Table 1 A Summary of KG construction approaches for drug discovery, drug repurposing, and adverse drug reaction

[46]

Drug discovery

Manual and fuzzy matching

Schema-based

Wikidata, DrugBank^{Footnote 13}, WedMD, and GoodRx

N/A

R, P

• Lack of statistics on the resultant KG.

• Limited discussion on the Ontology design

• The evaluation of the proposed model emphasized on KG embedding rather than the resultant integrated KG.

[47]

Drug discovery for COVID-19

Manual construction based on six KGs obtained from the literature

Schema-based

Literature on COVID-19

#n: 100,00

#e: 670,000

AUC, and AUPRC

• Insufficient discussion on the mechanism followed to integrate the incorporated KGs,

• The evaluation of Att-GCN-DDI is limited and not detailed.

[48]

Drug discovery

Manual extraction based on Bio2RDF KG

Hybrid

Bio2RDF^{Footnote 14}

#n: 2,947,140

#e: 10,131,654

AUC, AUPR, F1

• Inadequate discussion on the construction of drug KG.

[55]

Drug repurposing

Algorithms developed at BenevolentAI^{Footnote 15} and part of their IP

Hybrid

Structured and unstructured resourced including Literature on COVID-19

#n: millions

#e: hundreds of millions

Case study

• There is no detailed discussion on the mechanism followed to construct BenevolentAI graph.

• The evaluation was merely measured by case study.

[56]

Drug repurposing

Coarse- and fine-grained entity extraction

Manually based on CTD and MeSH

Schema-based

Multimodal scientific literature (CTD^{Footnote 16})

#n: 67,217

#e: 77,844,574

Case study on Drug Repurposing

Report Generation

• Although the proposed framework demonstrated success in tackling the quantity issue of relevant KG resources, the quality issue was not properly evaluated to demonstrate its effectiveness.

• Observed bias in training and development data, source, and test queries.

[57]

Drug repurposing

Manually encoded in Biological Expression Language

Schema-free

PubMed, LitCovid^{Footnote 17}, EuropePMC, etc.

#n: 4,016

#e: 10,232

Case study (Gene Expression Analysis)

• The mechanism followed to construct the KG (manual-based) is poor in terms of scalability.

[58]

Drug repurposing

Cross-referencing

Schema-based

PharmGKB, TTD, KEGG DRUG, DrugBank, SIDER^{Footnote 18}, and DID

N/A

Case study (Finding drug–disease pairs)

• The proposed data model that was used for data integration can be improved by using formal domain ontology toward better conceptualizing the domain.

[67]

Prediction of adverse drug reactions

Direct construction from structural databases

Schema-free

DrugBank database and SIDER database

#n: 12,473

#e:154,239

P, R, F1, AUC, and a case study on Drug-induced liver injury

• The KG skips information of drugs and protein target,

• The scope of information perceived by entities can be enlarged by using longer path in the KG as the input of Word2Vec model.

[66]

Prediction of adverse drug reactions

Direct construction from structural databases

Schema-free

DrugBank, SIDER

#n: 5,828

#e: 70,382

AUC and case study(Validation in EHRs and Eudravigilance)

• No clear discussion on KG construction approach,

• Insufficient discussion on the methodology followed in the ML benchmark comparison.

[68]

Discovery of adverse drug reactions

cTAKES^{Footnote 19}

naive Bayesian model

Schema-based

MEDLINE

#n: 9,699

#e: 139,254

co-occurrence analysis and Case study (Osimertinib)

• The computed drug-biomarker groupings cannot differentiate between a drug-treatment relationship,

• The study lacks the attention to drug-drug interaction,

• lack of rationale on using the entity extraction method

[75]

Drug action

Automatically using rule-based approach

Schema-free

Medical papers

#n: 40,963

#e: 57,865

R, and accuracy

• Lack of verification to the textual prio KG construction.

• Limited comparison with currently exiting similar KGs.