Automated QSAR

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  • 1. QSAR Process requires many choices
    Which descriptors?
    Which modelling algorithm?
    What model testing strategy?
    Quality of result depends on make correct choices
    All runs are different
    Discovery Bus manages this process
    Apply everything approach
    QSAR choices
  • 2. The Discovery Bus
    Manages the many model generation paths
    Random split
    80:20 split
    Partition training & test data
    Java CDK descriptors
    C++ CDL descriptors
    Calculate descriptors
    Correlation analysis
    Genetic algorithms
    Random selection
    Select descriptors
    Linear regression
    Neural Network
    Partial Least Squares
    Classification Trees
    Build model
    Add to database
  • 3. Filter Features
    QSAR Agent
    Model Build
    Filter feature
    request ...
    responses
    Model build
    request ...
    responses
    Calculate descriptors
    request ...
    responses
    Calculate Descriptors
  • 4. Filter Features
    QSAR Agent
    Model Build
    Calculate
    Descriptors
    Filter feature
    request ...
    responses
    responses
    Model build
    request ...
    responses
    Calculate descriptors
    request ...
    responses
    responses
    responses
    Calculate Descriptors
  • 5. Industrial Scale QSAR
    Predict likely properties based on similar molecules
    CHEMBL Database:
    data on 622,824 compounds,
    collected from 33,956 publications
    WOMBAT Database:
    data on 251,560 structures,
    for over 1,966 targets
    WOMBAT-PK Database:
    data on 1230 compounds,
    for over 13,000 clinical measurements
    Project Junior (Newcastle University & Microsoft Research)
    10,000 datasets gave 750,000 QSAR models in 3 weeks using 100 Azure Cloud Servers
  • 6. “The Discovery Bus is not a tool for users. It is a system for doing drug design independent of any user”
    The ambition is a step-change in productivity arising from breaking the link between human effort and drug discovery output.