Target Discovery in the age of Big Data and AI, Elix, CBI 2021

Transcript

1. Target Discovery in the age of
   Big Data and AI
   Nazim Medzhidov, PhD
   Research Engineer
   Elix Inc.
   2021/10/26
   

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2. 2
   Overview
   ● Background
   ● Problem components
   ● Data sources
   ● Approaches
   ● Challenges and opportunities
   ● Concluding remarks
   

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3. 3
   Background
   ● Selection and prioritization of novel drug targets in drug discovery process is a central problem
   ● Choice of the right target can significantly impact the probability of advancing the drug to clinical
   development and clinical trial success
   ● Multi-parameter optimization problem:
   ○ Target role in the disease progression
   ○ Novelty
   ○ Target Druggability
   ○ Safety
   ○ …..
   ○ …..
   

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4. Grand challenge(s)
   4
   ● Out of ~20,000 expressed proteins in human genome (~1.5%) about 2,000-3,000 (10-15%)
   are considered disease-related
   ● Less than 700 of these proteins have been effectively targeted
   ● Majority of the disease-related protein targets remain undruggable, lacking distinct binding
   motifs/pockets for small molecules to bind
   ● Focusing on protein targets only?
   How to identify novel targets? Can AI be used to facilitate
   this process?
   

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5. 5
   Problem Components
   Required
   Components
   Methodology
   Data
   Data Structure?
   Which
   Representation?
   Architecture Optimization
   

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6. 6
   Data
   A large number of publicly available resources:
   • UniProt
   • Open Targets
   • Therapeutic Target Database (TTD)
   • The Drug Gene Interaction database (DGIdb)
   • Target Central Resource Database (TCRD)
   • Large-scale human genomics, proteomics, and metabolomics data sets
   

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7. 7
   Data (2)
   ● Combining all this information in a single place for further analysis or prioritization of a list of
   targets can become a daunting task.
   ● Each data source specializes in different areas such as protein expression, disease association or
   pharmacology
   ● Navigating through a myriad of cross-references in order to paint an accurate portrait of a
   potential target is necessary and challenging for researchers
   How do we structure these
   massive data?
   

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8. 8
   Data (3)
   ● Biological Networks:
   ○ Protein-Protein interaction networks
   ○ Gene interaction networks
   ○ Metabolic networks
   ● These type of biological network data can be represented as graphs that capture interactions
   between nodes for subsequent analysis using computational approaches
   ● ML techniques can be helpful in processing these data and in identifying features related to the
   successful target selection
   

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9. 9
   Deep Learning
   ● One of the strengths of deep learning is its ability to detect complex patterns in the data making
   it well suited for application in bioinformatics, where the data represent complex, interdependent
   relationships between biological entities and processes, which are often intrinsically noisy and
   occurring at multiple scales
   ● DL methods have been extended to graph-structured data, making it a promising technology to
   tackle these biological network analysis problems
   

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10. 10
    Potential steps
    ● Predicting unknown function of a node (gene/protein/metabolite) in the network
    ● Link prediction: identification of interactions between nodes
    ● Combining predictions on separate tasks
    

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11. 11
    Concluding Remarks
    ● Novel target identification remains a challenging, yet central problem in drug discovery research
    ● Involvement of a myriad of parameters contributes to the problem complexity
    ● Need of robust ways to process large biological network data and suggest novel target
    candidates
    ● Complex biological network data can be structured as a graph for subsequent analyses using DL
    ● Community effort has mostly been focused on drug-target interaction prediction problem with
    only a few studies focused on novel target identification
    

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12. 12
    Q&A
    

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13. 株式会社Elix
    http://ja.elix-inc.com/
    13
    

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