1) The document proposes a deep learning framework called DeepLGF to predict drug-drug interactions by combining local and global feature extraction from biomedical knowledge graphs. 2) DeepLGF uses graph neural networks and knowledge graph embedding methods to extract local drug features from chemical structures and biological functions, and global features from the relationships between drugs and other biological entities. 3) Experimental results on prediction tasks using several drug interaction datasets demonstrate that DeepLGF outperforms other state-of-the-art models and has promising applications in drug development and clinical use.

1. Hyo Eun Lee
Network Science Lab
Dept. of Biotechnology
The Catholic University of Korea
E-mail: gydnsml@gmail.com
2023.08.15
Bioinformatics 2022

2. 1
 Introduction
• Previous Research
• Purpose and Goal
 Method
• Representation of drug CS
• Method of global feature extracting
• BF feature learning module
• Strategies of information integration
 Experimental
• Dataset
• Baseline models and Evaluation criteria
 Results
 Conclusion

3. 2
1. Introduction
Previous Research
• Combining drugs can lead to synergistic therapeutic effects
, but can also have side effects such as patient death due to potential interactions
• Traditional clinical experimentation methods are time-consuming and cumbersome
• DDI prediction models need to evolve to simplify the detection process and reduce
experimental complexity

4. 3
1. Introduction
Previous Research
• Previous DDI prediction methods can be separated into
heterogeneous network-based method and homogeneous network-based method
• A homogeneous network-based method considers only one type of node and edge
, which limits its inductive capability and prediction performance
• The heterogeneous network-based method overcomes the limitations of homogeneous
networks by considering different types of nodes and edges
• Recently, KG was also proposed to use data integration
and federation techniques to represent complex relationships between objects
• However, focusing on just simple data fusion, triple truths, can limit inductive capability

5. 4
1. Introduction
Purpose and Goal
• Proposing a deep learning framework called DeepLGF
to predict drug-drug interactions (DDIs) by aggregating local-global multi-information
based on biomedical knowledge graph (BKG)
• Propose an embedding method that includes chemical structure (CS) and
biological function (BF) as well as interactions with other biological entities
• Constructed a drug-receptor graph to fully train the BFs
and used BFGNN to generate drug embedding vectors
• Integrate multiple information without losing the ability to induce
, but make full use of the unique characteristics of BKGs as local information

6. 5
1. Introduction
Purpose and Goal

7. 6
2. Method
Representation of drug CS
• Extract features embedded in the drug structure using a method similar to the text embedding
method
• Propose a PB-DBOW method similar to PV-DBOW to predict words within a document
embedding vector.

8. 7
2. Method
Representation of drug CS
• PB-DBOW is based on skip-grams to compute the embedding vector through the weight matrix
of the center word
ℎ𝑐 = 𝑤𝑇
𝑥𝑐
• The loss function maximizes the probability of occurrence across the N context words for each
center word
𝐸 = log 𝑝 𝑣𝑜 𝑣𝑐 = 𝑢𝑜
𝑇ℎ𝑐 − log
𝑖∈𝑁
exp 𝑢𝑖
𝑇
ℎ𝑐
• Similarly, the method proposed in this paper learns to maximize the probability of a center word
occurring in sentences
𝐸 = log 𝑝 𝑣𝑜 𝑠𝑒𝑛𝑡𝑐 = 𝑢𝑜
𝑇
ℎ𝑐 − log
𝑖∈𝑁
exp 𝑢𝑖
𝑇
ℎ𝑐

9. 8
2. Method
Method of global feature extracting
• Using ComplEX, favorable for capturing antisymmetric relationships
, to obtain feature embedding vectors for associations between biological entities using BKGs
• Global representations can be learned through direct connections or potential flows
between nodes

10. 9
2. Method
Method of global feature extracting
• BKG Definitions
𝐺𝐾𝐺 = ℎ, 𝑟, 𝑡 ℎ, 𝑡 𝜖 𝐸, 𝑟 𝜖 𝑅 }
• Calculate the probability that an interaction exists between two objects
using relation adjacent matrices using a scoring function and a sigmoid function
𝑃 𝑌ℎ𝑖,𝑟𝑖,𝑡𝑖
= 1 = 𝜎(𝜙(𝑟𝑖, ℎ𝑖, 𝑡𝑖; 𝛩))
𝜙 𝑟𝑖, ℎ𝑖, 𝑡𝑖; 𝛩 = 𝑅𝑒
𝑘=1
𝐾
𝑤𝑟𝑘𝑒ℎ𝑘𝑒𝑡𝑘
𝐿𝑜𝑠𝑠 = min 𝑖
𝑁
log 1 + exp −𝑌ℎ𝑖,𝑟𝑖,𝑡𝑖
𝜙 𝑟𝑖, ℎ𝑖, 𝑡𝑖; 𝛩 + 𝜆||𝛩||2
2

11. 10
2. Method
BF feature learning module
• Propose BFGNN to leverage BF information to provide local details
and improve feature learning representation
• Different datasets have different amounts of BF information, so you need to build different
networks to extract features

12. 11
2. Method
BF feature learning module

13. 12
2. Method
BF feature learning module
• Map the drug-receptor matrix through an initial function
𝑓 ∶ 𝑉 → 𝑅𝑑
• A heterogeneous network can be used to generate feature A
𝑎𝑣 =
𝑟 ∈𝑅 𝑢∈(𝑁,(𝑣))
𝜎(𝑊
𝑐 𝜎 𝑊
𝑟𝑓 𝑢 + 𝑏𝑟 ∙ 𝜆𝑢,𝑣,𝑟 + 𝑏𝑐)
𝜆𝑢,𝑣,𝑟 =
𝑒 𝑢, 𝑣, 𝑟
𝑢∈(𝑁,(𝑣)) 𝑒 𝑢, 𝑣, 𝑟
𝑔 𝑣 =
𝜎 𝑊′𝑐𝑜𝑛𝑐𝑎𝑡 𝑓 𝑣 , 𝑎𝑣 + 𝑏′
𝜎 𝑊′𝑐𝑜𝑛𝑐𝑎𝑡 𝑓 𝑣 , 𝑎𝑣 + 𝑏′
2
min 𝐿𝑜𝑠𝑠 = min
𝑟 ∈𝑅 𝑣,𝑢∈𝑉
𝑒 𝑣, 𝑢, 𝑟 − 𝑔 𝑢 𝑇𝐺𝑟𝐻𝑟
𝑇𝑔(𝑣) 2

14. 13
2. Method
Strategies of information integration
1. Directly connecting-based
𝑓𝑖 = 𝑐𝑜𝑛𝑐𝑎𝑡𝑒𝑛𝑎𝑡𝑒 𝑢𝑖, 𝑣𝑖, 𝑤𝑖
2. Crossing matrix-based
𝐶𝑖
′
= 𝑢𝑖 ⊗ 𝑣𝑖
𝐶𝑖 = 𝐶𝑖
′
⊗ 𝑤𝑖
3. Average-based
𝑓𝑖 = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑢𝑖, 𝑣𝑖, 𝑤𝑖 =
𝑢𝑖 + 𝑣𝑖 + 𝑤𝑖
3
4. Self-attention-based
𝜃′ = 𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛 𝑄, 𝐾, 𝑉 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥
𝑄𝐾𝑇
𝑑𝑘
𝑉

15. 14
3. Experimental
Data set
• DrugBank
: Online database, comprehensive resource containing chemical, pharmacological and
pharmaceutical data
• SMILES - used as drug CS information
• Biological heterogeneous associations
- extract BF information, including protein targets, enzymes, transporters, and carriers
• DRKG
: Comprehensive BKG used for DDI prediction with BKG and for drug discovery and drug
repurposing
• used to extract information about GI, drug-drug interactions, drug-target interactions
, and other biomedical entities and relationships

16. 15
3. Experimental
Data set
• To avoid the label leakage problem, we chose 28 edge types and 12 nodes

17. 16
3. Experimental
Data set
• Since different heterogeneous networks need to be constructed depending on the BF
information, the dataset is divided into four main categories.

18. 17
3. Experimental
Data set
• Paper tests performance on three prediction tasks
• 𝑇𝑘𝑘 : randomly select pairwise drugs
• 𝑇𝑘𝑛 : predicting interactions between known drugs
and new drugs
• 𝑇𝑛𝑛 : predicting interactions between new drugs

19. 18
3. Experimental
Evaluation criteria
𝐴𝑐𝑐. =
𝑇𝑃 + 𝑇𝑁
𝑇𝑁 + 𝑇𝑃 + 𝐹𝑁 + 𝐹𝑃
𝑃𝑟𝑒𝑐. =
𝑇𝑃
𝑇𝑃 + 𝐹𝑃
𝑆𝑒𝑛. =
𝑇𝑃
𝑇𝑃 + 𝐹𝑁
𝐹1 =
2 × 𝑃𝑟𝑒𝑐. × 𝑆𝑒𝑛.
𝑃𝑟𝑒𝑐. +𝑆𝑒𝑛
𝑀𝐶𝐶 =
𝑇𝑃 × 𝑇𝑁 − 𝐹𝑃 × 𝐹𝑁
(𝑇𝑃 + 𝐹𝑃) × (𝑇𝑁 + 𝐹𝑁) × (𝑇𝑁 + 𝐹𝑃) × (𝑇𝑃 + 𝐹𝑁)

20. 19
4. Results
Assessment of prediction ability

21. 20
4. Results
Assessment of prediction ability

22. 21
4. Results
Ablation experiments

23. 22
4. Results
Comparison of different methods for extracting GI

24. 23
4. Results
Comparison of machine learning classifiers

25. 24
4. Results
Comparison of other methods

26. 25
4. Results
Comparison of other methods

27. 26
4. Results
Evaluation of prediction on 𝑻𝒌𝒏 and 𝑻𝒏𝒏 tasks

28. 27
4. Results
Evaluation of prediction on 𝑻𝒌𝒏 and 𝑻𝒏𝒏 tasks

29. 28
4. Results
Case studies: cannabidiol, cisplatin and dexamethasone

30. 29
4. Results
Case studies: cannabidiol, cisplatin and dexamethasone

31. 30
4. Results
Case studies: cannabidiol, cisplatin and dexamethasone

32. 31
4. Results
Case studies: cannabidiol, cisplatin and dexamethasone

33. 32
5. Conclusion
Conclusion
• A deep learning framework called DeepLGF is proposed for predicting potential drug-drug
interactions (DDIs) by fully utilizing local-global information.
• The framework combines natural language processing, graph neural networks, and
knowledge graph embedding methods to extract local and global features from biomedical
knowledge graphs.
• The authors also design four aggregation methods to fuse local-global features and
achieve advanced embedding of drugs.
• The performance of DeepLGF is evaluated based on several prediction tasks and datasets,
and the results show that DeepLGF outperforms state-of-the-art models.
• DeepLGF performs remarkably well and can be applied to a variety of prediction tasks,
making it promising for predicting potential DDIs in drug development and clinical
applications, this paper concludes.