Evotec - How can Knowledge Graphs support Druh Discovery
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The document discusses how knowledge graphs can support drug discovery by integrating public and internal knowledge towards a comprehensive understanding of diseases and therapies. It describes challenges like the diversity of life science data from various sources and the complexity of the literature. Knowledge graphs can help address these challenges by harmonizing heterogeneous data, combining public and internal knowledge, and enabling novel insights through graph algorithms. This unified knowledge graph approach allows for applications like defining molecular disease spaces, identifying target genes specific to conditions of interest, and sharing insights to support new therapy development.
Evotec - How can Knowledge Graphs support Druh Discovery
- 1. Polina Shpudeiko Scientific Programmer, Computational Biology How can Knowledge Graphs Support Drug Discovery? Graph Summit Neo4j, Frankfurt, October 10th, 2023
- 2. PAGE 2 Agenda 1. Why do we need knowledge graphs in drug discovery? 2. How can we build them to solve our challenges? 3. What can be done with the power of graphs? 4. Where will it lead us?
- 3. PAGE 3 Why do we need knowledge graphs in drug discovery?
- 4. PAGE 4 Integration of public and internal knowledge Towards a comprehensive understanding of diseases and therapies Public knowledge Internal knowledge
- 5. PAGE 5 The life science space is diverse Navigating the complexity of biology, chemistry and clinics Genes Proteins Mutations Tissues Pathways Cell types Compounds Diseases
- 6. PAGE 6 The life science space is diverse Various databases capture the structured public knowledge And these are only a few examples... Genes Proteins Compounds Mutations Tissues Diseases Pathways Cell types
- 7. PAGE 7 Literature space is adding more complexity Scientific articles are the key for sharing novel knowledge Statistics There are approximately 30,000 journals in the world with an increasing rate of 5-7% per year The rapidly evolving landscape of scientific research, marked by an annual influx of approximately 2 million new articles There are already 36 million articles in the open source database for articles These ideas can be extracted by utilising natural language processing (NLP)
- 8. PAGE 8 The drug discovery process as our in-house data source Each step generates novel insights and requires dedicated expertise Clinics Disease biology Screening and compound chemistry Target ID and validation Hit ID and optimization Lead optimisation Candidate selection Candidate profiling Clinical trial Areas of interest
- 9. PAGE 9 Mission – harmonize data, understand diseases and support the development of new therapies Integration of public and internal knowledge Combinig it together will lead us towards novel targets discovery at Evotec Public knowledge Internal knowledge
- 10. PAGE 10 How can we build them to solve our challenges?
- 11. PAGE 11 Challenge of managing diverse data
- 12. PAGE 12 Experimental data Public ontologies space is not standardized Public ontologies space is incomplete Ontologies do not cover cutting-edge science and novel associations Multiple distinct ontologies for Diseases The ontological space is complex and incomplete Stable and reliable data models and custom ontologies are essential
- 13. PAGE 13 Knowledge graph as harmonisation tool Bringing together heterogeneous biological data in one place • 15 databases • 30 mln nodes and 100 mln connections and counting • Deep understanding of ontologies (hierarchical/ semantical connections between different entities) was re-quired for har- monisation of diseases and traits
- 14. PAGE 14 • Extract public know- ledge from scientific articles with NLP • Overlay de-novo mined knowledge with ontological database space Knowledge graph as integration tool Integration of literature data with NLP approaches Pathways Tissue Genes Compounds Diseases Traits Mutations NLP Article
- 15. PAGE 15 PMID prevent Depressive disorder BAIAP2 Knowledge graph • Natural Language Processing (NLP) extracts keys mentioned in the articles • NLP-powered search engines can understand the context and semantics of queries • Ontologies help to harmonize the extracted knowledge in one graph Knowledge graph as tool for integration Integration of literature data with NLP approaches
- 16. PAGE 16 • Signatures are a representation of internal experimental knowledge • Example: genes which are changing their expression in response to therapy Pathways Tissue Genes Compounds Diseases Traits Mutations NLP Article Knowledge graph as tool for unification Combining internal knowledge with public data Signatures
- 17. PAGE 17 What can be done with the power of graphs?
- 18. PAGE 18 Disease tree ontology: Fibrosis Integration of Public and Internal data in graph Using public knowledge for target identification from patient-derived signatures Graph representation of a Signature
- 19. PAGE 19 • Expression data in a large patient cohort can be enriched with hetero- geneous data from public (NLP, pathways, cell types) and internal (in-house signatures) resources • This allows us to understand better underlying mechanisms that drive the disease Patient stratification based signature Integration of patients signatures and experimental models Translational research from animal to human In vivo signatures In vitro signatures
- 20. PAGE 20 Kidney Diseases Genes Any Connected Disease • Disease space is defined by Parent term of ontology (Kidney Disease) • All NLP co-mentions of child diseases to genes are collected • To determine specificity all other diseases that were co-mentioned are added • Co-mention edges are weighted by the number of unique articles Defining molecular disease spaces Based on internal experimental data and NLP-mined external knowledge
- 21. PAGE 21 Genes associated with genetic kidney diseases Genes are involved in the infectious diseases Neoplasms Infectious Diseases Kidney Diseases Defining molecular disease spaces Identification of kidney-specific genes in the embeddings of kidney disease space Polycystic Kidney Diseases Genes which are taking part in cancer and not specific to disease space of interest Genes which drive kidney diseases are the most important target candidates
- 22. PAGE 22 Sharing of the data insights Neodash solution for internal knowledge sharing
- 23. PAGE 23 Where will it lead us?
- 24. PAGE 24 PAGE 24 Summary and outlook • Graphs are powerful tools for data harmonization in diverse life science space – bringing ontologies together • Alliance between public and internal knowledge into one place with graphs – allowed to characterize internal signatures in the most efficient way • Application of diverse graph algorithms helps us understand hidden insights in our data – identification of specific genes for the disease of interest with the highest potential for Target ID
- 25. Polina Shpudeiko Scientific Programmer, Computational Biology polina.shpudeiko@evotec.com