# AMFPMC - An improved method of detecting multiple types of drug-drug
interactions using only known drug-drug interactions

B.V.Bar Vered  L.RGuy Shtar  L.RLior Rokach  G.SBracha Shapira  Department of
Software and Information Systems Engineering, Ben-Gurion University of the
Negev, Beer-Sheva, Israel

###### Abstract

Background Adverse drug interactions are largely preventable causes of medical
accidents, which frequently result in physician and emergency room encounters.
The detection of drug interactions in a lab, prior to a drug’s use in medical
practice, is essential, however it is costly and time-consuming. Machine
learning techniques can provide an efficient and accurate means of predicting
possible drug-drug interactions and combat the growing problem of adverse drug
interactions. Most existing models for predicting interactions rely on the
chemical properties of drugs. While such models can be accurate, the required
properties are not always available.

Results In this article we address the drug-drug interaction issue as a link
prediction problem and extend a method proposed by Shtar et al [1], which uses
artificial neural networks and propagation over graph nodes in order to
consider specific interactions when detecting drug-drug interactions. After
extracting and analyzing the possible interactions, a table which presents the
interactions as a one-hot vector between each pair of drugs is created. Then,
a deep neural network (DNN) is used as a predictor. in the training stage,
receiving two vectors in which the corresponding indexes are two drugs with a
specific interaction, and then, predicting an interaction for two drugs with
an unknown interaction. We perform holdout and retrospective analyses using
DrugBank data. Our results show that the proposed graph similarity derived
method, which is a graph similarity derived method, outperforms models that
use chemical and biologic properties and other state-of-the-art models and is
very efficient, as it uses less data to generate better predictions in less
time. Conclusion In this research, we have extended our DDI prediction
algorithm while using the same graph similarities and artificial intelligence
techniques. This extension enabled the model to detect a specific interaction
and not only predicting whether there is or there isn’t an interaction between
a pair of drugs. The drug-drug interaction prediction problem should be solved
using different and diverse datasets, in AMFPMC, these diverse and different
datasets are important since our algorithm relays on interactions network, and
interactions network are not helpful when it comes to new drugs with unknown
interactions. The main difference between many studies and our research is
that usually these studies relay on a multiple domain dataset, which means
they use several chemical properties of drugs while our algorithm solely
relays on basic mathematical properties. This usually means that our algorithm
and DNN’s input used are quite simpler and enabling future extensions. Our
evaluation and results demonstrates the proposed method’s superiority, which
can be seen in the metrics calculated comparison. Moreover, we demonstrate
that the embedding of the input to the DNN can be used and be helpful in other
various problems.

Drug-drug Interaction (DDI),

Deep neural network,

Graph similarity,

Adjacency matrix factorization with propagation multi-class(AMFPMC) ,

###### keywords:

Research

## Background

Adverse drug events are considered medical injuries and are thought to be one
of the top causes of death in the U.S., ahead of many chronic diseases such as
diabetes, AIDS, and heart disease [2]. The cost attributed to these reactions
is high and is estimated at thousands of dollars per patient annually in the
U.S. [3]. The range of patients that are harmed by drug-drug interactions
range 3-5% of all medications errors within hospitals. Furthermore, ADRs
(adverse drug reactions) represent a substantial proportion of the number of
patients seeking medical care in general and the number of patients rushed to
intensive care units [4, 5] in particular.

Many studies using computational techniques for the detection of DDIs have
been published in the last decade. In most cases, initial efforts focused on
detecting interactions in a binary manner, using an algorithm to detect
whether an interaction between two drugs exists or not. However, recent
studies have proposed models capable of detecting a specific set of
interactions. Various similarity-based models have been proposed, including
models based on DDI similarities [6], side effect similarities [7], structural
similarities [8], and a combination of similarities [9, 10, 11]. In one study
natural language processing techniques were used to embed the interactions and
then the embeddings were used to predict DDIs [12], while another used
integrated similarity and then applied another neural network to classify the
interaction [13]; In the latter, the problem is firstly considered a binary
problem in which the model detects whether there is an interaction. After this
stage the output generated by the model served as an input to an additional
model which predicted a specific interaction. This way the model’s complexity
is divided between two neural networks. There are models which use the
interactions of other components to predict DDIs; for example, in one proposed
method substructure-substructure interactions were used [14]; that method is
similar to the one proposed in this study, as it utilizes a graph and graph
similarities techniques, but the two models differ in that the authors of that
study used a graph and graph similarities techniques on substructures while we
use it on matrices. In a more complex method, other properties in addition to
chemical substructures were used (e.g., biologic properties), enabling the
prediction of interactions between biologic drugs and other types of drugs
[15].

DDI detection using an interaction network can be reduced to a link prediction
problem in a graph, this reduction is used in this study. In a link prediction
problem, the aim is to accurately detect and predict the edges (interactions)
between the nodes (drugs) that will be inserted into the graph. The most
intuitive approach for this is to add edges to nodes for which there is a
large overlap; this means that if nodes A and B have many similar neighbors,
and they are not currently connected by an edge, they might need to be
connected because of the large overlap between their neighbors; In the case of
DDIs, edges are interactions, and nodes are drugs, and when two drugs have
many similar neighbors, there may be an interaction between them. The DDI
prediction problem involves a different characterization of the link
prediction problem in which edges indicate whether an interaction occurs
between two drugs (two nodes); here, we want to be more specific by indicating
a type of edge that corresponds to a specific interaction. This problem can be
approached as a link prediction in weighted multiplex systems [16] problem.
Another approach, which is less intuitive but more powerful, is the matrix
factorization method in which a matrix is factorized into a product of
matrices. This technique is widely used in dimensionality reduction area, and
recently, many studies have successfully factorized a matrix using deep neural
networks [17, 18, 19]. Figure 1 present a Link prediction reduction example of
the adjacency matrix representing the DDI graph. Although this technique has
obtained meaningful results in the DDI field, it has some drawbacks, such as
asymmetric decomposition when the matrix’s transpose should be equal to the
original matrix; this occurs because the row vectors and column vectors are
identical. Another drawback is the need to limit the score to the range [0,1].

Fig 1: Link prediction problem reduction of the DDI problem. A) A DDI graph B)
The DDI graph is represented by an adjacency matrix: the rows and columns
represent drugs, and the different values stand for different interactions.
For example, the cell in the first row and the right-most column represents
the interaction between D1 and D5. In a link prediction problem, a score is
calculated for every non-existent interaction.

For an accurate DDI prediction, a neural network that encompasses the linear
structure of the interaction graph is required. In this study, AMFPMC receives
a one-hot encoding representation of two nodes as input. The model’s output is
a single class representing the interaction between the two drugs. In AMFPMC,
the numbers greater than zero indicate specific types of interactions, the
value of zero has different meanings according to the evaluation used
(retrospective or holdout). Only the drug interaction graph is needed to
utilize this model, and no other domain-specific information (such as chemical
properties and substructures) is required.

Fig 2: Overview of AMFPMC’s architecture. Drugs are represented as nodes;
embedding layers (which act as latent factors) and biases are shared between
input nodes. Dropout is used as a regularization mechanism to prevent
overfitting.

## Evaluation

In this section, we discuss the two experiments performed to properly evaluate
AMFPMC (AMFPMC): retrospective and holdout evaluations using drugs from
various versions of the DrugBank database [20]. We have also evaluated AMFPMC
on specifically biologic drugs and checked its performance, this is due to
prior studies often neglecting biologic drugs and supporting just small
molecules. In addition, we have evaluated the performance of the embedding
created by the model further, to assess its ability to address other problems
(besides drug interaction prediction) and determine whether it contains useful
information about drugs.

Figure 3 presents the training and testing evaluation for the retrospective
evaluation in which two version of the DrugBank database were used. There are
many differences between the two versions - many new interactions were added
to the more recent version, along with some modifications to interactions
included in the earlier version (which can interfere the learning process).
DrugBank’s version $5.1.0$ (published in April $2018$) served as the dataset
for training, and DrugBank’s version $5.1.6$ (published in April $2020$) was
used for testing. Version $5.1.0$ of the database contains $321,738$
interactions and $2,904$ drugs, while version $5.1.6$ contains $1,334,875$
interactions and $4,264$ drugs. To ensure dataset compatibility for the model
introduced in this paper. Only the drugs that appear in both versions were
extracted during the preprocessing stage. For the retrospective evaluation, a
total of $37$ interactions were extracted in the preprocessing stage; this
includes interaction numbered $0$ which is no interaction, interactions
numbered $1-35$ which are interactions that are considered common (have plenty
of pairs of drugs with these interactions), and interactions numbered $36$
which are ”other” - a set of uncommon interactions which were grouped together
because of their rarity. For the holdout evaluation a total of $60$
interactions were extracted, furthermore, the holdout evaluation uses a
modified DrugBank’s data and these changes are elaborated below. Since the
adjacency matrix is symmetric, we used the original wording in the
interactions and extracted the keyword phrases; then we removed words that are
redundant, for example, in the case of the following sentence from DrugBank:
”$The\ metabolism\ of\ Drug\ b\ can\ be\ decreased\ when\ combined\ with\
Drug\ a$” we extract ”$metabolism\ decreased$,” and an index was assigned to
represent the interaction (as a number) in the adjacency matrix. In this
example, the number $2$ was assigned, and the cell representing the
interaction between $Drug\ a$ and $Drug\ b$ holds the value $2$. Since all of
the interactions are symmetric, the $Drug\ a$ and $Drug\ b$ wording in the
interaction, which is comprised of the keyword phrase, is not needed. In the
holdout evaluation, the interactions were randomly selected, although in
reality some interactions are more likely to be found due to the drugs
prevalence in drug combinations, the interactions prevalence in drug
combinations, drugs chemical compounds, and more.

Fig 3: Training and testing evaluation for in retrospective evaluation. In the
training stage, parameter tuning is performed using DrugBank version $5.1.0$.
The final model is trained using these parameters with the data from DrugBank
version $5.1.0$ and tested using DrugBank version $5.1.6$.

In the holdout evaluation, a tuned version of DrugBank $5.1.3$ which was used.
This version, which only includes molecule drugs, was compiled by Deng and his
team [21] and is required for the comparison. This version includes $572$
drugs and $37,264$ interactions among them. In this version, interactions
numbered $0-64$ are valid interactions, and there is no representation for
non-interactions like there is in the retrospective evaluation. In the holdout
evaluation, we have also compared AMFPMC’s performance to that of the
following state-of-the-art models: random forest (RF) [22], k-nearest
neighbors (KNN), and logistic regression (LR).

### Metrics

The following metrics are used to evaluate the performance of the model in a
holdout evaluation: the accuracy, area under the receiver operating
characteristic curve (AUROC), F1-score, recall, and precision metrics. We also
used the area under the precision-recall curve (AUPR) metric, which could be
relevant for use in other link prediction problems [23]. For the retrospective
evaluation, the AUROC served as our main metric, since this evaluation is
mainly aimed at demonstrating AMFPMC’s ability to predict biologic drugs.

### Baselines

We compared our AMFPMC method to the following models:

  * •

DDIMDL which was used by Deng et al. [21]. This method relies on the chemical
properties of drugs, such as drug’s smiles, enzymes, and targets, it also uses
Jaccard similarity, since each vector contains properties about the drugs
themselves, rather than just the index as done in AMFPMC. DDIMDL uses a single
database which means it is not suitable for retrospective evaluation. To
compare DDIMDL and AMFPMC, as a part of the holdout evaluation we performed
$5$-fold cross-validation.

  * •

State-of-the-art models: RF, LR, and KNN. These models perform well in the
scenarios of link prediction. As in the case of the model proposed by Deng et
al, these models receive the following features as input: drug’s smiles,
enzymes, and targets.

We implemented AMFPMC using Keras [24] as was done in DDIMDL. However, their
model was configured to be evaluated solely using holdout evaluation, while
ours was configured to be evaluated using retrospective evaluation. This is
due to their usage of a single database. To enable comparison between the
models, we refactored our code so it could run on the same data they used.

### Parameter tuning

In order to determine the optimal parameters for each evaluation, we used grid
search to identify the maximum accuracy value on the validation set. We did so
by extracting $20$% of the test data and used this as a validation set. We
then trained on the original training set and validated on the validation set.
Then, the parameters values which achieved the maximum accuracy values in the
grid search were used in AMFPMC to achieve the best results. This was done
twice, once for the holdout evaluation and once for the retrospective
evaluation. In both cases, we examined the following batch sizes {$128$,
$256$, $512$, $102$4} and learning rates {$0.1$, $0.01$, $0.001$, $0.0001$},
and the following ranges for the dropout levels [$0$-$0.9$], number of epochs
[$1$-$50$], and propagation factors [$0$-$1$] (in intervals of $0.1$). The
optimal parameters, which were used in our experiments, are presented in Table
1.

Table 1: Optimal parameters for each evaluation: retrospective and holdout Evaluation type | Embedding Size | Dropout | Epochs | Batch Size | Learning Rate | Propagation Factor
---|---|---|---|---|---|---
Retrospective | 512 | 0.3 | 5 | 1024 | 0.01 | 0.8
Holdout | 512 | 0.3 | 15 | 256 | 0.01 | 0.6-1.0

## Results

In this section, we present AMFPMC’s results in the holdout and retrospective
evaluations (we also present the results of embedding evaluation). The
retrospective evaluation includes the comparison between biologic and molecule
drugs. As seen below, the results demonstrate AMFPMC’s superiority and ability
to outperform the other models on most metrics, particularly the AUROC and
AUPR. It can also be seen that AMFPMC performs equally well on biologic drugs
due to the fact that it uses general properties, such as interaction
neighboring, rather than relying solely on chemical substructures and
properties. Our results are mainly based on the holdout evaluation - because
we compare AMFPMC to DDIMDL and due to the fact that Deng et al [21] provided
a dataset that is relevant only to a single version (DrugBank $5.1.3$), this
prevents us from performing a retrospective evaluation on their data. Other
state-of-the-art models are also compared to in this evaluation.. Therefore
the findings presented are based mainly on the holdout evaluation.

### Holdout evaluation

Holdout evaluation was performed by using the tuned version DrugBank’s
database provided by Deng et al [21], which is a subset of DrugBank $5.1.3$
that only includes molecule drugs. In AMFPMC original setup, zero indicates no
interaction, to enable a holdout evaluation, we modified the DNN so that
AMFPMC could be trained, tested, and make predictions. In our comparison,
$5$-fold cross-validation was used.

Table 2: Performance comparison (micro-comparison and accuracy) Model | ACC | AUPR | AUC | F1-SCORE | PRECISION | RECALL
---|---|---|---|---|---|---
AMFPMC | 0.8964 | 0.9565 | 0.9987 | 0.8945 | 0.8944 | 0.8943
DDIMDL | 0.8818 | 0.9334 | 0.9977 | 0.8817 | 0.8816 | 0.8815
KNN | 0.7147 | 0.7793 | 0.9806 | 0.7147 | 0.7146 | 0.7145
LR | 0.7211 | 0.7845 | 0.9933 | 0.7211 | 0.7210 | 0.7209
RF | 0.7729 | 0.8457 | 0.9953 | 0.7729 | 0.7728 | 0.7727
Table 3: Performance comparison (macro-comparison) Model | AUPR | AUC | F1-SCORE | PRECISION | RECALL
---|---|---|---|---|---
AMFPMC | 0.8821 | 0.9915 | 0.7534 | 0.8878 | 0.7001
DDIMDL [21] | 0.8343 | 0.9873 | 0.7706 | 0.8723 | 0.7271
KNN | 0.6266 | 0.9299 | 0.4917 | 0.7491 | 0.4125
LR | 0.6115 | 0.9796 | 0.3062 | 0.5036 | 0.2543
RF | 0.6545 | 0.9752 | 0.4871 | 0.7156 | 0.4134
Table 4: Top-$20$ types of interactions keyword phrases along with the model’s AUROC in order based on the # of samples of each interaction, predicted as holdout on DrugBank $5.1.3$. Interaction | AMFPMC | DDIMDL | # samples
---|---|---|---
the metabolism decreases | 0.9344 | 0.9343 | 9810
the risk or severity of adverse effects increases | 0.9576 | 0.9513 | 9496
the serum concentration increases | 0.9289 | 0.9028 | 5646
the serum concentration decreases | 0.9440 | 0.9169 | 2386
the therapeutic efficacy decreases | 0.9084 | 0.9188 | 1312
the central nervous system depressant ( CNS depressant ) activities increases | 0.9873 | 0.9645 | 1132
the QTc - prolonging activities increases | 0.9309 | 0.9140 | 1102
the hypotensive activities increases | 0.9620 | 0.9485 | 1086
the metabolism increases | 0.8464 | 0.8790 | 695
the antihypertensive activities decreases | 0.9336 | 0.9588 | 551
the hypoglycemic activities increases | 0.9611 | 0.9430 | 362
the anticoagulant activities increases | 0.9444 | 0.9348 | 318
the serum concentration of the active metabolites increases | 0.8911 | 0.7750 | 245
the bradycardic activities increases | 0.9426 | 0.9201 | 245
the serotonergic activities increases | 0.8083 | 0.8162 | 188
the therapeutic efficacy increases | 0.8636 | 0.8967 | 165
the hypokalemic activities increases | 0.9938 | 0.9785 | 163
the orthostatic hypotensive activities increases | 0.9244 | 0.8738 | 159
the cardiotoxic activities decreases | 0.9523 | 0.9648 | 158
the excretion rate decreases, which could result in a higher serum level | 0.9643 | 0.9642 | 154

https://www.overleaf.com/project/6289e22895afb9cfbfd6ea77

Fig 4: AUPR curves for top-$4$ algorithms in the evaluation. LR had results
that were close to $0.5$ so it is not shown in this graph.

Table 2 presents a comparison of the examined models performance on all of the
metrics, with values for each model calculated using micro-calculations and
the accuracy scores, while Table 3 presents the macro-calculations, with the
best scores appearing in bold. As can be seen, AMFPMC outperforms the other
models on almost all of the metrics. Figure 4 presents the AUPR values
obtained for all of the models (except for the LR model which scored poorly)
averaged over all of the interactions classes. A graph of the AUC scores is
less useful (and therefore was not included), since the AUC tends to be high
in holdout evaluations and the results were quite close to each other. As
shown in the figure, AMFPMC is superior to the other models in terms of the
AUPR. Table 4 provides a brief summary of the top-$20$ interactions in terms
of the number of samples and their AUROC scores for AMFPMC and DDIMDL.

### Retrospective evaluation

In this evaluation, AMFPMC was trained on DrugBank’s version $5.1.0$ and
tested on DrugBank version $5.1.6$. In this case, the pairs of drugs that the
model is trained on are pairs that have some interaction between them, and the
pairs of drugs that are tested on are the pairs that have no interaction
between them; the final prediction is then verified using DrugBank’s version
$5.1.6$. This evaluation of AMFPMC is to predict on solely biologic drugs. The
point of this evaluation is to evaluate AMFPMC’s ability to predict on
biologic drugs.

In table 5 the results obtained when AMFPMC is only used to predict
interactions between biologic drugs are shown.

Table 5: AMFPMC’s performance when predicting interactions between biologic drugs  | # of Drugs | # of DDIs | AUC | AUPR
---|---|---|---|---
Biologic Drugs | 452 | 162,949 | 0.820 | 0.98

### Embedding evaluation

As can be seen in Figure 2, AMFPMC creates an embedding for each drug by
performing various functions on the input data. This embedding contains useful
data about the drug which can be used in drug interaction prediction as well
as other areas. To evaluate the embedding’s capability for this, we used two
datasets and added a column containing an embedding for each drug in each
dataset; then we used state-of-the-art models to examine whether the data in
this column was could predict the result column. We used datasets from a paper
on drug safety in pregnancy [25] and an unpublished paper on the prediction of
drugs’ anticancer activity. In those papers, the model was used to predict
respectively whether a drug is safe to use within being pregnant and or has
anticancer activities. Tables 6 and 7 compare the performance of AMFPMC and
AMFP (a binary model) in terms of the AUC. As can be seen, despite the fact
that AMFPMC and its embeddings are quite complex, AMFPMC’s performance still
often surpasses the performance of AMFP on each dataset; these results
demonstrate that AMFPMC’s embeddings have widespread relevance and can be used
for problems beyond drug interaction prediction.

Table 6: Performance (AUC) of AMFPMC and AMFP embeddings in the pregnancy drug safety task | AMFPMC | AMFP
---|---|---
XGBoost | 0.676 | 0.586
Random Forest | 0.702 | 0.689
LightGBM | 0.7 | 0.620
Table 7: Model’s embeddings result in anticancer activities drugs | AMFPMC AUC | AMFP AUC
---|---|---
XGBoost | 0.915 | 0.9133
Random Forest | 0.913 | 0.909
LightGBM | 0.901 | 0.908

## Case studies

In this section, we analyze the performance of AMFPMC by taking the
predictions of AMFPMC on unobserved interactions of pairs of drugs that were
extracted from DrugBank’s version $5.1.9$ (the most recent version at the time
of this study. Then, we check on various validation sources to see if those
sources also mention or hint at the drugs interaction. More specifically, for
interactions for which AMFPMC has a high confidence in its prediction, we try
to obtain some kind of confirmation or hint from another source that
corroborates our finding and helps us better understand AMFPMC’s decision.
Table 8 includes the top-10 interactions predicted by AMFPMC ranked by the
model confidence. This section is highly important as it emphasizes the
strength of DDI prediction models. To perform this evaluation, the $40$
interactions for which our model had the greatest confidence in its prediction
were extracted. Of these $40$, we identified eight interactions which did not
appear in DrugBank’s version $5.1.9$ which were mentioned in various other
sources (e.g., the literature, drug manuals). We note that DrugBank is updated
on a daily basis, and these interactions will likely be added to the next
version released. Table 8 provides additional details pertaining to our
evaluation of these eight interactions.

Table 8: $8$ interactions that were not observed in DrugBank $5.1.9$ (the most recent version at the time of this study) but were predicted by AMFPMC and also either confirmed or hinted at by various other sources. # |  Drug 1 |  Drug 2 |  Signaling Source |  Interaction Predicted |  Explanation
---|---|---|---|---|---
1 |  Lixisenatide |  Insulin Glargine |  Wikipedia |  Lixisenatide may increase the hypoglycemic activities of Insulin Glargine. |  According to Wikipedia [26], this drug combination can cause hypoglycemia.
2 |  Oxitriptan |  Melatonin |  DrugBank |  The risk or severity of adverse effects can be increased when Oxitriptan is combined with Melatonin. |  According to DrugBank [27], Oxitriptan can cause increased Serotonin production; since Serotonin is a precursor for Melatonin, one can consider that taking the two drugs together could result in an overdose.
3 |  Docusate |  Phenolphtalein |  NCBI |  The risk or severity of adverse effects can be increased when Docusate is combined with Phenolphtalein. |  Some studies [28] indicate adverse effects when the drugs are taken together.
4 |  Chromic Chloride |  Cyanocobalamin |  NCBI |  Chromic Chloride may decrease the excretion rate of Cyanocobalamin which could result in a higher serum level. |  According to an NCBI article [29], this combination of drugs could result in decreased excretion and increased efficiency of Cyancobalamin.
5 |  Bromazepam |  Propantheline |  ndrugs |  Bromazepam may increase the CNS depressant activities of Propantheline. |  According to a manual [30] which relies on references from European health department, this drug combination is not recommended for patients with pre-existing CNS depression.
6 |  Tocopherol |  Cyanocobalamin |  NCBI |  Tcopherol may decrease the excretion rate of Cyanocobalamin which could result in a higher serum level. |  ccording to an NCBI article [31] hinted about a probable interaction between the two related to the excretion rate.
7 |  Isopropamide |  Trifluoperazine |  TabletWise |  Isopropamide may increase the CNS depressant activities of Trifluoperazine. |  According to a manual [32] an individual should not take this drug combination if he/she has pre-existing CNS depression.
8 |  Clidinium |  Chlordiazepoxide |  NIH DailyMed |  Clidinium may increase the CNS depressant activities of Chlordiazepoxide. |  In the manual [33] there is an entire paragraph discussing adverse CNS reactions seen when taking this drug combination.

## Discussion

Drug interactions are the cause of many emergency room visits. Estimates of
the percentage of patients harmed by drug interactions range from $3$-$5$% of
all medication errors within hospitals [4, 5]. In recent years, the adoption
of drug-drug interaction detection models and prediction models has increased,
particularly during the COVID-19 pandemic [34]. Drug-drug interactions are
usually not identified until late in the research and clinical stages;
therefore, drug-drug interaction models are still considered the most
practical way of identifying potentially harmful interactions since they can
be performed earlier in the drug experimentation process. In our latest
article we introduced AMF and AMFP, two models for DDI detection; we also used
DrugBank’s powerful databases to compare our proposed models to other models,
a comparison which demonstrated the new models superiority on the AUROC, AUPR,
accuracy, and F1-score, precision, and recall metrics. That study focused on
predicting whether an unobserved interaction exists or not, while the current
research empowers these models with the ability to predict the type of
interaction present rather than just whether an interaction exists. Our
proposed model can handle multiple types of DDIs, and our findings confirm
that an interaction network of DDIs is the most useful data source for
discovering previously unobserved DDIs. The models flexibility is due to the
fact that AMFPMC does not rely on knowledge of drugs chemical structures or
properties and instead relies on graph similarities. Moreover, AMFPMC also has
the ability to predict interactions between biologic drugs. In addition, since
it relies on a very small set of properties (just the interaction network), it
provides accurate results quickly and effciently. The use of weight balancing
was also shown to be beneficial to AMFPMC, especially on small datasets or
datasets which have interactions that have a very small number of samples
(i.e., less than 10), and this finding demonstrates AMFPMC’s effectiveness on
datasets of various sizes.

We performed an evaluation of the AUROC score as a function of the propagation
factor, both in the holdout and retrospective evaluations. For the holdout
evaluation, the optimal values of the propagation factor were [$0.6$ \- $1$],
and for the retrospective evaluation, the optimal values were $0.6$ and $0.7$.
The retrospective evaluation is of course preferable, because it is more
realistic and indicates whether a model truly generalizes or not. The holdout
evaluation is helpful when comparing the results of multiple models, as it can
show which model produces better results (on average), while for case studies
and predicting unobserved interactions, a retrospective evaluation is
preferable. As can be seen, the fluctuations in the holdout evaluation metrics
comparisons are small, and in the retrospective evaluation, the propagation
factor has more impact.

Figure 5 presents the propagation factor analyses for both evaluations. For
the holdout evaluation, the score remains almost the same starting from $0.6$;
this means that the weights being propagated are very critical for the
performance of the holdout evaluation. In contrast, for the retrospective
evaluation, the score does not change drastically. However the larger the
propagation factor, the larger the AUROC score; this is not the case for AMFP
(binary model) which obtained the highest score when the propagation factor is
$0.6$. It is then safe to assume that in AMFPMC, the weights that were
propagated are of great importance, likely due to the weight balancing
required and the fact that there are many interactions with differing numbers
of samples, including some with a very small number of samples.

Fig 5: Propagation factor analyses for both evaluations. A) Holdout evaluation
propagation factor evaluation. As can be seen, the value maximizes starting at
a propagation factor equal to 0.6 and remains the same until it reaches a
propagation factor of $1$ and drops by 0.001 (an insignificant amount). B)
Retrospective evaluation propagation factor evaluation. As can be seen, it
does not change drastically, and it reaches a maximum value with a propagation
factor equal to; this emphasizes the thought that in retrospective evaluation,
weights do not change the final results, regardless of whether they are
propagated or not, although a little propagation is, as can be seen,
appreciated.

## Contribution

AMFPMC can be useful in the medical domain to identify unknown interactions.
The use of additional information, such as chemical properties like smiles,
enzymes, and targets, and chemical substructures, could improve AMFPMC’s
performance. In contrast, most of the recently proposed models have relied
solely on chemical properties for drug-drug interaction detection, which means
they are not be effective for drugs which the chemical properties are unknown
or non-existent (i.e., biologic drugs).

In addition, we have compiled a list of interactions that were identified on
DrugBank 5.1.3 and 5.1.6 and made this list publicly available. This list may
be helpful for future studies on DDI. This list may also be used by qualified
physicians, who are in a position to analyze which interactions are considered
harmful and which are considered benign, this knowledge which will enable
researchers to focus on those interactions that are considered harmful.

AMFPMC can support large datasets containing many drugs and interactions. In
the retrospective evaluation, the scalable model was trained on over
$2,000,000$ interactions in just a few minutes and can support even larger
datasets. The holdout evaluation showed that on small datasets (with less than
$40,000$ interactions) AMFPMC’s precision is also obvious, even without the
use of any chemical properties.

By successfully performing multi-class DDI prediction using just graph
similarity algorithms, without the need for knowledge on drugs (besides their
interactions), we have opened the door to future work on ensemble models
combining graph similarity algorithms with other algorithms or properties, and
this could enhance existing models. In this research, we did not see the
benefit of using such ensemble models, however future research could explore
this further, perhaps using different networks, like squeeze-and-excitation
networks [35], which can be used to combine different models and extract one
result.

## Appendix

The tables presented in this section contain the interactions (keyword
phrases) identified in the holdout and retrospective analyses. Interactions
are listed using their keyword phrases, which represent the main parts of the
interaction, without redundant words. For example, the interaction
$metabolism\ decreased$ means that $Drug\ A\ decreases\ the\ metabolism\
activities\ of\ Drug\ B$. It is important to note that this does not mean that
the interaction between these drugs results in decreased metabolism activities
in the patient; rather it means that the metabolism activities of one drug are
increased if the drug is taken with another drug. The main difference between
the holdout and retrospective analyses in terms of the interactions is that in
the holdout evaluation, thane interaction indexed 0 is a real interaction, and
in the retrospective evaluation, an interaction indexed 0 indicates that there
is no known interaction.

Index | Keyword phrase
---|---
0 | no interaction
1 | increased risk adverse effects
2 | decreased metabolic activities
3 | decreased excretion rate
4 | decreased therapeutic activities
5 | increased serum concentration
6 | increased metabolic activities
7 | increased (CNS depressant) activities
8 | increased QTc prolonging activities
9 | increased hypotensive activities
10 | decreased antihypertensive activities
11 | decreased serum concentration
12 | increased hypertension
13 | increased therapeutic activities
14 | increased bleeding
15 | increased excretion rate
16 | increased hypoglycemic activities
17 | increased hyperkalemia
18 | increased hemorrhage
19 | increased nephrotoxic activities
20 | decreased absorption which results in a reduced serum efficacy
21 | increased hypotension
22 | increased arrhythmogenic activities
23 | increased thrombogenic activities
24 | increased bradycardic activities
25 | increased gastrointestinal activities
26 | increased tachycardia
27 | increased hypertensive activities
28 | increased neuroexcitatory activities
29 | increased renal failure
30 | increased myopathy
31 | increased serotonin syndrome
32 | increased hyperglycemic activities
33 | increased serotonergic activities
34 | increased sedative activities
35 | increased hypokalemic activities
36 | Other (set of uncommon interactions combined)
Table 9: Indexed interactions for retrospective evaluation. The first column
contains the index assigned to the interaction inside the code, and the second
column contains the keyword phrase extracted for simpler calculation
(redundant words and words that are redundant because the problem is symmetric
were removed). The keyword sentences were extracted from full interaction
sentences; ”other” interactions can be seen in our comprehensive extraction of
interactions [36].

Index | Keyword Sentence
---|---
0 | decreased metabolism
1 | increased risk adverse effects
2 | increased serum concentration
3 | decreased serum concentration
4 | decreased therapeutic efficacy
5 | increased (CNS depressant)
6 | increased QTc-prolonging activities
7 | increased hypotensive activities
8 | increased metabolism
9 | decreased hypoglycemic activities
10 | decreased anti hypertensive activities
11 | increased anticoagulant activities
12 | increased bradycardic activities
13 | increased serotonergic activities
14 | increased therapeutic efficacy
15 | increased hypokalemic activities
16 | increased orthostatic hypotensive activities
17 | decreased cardiotoxic activities
18 | decreased excretion rate
19 | increased atrioventricular blocking activities
20 | decreased sedative activties
21 | increased tachycardic activities
22 | increased hypertensive and vasoconstricting activities
23 | increased QTc prolongation
24 | increased anti hypertensive activities
25 | increased arrhythmogenic activities
26 | increased cardiotoxic activities
27 | increased hypotension
28 | decreased bronchodilatory activities
29 | increased hyperkalemic activities
30 | increased nephrotoxic activities
31 | increased hypertensive activities
32 | increased vasoconstricting activities
33 | increased neuroexcitatory activities
34 | increased fluid retaining activities
35 | decreased serum active metabolites
36 | increased immunosuppressive activities
37 | increased bleeding
38 | decreasedstimulatory activities
39 | increased myelosuppression
40 | decreased vasoconstricting activities
41 | increased thrombogenic activities
42 | increased analgesic activities
43 | increased anticholinergic activities
44 | increased absorption
45 | increased sedation and somnolence
46 | increased rhabdomyolysis
47 | increased hyperkalemia
48 | increased hepatotoxic
49 | increased myopathic rhabdomyolysis activities
50 | decreased absorption
51 | increased hyponatremic activities
52 | increased vasopressor activities
53 | increased excretion rate
54 | decreased neuromuscular blocking activities
55 | increased hypersensitivity
56 | increased hyperglycemic activities
57 | increased hypocalcemic activities
58 | increased myelosuppressive activities
59 | increased vasodilatory activities
Table 10: Indexed interactions for holdout evaluation. The first column
contains the index assigned to the interaction inside the code, and the second
column contains the keyword phrase extracted for simpler calculation
(redundant words and words that are redundant because the problem is symmetric
were removed). The keyword sentences were extracted from full interaction
sentences; ”other” interactions can be seen in our comprehensive extraction of
interactions [36].

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