The traditional machine learning–based methods conventionallytrain a CPI extractor by handcrafted features [7]. The neuralnetwork–based methods can automatically learn powerfulfeatures to train a classifier, and therefore, have become apromising method for CPI extraction.
Mehryary et al [9] combined a support vector machine (SVM)
and long short-term memory (LSTM) to extract CPIs andachieved a high F-score by a rich set of features. Warikoo et al[10] also exploited a set of linguistic features to train a tree
kernel classifier to obtain CPIs from biomedical literature.
Generally, these methods depend heavily on feature engineering.
Recently, attention mechanisms have been successfully used inmany natural language processing tasks, and some works have
employed it in CPI extraction. Liu et al [11] aggregated an
attention mechanism and gated recurrent units (GRU) to extend
the LSTM model. Verga et al [12] encoded pair-wise predictions
over entire abstracts by synthesizing self-attention andconvolutions. Corbett and Boyle [13] employed multiple LSTM
layers with unlabeled data to extract relations amongst the
ChemProt corpus and achieved good performance. Peng et al[14] applied an ensemble system to extract CPIs, which consistsof three individual models, including SVM, convolutional neuralnetwork (CNN), and bi-directional long short-term memory(Bi-LSTM) modules. The system achieved an F-score of 64.1%
and won the top rank in the BioCreative VI ChemProt sharedtask.
However, most of the proposed methods only utilize thesequential information of sentences; syntactic information has
not been carefully studied yet. Due to the presence of complex
sentences in biomedical literature, it is difficult to effectively
learn the semantic and syntactic information for some neuralnetwork–based models (eg, CNN [15], LSTM [13,16], and GRU[17]). To address this problem, we apply a graph convolutional
network (GCN) [18,19] for CPI extraction. The GCN can exploit
dependency structure and capture long-range syntactic relationsof input sentences. Therefore, it is more effective and precise
than other modules for CPI extraction.

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Wang et al
Additionally, sentences in the biomedical literature are generally
lengthy, so there is a considerable amount of irrelevant words.
For example, in the sentence “Dasatinib (BMS-354825) is a
novel orally bioavailable SRC/ABL inhibitor that has activity
against multiple imatinib-resistant BCR-ABL isoforms in vitro
that is presently showing considerable promise in early-phaseclinical trials of chronic myeloid leukemia (CML),” “Dasatinib(BMS-354825) is a novel orally bioavailable SRC/ABL
inhibitor” can already express the inhibitory relationshipbetween the entities “Dasatinib” and “SRC.” Other words, which
may affect the performance of the relation extractor, areirrelevant. Inspired by Zhang et al [20], we apply a path-centricpruning strategy to incorporate relevant information while
maximally reducing the influence of noisy words in longsentences. This strategy retains tokens that are up to distance
N away from the dependency path in the lowest common
ancestor (LCA) subtree [21]. The experimental results provethat this strategy can improve the robustness of our model. The
model achieves the best balance between noisy words andrelevant words when N is set to 2.
A single GCN model usually depends highly on correct parsetrees to extract crucial information from sentences, while
existing parsing algorithms produce imperfect trees in manycases. To further improve the robustness of our mode, we apply
a Bi-LSTM network to obtain contextual information about
word order or disambiguation. The compound model can better
leverage local word patterns regardless of parsing quality.
In summary, we propose a GCN-based model in this paper toextract CPIs. We evaluated our model on the ChemProt corpus,
which is the benchmark data set for CPI extraction. To the bestof our knowledge, this is the first study to use a GCN encoding
syntactic graph for CPI extraction.

Methods
Overview
The overall architecture of our model is presented in Figure 1.
Our model contains three parts: the Bi-LSTM layer, the GCN
layer, and the classification layer. In the model, a Bi-LSTM
layer is applied first to capture local word patterns and output
the representation of each word within the whole sentence.
Subsequently, the contextualized representation and thedependency graph (with two directly attached dependencies)of input sentences are fed into the GCN layer to integrate
dependency information into word representations. After that,a max-pooling layer is applied to generate the representation of
the sentence and two target entities from word representations.
Finally, these representations are concatenated and fed into a
multilayer perceptron (MLP) for softmax classification. In thefollowing section, we will introduce our model in detail.

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Figure 1. The overall architecture of our model. Bi-LSTM: bi-directional long short-term memory; GCN: graph convolutional network; POS:
part-of-speech; MLP: multilayer perceptron; sub: subject; obj: object; hc: representation of chemical; hs: representation of sentence; hp: representationof protein; f: max-pooling function.

The Bi-LSTM Layer
We adopt a Bi-LSTM layer to capture contextual information
about word order and reduce the impact of parsing errors in ourmodel. The Bi-LSTM layer is applied on the whole sentence tolearn the representation of each word. Bi-LSTM can capture
more comprehensive features by dealing with the input sequence
from forward and backward directions, compared withunidirectional LSTM; it is the combination of the forward LSTM
and backward LSTM.
In the ChemProt corpus, some entities contain multiple types
of words, especially the relation type “PART_OF,” which means
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one entity is part of another type of entity within a relation entity
pair. For example, “thiazide-sensitive sodium-chloride
cotransporter” is a gene entity, and “sodium-chloride” is achemical entity. To reduce this interference, we apply priorknowledge of the entity type as a feature to improve CPI
extraction.
The input of the Bi-LSTM layer consists of three parts, including
word embedding, part-of-speech (POS) embedding and entity
type embedding. Given a sentence S = {w1,w2,…,wn}, the POS
sequence P = {p1,p2,…,pn} can be obtained by the Stanford
CoreNLP toolkit [22], where wi is the i-th word in a sentence
and pi is its POS. We obtain the sequence of entity types T =JMIR Med Inform 2020 | vol. 8 | iss. 5 | e17643 | p. 3
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Wang et al{t1,t2,…,tn} through the index information of the entity pairs ina sentence. We tagged entity tokens “chemical” or “gen” andother words “O.” The word embedding is initialized withpretrained word embedding, which is obtained by FastText [23].
POS and entity type embedding are initialized randomly. The
input of the model is denoted as follows:

For each token xi, the forward LSTM and backward LSTM
consider the contextual information before and after it,
respectively. The final output is the concatenation of the two
directions. The Bi-LSTM calculation process is presented asfollows:

where W(j) and W(l) are weight linear transformations, b(j) andb(l) are bias terms, and f is a nonlinear function (eg, a rectified
linear unit [ReLU]). We could obtain the hidden representationof each token directly influenced by its neighbors no more than
L edges apart in the dependency trees after applying an L-layer
GCN over word vectors. To avoid a sentence representation
favoring high-degree nodes regardless of the information carriedin the node and to transfer information into, wenormalized the activations in the graph convolution before
feeding it through a nonlinearity, and added self-loops to eachnode in the graph:where. I is the n × n identity matrix, anddegree of token i in the resulting graph.

is theThe Output and Classification Layer

where

and

denote the hidden states of the forward andbackward LSTM of xi, respectively.
operation.

denotes concatenation

The GCN Layer
GCNs can learn a state embedding, which contains theinformation of a neighborhood for each node in a graph. It has
been proven that models or dependency-based models are very
effective in relation extraction by capturing long-range syntactic
relations [24-26]. In our model, we apply a GCN to improve
the performance of CPI extraction by utilizing the dependency
parse trees of the input sentences. In order to reduce theinfluence of noisy words in long sentences, we further apply apruning strategy on the dependency trees to remove irrelevant
words while maximally keeping crucial content.
Given a sentence, we first apply the Stanford CoreNLP toolkit
to get its dependency tree, which is considered as an undirected
graph. Then, we apply a path-centric pruning strategy and retain
two directly attached words around the shortest path at the LCAof the two entities [20]. After that, we convert the subgraph into
an adjacency matrix A. If there is a dependency relation between
node i and j, is assigned with a value of 1. Finally, we apply a
GCN over the output of Bi-LSTM and adjacency matrix A to
get an updated hidden representation of hi. This can be
represented as shown in formula 5. In an L-layer GCN, if weuse
as the input vector andas the output vector for nodei at the l-th layer, the graph convolution operation of the l-th
layer can be represented as shown in formula 6.

The CPI extraction can be regarded as a classification problem.
Given a sentence S = {w1,w2,...,wn} where wi is the i-th token,
let Sc = {wc1,wc2,...,wcn} and Sp = {wp1,wp2,...,wpn} denotechemical sequence and protein sequence, respectively. The goalof CPI extraction is to predict the relation rR hold between thechemical Sc and gen Sp; otherwise, “no relation” is declared.
After the Bi-LSTM and GCN layers, we can obtain the hidden
representation of each token, which is influenced by not only
local word patterns but also long-range words. To utilize theseword representations for relation extraction, we mapped fromh(L) (n output vectors) to the sentence vector hsent. The
information close to entity tokens in the dependency trees isgenerally important in relation classification. Therefore, we also
apply a max-pooling function to obtain entity pair
representations hc and hp from h(L) as follows:wheredenotes the output after L-layer GCN, and f denotes
a max-pooling function.
Then, we connect sentence representation with entity
representation [27,28] as a new representation, and feed it intoa feed-forward neural network (FFNN) inspired by relational
reasoning works:

Finally, we apply a linear layer followed by a softmax operation
over the final representation hfinal to obtain a probability
distribution over chemical-protein relations and the computationis shown as follows:

where Wr and br are trainable parameters, and r is relation type.

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Evaluation Metrics
In experiments, the Micro-average F-score is applied to evaluate
the performance of our model, which is a harmonic mean of P
and R, where P denotes precision and R denotes recall:

Wang et al
TP, FN, and FP denote true positive, false negative, and false
positive, respectively.

Results
Data Retrieval and Preprocessing
CPI extraction aims to classify whether a semantic relation that
holds between the chemical and protein entity pairs within asentence or document. The BioCreative VI ChemProt task
delivered the corpus as a manually annotated CPI data set thatconsists of training, development, and test sets. Each set includes
the abstracts, entities, and relations files. Figure 2 provides anexample of the three files from the ChemProt training set.

Figure 2. Examples of the ChemProt corpus. CPI: chemical-protein interaction.

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The abstracts file provides the article identifier, title, and abstract
document for each article. The entities file consists of the
PubMed Unique Identifier (PMID), entity number, type of entity
mentions, start and end character offset, and text string of entity
mention. The relations file is composed of the PMID, CPI
relation class, evaluation type, and CPI relation and interactor
arguments. In the ChemProt corpus, there are 10-type relation
classes, and each relation class includes one or multiple relation
types (Table 1). Although there are 10-type relation classes in
ChemProt corpus, only five are used for evaluation purposes(ie, CPR:3, CPR:4, CPR:5, CPR:6, and CPR:9). Table 2 shows
the statistics of the ChemProt corpus.
The original corpus consists of PubMed abstracts frombiomedical literature in which more than 98% of relation entity
pairs within a sentence [8]. Therefore, we neglected thecross-sentence entity pairs and conducted experiment at thesentence level. For CPI extraction, we took some preprocessing
steps on the original corpus. First, we split abstracts into

sentences and only retained the sentences that contained therelational entity pairs. Then, we reassigned the training set anddeveloping set with a ratio of 9:1. Finally, we replaced each
digit string that was not an entity substring with a particular“num” tag.
Figure 3 gives two illustrative examples of CPI extraction. In
the first example, the sentence “Alprenolol and BAAM also
caused surmountable antagonism of isoprenaline responses, andthis beta 1-adrenoceptor antagonism was slowly reversible.”
contains a relational entity pair. To accurately extract the CPI,
we need to first detect the chemical entity “Alprenolol” andprotein entity “beta 1-adrenoceptor,” and then classify theinteraction as the CPR:6 class. The second example is a longand complex sentence. It is more difficult for the relation
classifier to extract the interaction between the chemical andprotein entities. Our model aims to predict the interactions, andthe output is the relation type of chemical-protein entity pairs
as shown in Figure 3.

Table 1. The chemical-protein relation (CPR) groups.
Group

Evaluated in the BioCreative VI
ChemProt shared task?

ChemProt relations

CPR:1

No

PART_OF

CPR:2

No

REGULATOR|DIRECT_REGULATOR|INDIRECT_REGULATOR

CPR:3

Yes

UPREGULATOR|ACTIVATOR|INDIRECT_UPREGULATOR

CPR:4

Yes

DOWNREGULATOR|INHIBITOR|INDIRECT_DOWNREGULATOR

CPR:5

Yes

AGONIST|AGONIST-ACTIVATOR|AGONIST-INHIBITOR

CPR:6

Yes

ANTAGONIST

CPR:7

No

MODULATOR|MODULATOR-ACTIVATOR|MODULATOR-INHIBITOR

CPR:8

No

COFACTOR

CPR:9

Yes

SUBSTRATE|PRODUCT_OF|SUBSTRATE_PRODUCT_OF

CPR:10No

NOT

Table 2. Statistics of the ChemProt corpus.
AnnotationsaData set
Training, nDevelopment, nTest, nDocument

1020

612

800

Chemicals

13,0178004

10,810Proteins

12,7527567

10,019

CPRa:3

768

550

665

CPR:4

2254

1094

1661

CPR:5

173

116

195

CPR:6

235

199

293

CPR:9

727

457

644

Evaluated CPIsb

4157

2416

3458

Evaluated CPIs in one sentence

4122

2412

3444

CPR: chemical-protein relation.

b

CPI: chemical-protein interaction.

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Figure 3. Illustrative examples of chemical-protein relation (CPR) classes.

Experimental Settings
In this work, FastText [23] was used to pretrain word embeddingon the ChemProt corpus. Before the experiments, we set therange of parameters based on experience, then tuned theparameters on the development set by using grid search todetermine the optimal parameters, and finally selected the bestmodel of parameters that were optimal for evaluation on thetest set. Without overfitting, the best model generally canachieve the best performance (the highest F-score) on thedevelopment set. The detailed tune range and hyperparametervalues are listed in Table 3.

pruning the tree down to the path; N=1 keeps all nodes that are
directly attached to the path; N=2,3 means holding words up to
distance 2 and 3 away from the dependency path in the LCA
subtree; and N=∞ retains the entire LCA subtree.
As shown in Figure 4, the performance of our model reaches
its peak and outperforms other pruning distance at N=2. Thisconfirms that pruning too aggressively (N=0,1) could lead to aloss of crucial information while retaining too many irrelevant
words (N=3) also decreases model performance due to theinterference of irrelevant information. When N=2, the model
achieves the best balance between including relevant andirrelevant information.

Comparison of Different Pruning Distances
To obtain the best pruning distance, we experimented with
N{0,1,2,3,∞} on the ChemProt corpus—N=0 corresponds toTable 3. Hyperparameter setting.
Hyperparameter

Tuned range

OptimalWord embedding dimension[100,200,300]

200

POS embedding dimension[10,20,30,40]

20

Entity type embedding dimension[40,50,60,70,80]

60

GCNb hidden units[100,200,300]

200

LSTMc hidden units[100,200,300]

200

Learning rate

[0.1,0.2,0.3,0.4]

0.3

Dropout rate

[0.4,0.5,0.6]

0.5

a

a

POS: part-of-speech.

b

GCN: graph convolutional network.

c

LSTM: long short-term memory.

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Figure 4. Comparison of different pruning distances.

Comparison of Different Embedding Features
Table 4 shows the effectiveness of different embedding features,
including word embedding, entity type embedding, and POS
embedding. The model achieves an F-score of 59.56% when
only using word embedding. When POS and word embedding
are combined, the F-score increases to 60.69%. When the entity
type and word embedding are combined, the F-score increases

to 62.52% (an increase of 2.96%). Furthermore, when both
entity type and POS embedding are integrated with wordembedding, the F-score improves to 65.17%. The results suggestthat the main contributor to performance is prior knowledge ofthe entity type. This confirms the validity of the entity type in
CPI extraction. The POS embedding is also valuable to themodel.

Table 4. Performance evaluation of different embedding features.

a

Embedding feature

Precision (%)

Recall (%)

F-score (%)

Δ (%)

Word

57.64

61.62

59.56

—a

Word+POSb

58.49

63.06

60.69

+1.13

Word+Entity type

64.06

61.05

62.52

+2.96

Word+POS+Entity type

63.79

66.62

65.17

+5.61

Not applicable.

b

POS: part-of-speech.

Comparison With the Baseline Method
Different single models and their ensemble models are comparedwith each other in this section. As shown in Table 5, allensemble models perform better than all single models, and the
GCN+Bi-LSTM model performs better than the Bi-LSTM+CNN
model. The results indicate that ensemble models can generally
capture more information than single models. In terms of overall

performance, the precision, recall, and F-score of the
Bi-LSTM+GCN model are higher than those of the
Bi-LSTM+CNN model. Our model can fully capture the overall
information of the sentence by combining sequence structure
information and syntactic information, while the Bi-LSTM+
CNN model could only obtain sequence structure information,
which confirms the effectiveness of the GCN model in CPI
extraction.

Table 5. Comparison with the baseline method.
ModelPrecision (%)

Recall (%)

F-score (%)

CNNa

42.47

69.43

52.70

GCNb

48.77

63.69

55.24

Bi-LSTMc

60.59

60.34

60.46

Bi-LSTM+CNN

57.77

64.73

61.05

Bi-LSTM+GCN (our model)

63.79

66.62

65.17

Single models

Ensemble modelsaCNN: convolutional neural network.

b

GCN: graph convolutional network.

c

Bi-LSTM: bi-directional long short-term memory.

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Discussion

by only retaining N away tokens from the dependency path inthe LCA subtree.

The experimental results suggest that our model can effectively
extract CPIs; it is better at learning semantic and syntactic
information from sentences compared to other models.
Additionally, the pruning strategy can alleviate the influenceof irrelevant words in long sentences in biomedical literature,

Comparison With Prior Work
A comparison of our model with other existing methods on the
ChemProt corpus is shown in Table 6. It can be found that neuralnetwork–based methods perform better than traditional machinelearning–based methods, and our method achieves the highest
F-score of 65.17%.

Table 6. Comparison with other existing methods.
Model

Precision (%)

Recall (%)

F-score (%)

Verga et al [12]

48.00

54.10

50.80

Matos [29]

57.38

47.22

51.81

Liu et al [11]

57.4

48.7

52.7

Lung et al [30]

63.52

51.21

56.71

Corbett and Boyle [13]

62.97

62.20

62.58

Mehryary et al [9]

59.05

67.76

63.10

Peng et al [14]

72.66

57.35

64.10

Our model

63.79

66.62

65.17

Lung et al [30] used machine learning methods to integrate thesemantic and dependency graph features through a three-stage
model. They achieved an F-score of 56.71%. Similarly, Corbett
and Boyle [13] used pretrained LSTM and Bi-LSTM to extract
CPIs in two stages and achieved a higher F-score of 61.5%. A
particular feature of their system was the usage of unlabeleddata both to pretrain word embedding and pretrain LSTM layers
in the neural network.
Verga et al [12] applied attention mechanisms in their model.
They synthesized convolutions and self-attention to extract
CPIs. Liu et al [11] achieved an F-score of 52.7% bysynthesizing GRU and attention pooling. The results ofword-level attention weights in the model of Liu et al [11]showed that attention mechanism is effective in selecting themost important trigger words when trained with semantic
relation labels without the need of semantic parsing and featureengineering.
Mehryary et al [9] employed an ensemble system that combined
the results of SVM and LSTM, and they achieved a competitive
result. Peng et al [14] utilized more external features. They
stacked SVM, CNN, and RNN models, and combined theoutputs of the three systems by either majority voting or
stacking. They achieved the best F-score of 64.10% in the
BioCreative VI ChemProt shared task. Our model synthesized
Bi-LSTM and GCN and achieved an improvement of 1.07% in
F-score over the system of Peng et al [14]. We further performed
significance tests with P<.05 indicating significance. The P
value of Peng et al [14] and our model is less than .001. It
indicates that the improvement of 1.07% in F-score issignificant.

Results Analysis
The experimental results indicate that the GCN module isvaluable in CPI extraction. It can extract CPIs from biomedical
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texts with syntactic graph representations. It might be also
efficient in other biomedical tasks by utilizing the sentenceparse structure. By comparing different pruning distance, we
revealed that the length of sentence also plays an important role
in relation extraction. The noisy words that are irrelevant torelations might hamper the performance of the extractor.
GCNs can learn effective representation for relation extraction.
However, a single GCN model could not capture the contextual
information of word order. Additionally, GCN highly depends
on correct parse trees to extract information from sentences,while existing parsing algorithms produce imperfect trees inmany cases. To resolve these issues and improve the robustnessof our model, we applied Bi-LSTM to generate contextualized
representation and feed it into the GCN layer. The results
confirm that the ensemble model of GCN and Bi-LSTM isvalidated for CPI extraction.

Contributions
The model we proposed in this paper aims to extract CPI andachieve state-of-the-art performance on the ChemProt corpus.
Our main contributions are as follows.
We proposed a novel neural model based on a GCN for CPI
extraction, which can capture long-range syntactic information
by utilizing the dependency structure of the input sentence. To
improve the robustness, we applied a path-centric pruning
strategy to remove irrelevant words without damaging crucial
content on the dependency trees. Through the pruning strategy,
the influence of noisy words can be reduced, thereby further
improving the performance of the model. Furthermore, aBi-LSTM layer is utilized to better leverage local word patterns
regardless of parsing quality.
Our model can automatically extract CPIs from a large amountof biomedical literature, which can save significant labor force
and resources. Abundant biological entity relations can deliver
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useful chemicals for some diseases and save time by optimizing
the drug development cycle, thereby helping pharmacists
discover drugs. Furthermore, the knowledge graph generally
contains rich, structured knowledge and has been widely usedin natural language processing applications, such as searchengines and question answering systems. However, the rapidly
increasing volume of information requires refinement in the
coverage of knowledge graphs. CPI extraction can help
researchers to efficiently acquire biomedical knowledge, which
can enrich the information needed for knowledge graphconstruction.

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Conclusions
We proposed a novel model based on a GCN to extract CPI.
The GCN module can encode syntactic information over the
dependency graphs of input sentences. To reduce the impact of
noisy words, our model only retains tokens that are up to a
distance of N=2 away from the dependency path in the LCA
subtree. Additionally, it applies Bi-LSTM to generate acontextualized representation and feed it into the GCN layer toresolve parsing errors and improve the robustness of the model.
The experimental results demonstrated that our model achieves
state-of-the-art performance. We plan to further improve ourmodel and apply our method to extract other biomedical relation
entity pairs.

Acknowledgments
We appreciate the valuable feedback provided by three anonymous reviewers. EW carried out the overall algorithm design andexperiments as well as the writing of the manuscript. FW, ZY, LW, YZ, HL, and JW contributed to the algorithm design and thewriting of the manuscript. All authors read and approved the final manuscript. This work was supported by a grant from the
National Key Research and Development Program of China (#2016YFC0901902).

Conflicts of Interest
None declared.

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Abbreviations
Bi-LSTM: bi-directional long short-term memory
CNN: convolutional neural network
CPI: chemical-protein interaction
CPR: chemical-protein relation
FFNN: feed-forward neural network
FN: false negative
FP: false positive
GCN: graph convolutional network
GRU: gated recurrent units
LCA: lowest common ancestor
LSTM: long short-term memory
MLP: multilayer perceptron
PMID: PubMed Unique Identifier
POS: part-of-speech
ReLU: rectified linear unit
SVM: support vector machine
TP: true positive

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Wang et al

Edited by T Hao, B Tang; submitted 30.12.19; peer-reviewed by M Hua, T Liyuan; comments to author 15.02.20; revised version
received 14.03.20; accepted 19.03.20; published 19.05.20
Please cite as:
Wang E, Wang F, Yang Z, Wang L, Zhang Y, Lin H, Wang J
A Graph Convolutional Network–Based Method for Chemical-Protein Interaction Extraction: Algorithm Development
JMIR Med Inform 2020;8(5):e17643
URL: http://medinform.jmir.org/2020/5/e17643/
doi: 10.2196/17643
PMID: 32348257©Erniu Wang, Fan Wang, Zhihao Yang, Lei Wang, Yin Zhang, Hongfei Lin, Jian Wang. Originally published in JMIR Medical
Informatics (http://medinform.jmir.org), 19.05.2020. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The
complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright andlicense information must be included.

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