Drug Repurposing Knowledge Graph (DRKG)

Drug Repurposing Knowledge Graph (DRKG) is a comprehensive biological knowledge graph relating genes, compounds, diseases, biological processes, side effects and symptoms. DRKG includes information from six existing databases including DrugBank, Hetionet, GNBR, String, IntAct and DGIdb, and data collected from recent publications particularly related to Covid19. It includes 97,055 entities belonging to 13 entity-types; and 5,869,294 triplets belonging to 106 edge-types. These 106 edge-types show a type of interaction between one of the 17 entity-type pairs (multiple types of interactions are possible between the same entity-pair), as depicted in the figure below. It also includes a bunch of notebooks about how to explore and analysis the DRKG using statistical methodologies or using machine learning methodologies such as knowledge graph embedding.

Figure: Interactions in the DRKG. The number next to an edge indicates the number of relation-types for that entity-pair in DRKG.

Download DRKG

You can directly download drkg by following commands:

wget https://dgl-data.s3-us-west-2.amazonaws.com/dataset/DRKG/drkg.tar.gz

DRKG dataset

The whole dataset contains three part:

• drkg.tsv, a tsv file containg the original drkg in the format of (h, r, t) triplets.
• embed, a folder containing the pretrained Knowledge Graph Embedding using the entire drkg.tsv as the training set.
• entity2src.tsv, a directory mapping entities in drkg to their original sources.

Pretrained DRKG embedding

The DRKG mebedding is trained using TransE_l2 model with dimention size of 400, there are four files:

• DRKG_TransE_l2_entity.npy, NumPy binary data, storing the entity embedding
• DRKG_TransE_l2_relation.npy, NumPy binary data, storing the relation embedding
• entities.tsv, mapping from entity_name to tentity_id.
• relations.tsv, mapping from relation_name to relation_id

To use the pretrained embedding, one can use np.load to load the entity embeddings and relation embeddings separately:

import numpy as np
entity_emb = np.load('./embed/DRKG_TransE_l2_entity.npy')
rel_emb = np.load('./embed/DRKG_TransE_l2_relation.npy')

Statistics of DRKG

The type-wise distribution of the entities in DRKG and their original data-source(s) is shown in following table.

Entity type	Drugbank	GNBR	Hetionet	STRING	IntAct	DGIdb	Bibliography	Total Entities
Anatomy	-	-	400	-	-	-	-	400
Atc	4048	-	-	-	-	-	-	4048
Biological Process	-	-	11381	-	-	-	-	11381
Cellular Component	-	-	1391	-	-	-	-	1391
Compound	9708	11961	1538	-	153	6348	6250	24313
Disease	-	4747	257	-	-	-	33	4920
Gene	4973	27111	19145	18316	16321	2551	3181	39220
Molecular Function	-	-	2884	-	-	-	-	2884
Pathway	-	-	1822	-	-	-	-	1822
Pharmacologic Class	-	-	345	-	-	-	-	345
Side Effect	-	-	5701	-	-	-	-	5701
Symptom	-	-	415	-	-	-	-	415
Tax	-	215	-	-	-	-	-	215
Total	18729	44034	45279	18316	16474	8899	9464	97055

The following table shows the number of triplets between different entity-type pairs in DRKG for DRKG and various datasources.

Entity-type pair	Drugbank	GNBR	Hetionet	STRING	IntAct	DGIdb	Bibliography	Total interactions
(Gene, Gene)	-	66722	474526	1496708	254346	-	58629	2350931
(Compound, Gene)	24801	80803	51429	-	1805	26290	25666	210794
(Disease, Gene)	-	95400	27977	-	-	-	461	123838
(Atc, Compound)	15750	-	-	-	-	-	-	15750
(Compound, Compound)	1379271	-	6486	-	-	-	-	1385757
(Compound, Disease)	-	77782	1145	-	-	-	-	78927
(Gene, Tax)	-	14663	-	-	-	-	-	14663
(Biological Process, Gene)	-	-	559504	-	-	-	-	559504
(Disease, Symptom)	-	-	3357	-	-	-	-	3357
(Anatomy, Disease)	-	-	3602	-	-	-	-	3602
(Disease, Disease)	-	-	543	-	-	-	-	543
(Anatomy, Gene)	-	-	726495	-	-	-	-	726495
(Gene, Molecular Function)	-	-	97222	-	-	-	-	97222
(Compound, Pharmacologic Class)	-	-	1029	-	-	-	-	1029
(Cellular Component, Gene)	-	-	73566	-	-	-	-	73566
(Gene, Pathway)	-	-	84372	-	-	-	-	84372
(Compound, Side Effect)	-	-	138944	-	-	-	-	138944
Total	1419822	335370	2250197	1496708	256151	26290	84756	5869294

Installation

Before using notebooks here, you need to install some dependencies:

Install PyTorch

Currently all notebooks use PyTorch as Deep Learning backend. For install other version of pytorch please goto Install PyTorch

sudo pip3 install torch==1.5.0+cu101 torchvision==0.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html

Install DGL (Optional)

If you want to analyze DRKG with DGL with notebooks at [drkg-with-dgl], you need to install DGL package. Currently we use the newest stable version of DGL. For install other version of DGL please goto Install DGL

sudo pip3 install dgl-cu101

Install DGL-KE (Optional)

If you want to training the model with notebooks (e.g., using Train_embeddings.ipynb or Edge_score_analysis.ipynb) at [knowledge-graph-embedding-based-analysis-of-drkg], you need to install DGL as in [install-dgl-optional] and install DGL-KE package here. Currently we use the newest stable version of DGL-KE. DGL-KE can work with DGL > 0.4.3 (either CPU or GPU)

sudo pip3 install dglke

DRKG with DGL

We provide a notebook, with example of using DRKG with Deep Graph Library (DGL). The following notebook provides an example of building a heterograph from DRKG in DGL; and some examples of queries on the DGL heterograph:

• loading_drkg_in_dgl.ipynb

Basic Graph Analysis of DRKG

We analyzed directly the structure of the extracted DRKG. Since the datasources may contain related information, we want to verify that combining the edge information from different sources is meaningful.

To evaluate the structural similarity among a pair of relation types we compute their Jaccard similarity coefficient and the overlap among the two edge types via the overlap coeffcient:

• Jaccard_scores_among_all_edge_types_in_DRKG.ipynb

Knowledge Graph Embedding Based Analysis of DRKG

We analyze the extracted DRKG by learning a TransE KGE model that utilizes the $\ell_2$ distance. As DRKG combines information from different data sources, we want to verify that meaningful entity and relation embeddings can be generated using knowledge graph embedding technology.

We split the edge triplets in training, validation and test sets as follows $90%$, $5%$, and $5%$ and train the KGE model as shown in following notebook:

• Train_embeddings.ipynb

Finally, we obtain the entity and relation embeddings for the DRKG. We can do various embedding based analysis as provided in the following notebooks:

• Relation_similarity_analysis.ipynb, analyzing the generate relation embedding similarity.
• Entity_similarity_analysis.ipynb, analyzing the generate entity embedding similarity.
• Edge_score_analysis.ipynb, evaluating whether the learned KGE model can predict the edges of DRGK
• Edge_similarity_based_on_link_recommendation_results.ipynb, evaluating how similar are the predicted links among different relation types.

Drug Repurposing Using Pretrained Model for COVID-19

We present an example of using pretrained DRKG model for drug repurposing for COVID-19. In the example, we directly use the pretrined model provided at DRKG dataset and proposed 100 drugs for COVID-19. The following notebook provides the details:

• COVID-19_drug_repurposing.ipynb

Licence

This project is licensed under the Apache-2.0 License. However, the DRKG integrates data from many resources and users should consider the licensing of each source (see this table) . We apply a license attribute on a per node and per edge basis for sources with defined licenses.