Predicting Activity Cliffs - Can Machine Learning Handle Special Cases?
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Predicting Activity Cliffs - Can Machine Learning Handle Special Cases?

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  • Outliers in a cliff prediction model are not as severe since SALI changes more slowly than just activity differences
  • For SALI = 0, had to set log10(SALI) = 0Similar performance if we use SALI and not log10(SALI) at least more % variance is explained. Still fail on most significant cliffs

Predicting Activity Cliffs - Can Machine Learning Handle Special Cases? Predicting Activity Cliffs - Can Machine Learning Handle Special Cases? Presentation Transcript

  • Predicting Activity Cliffs - Can We Use Machine Learning for Special Cases?
    Rajarshi Guha
    NIH Center for Translational Therapeutics
    August 4, 2011
    Joint Statistical Meeting, Miami Beach
  • Outline
    Structure-activity landscapes
    Characterization
    Prediction
  • Structure Activity Relationships
    Similar molecules will have similar activities
    Small changes in structure will lead to small changes in activity
    One implication is that SAR’s are additive
    This is the basis for QSAR modeling
    Martin, Y.C. et al., J. Med. Chem., 2002, 45, 4350–4358
  • Exceptions Are Easy to Find
    Ki = 39.0 nM
    Ki = 1.8 nM
    Ki = 10.0 nM
    Ki = 1.0 nM
    Tran, J.A. et al., Bioorg. Med. Chem. Lett., 2007, 15, 5166–5176
  • Structure Activity Landscapes
    Rugged gorges or rolling hills?
    Small structural changes associated with large activity changes represent steep slopes in the landscape
    But traditionally, QSAR assumes gentle slopes
    Machine learning is not very good for special cases
    Maggiora, G.M., J. Chem. Inf. Model., 2006, 46, 1535–1535
  • Characterizing the Landscape
    A cliff can be numerically characterized
    Structure Activity Landscape Index (SALI)
    Cliffs are characterized by elements of the matrix with very large values
    Guha, R.; Van Drie, J.H., J. Chem. Inf. Model., 2008, 48, 646–658
  • Visualizing SALI Values
    The SALI graph
    Compounds are nodes
    Nodes i,j are connected if SALI(i,j) > X
    Only display connected nodes
  • What Can We Do With SALI’s?
    SALI characterizes cliffs & non-cliffs
    For a given molecular representation, SALI’s gives us an idea of thesmoothness of the SAR landscape
    Models try and encodethis landscape
    Use the landscape to guidedescriptor or model selection
  • Descriptor Space Smoothness
    Edge count of the SALI graph for varying cutoffs
    Measures smoothness of the descriptor space
    Can reduce this to a single number (AUC)
  • Feature Selection Using SALI
    Instead of fingerprints, we use molecular descriptors
    SALI denominator now uses Euclidean distance
    2D & 3D random descriptor sets
    None are really good
    Too rough, or
    Too flat
    2D
    3D
  • Measuring Model Quality
    A QSAR model should easily encode the “rolling hills”
    A good model captures the most significantcliffs
    Can be formalized as
    How many of the edge orderings of a SALI graph does the model predict correctly?
    Define S (X ), representing the number of edges correctly predicted for a SALI network at a threshold X
    Repeat for varying X and obtain the SALI curve
  • SALI Curves
  • Predicting the Landscape
    Rather than predicting activity directly, we can try to predict the SAR landscape
    Implies that we attempt to directly predict cliffs
    Observations are now pairs of molecules
    A more complex problem
    Choice of features is trickier
    Still face the problem of cliffs as outliers
    Somewhat similar to predicting activity differences
    Scheiber et al, Statistical Analysis and Data Mining, 2009, 2, 115-122
  • Motivation
    Predicting activity cliffs corresponds to extending the SAR landscape
    Identify whether a new molecule will perform better or worse compared to the specific molecules in the dataset
    Can be useful for guiding lead optimization, but not necessarily useful for lead hopping
  • Predicting Cliffs
    Dependent variable are pairwise SALI values, calculated using fingerprints
    Independent variables are molecular descriptors – but considered pairwise
    Absolute difference of descriptor pairs, or
    Geometric mean of descriptor pairs

    Develop a model to correlate pairwise descriptors to pairwise SALI values
  • A Test Case
    We first consider the CavalliCoMFA dataset of 30 molecules with pIC50’s
    Evaluate topological and physicochemical descriptors
    Developed random forest models
    On the original observed values (30 obs)
    On the SALI values (435 observations)
    Cavalli, A. et al, J Med Chem, 2002, 45, 3844-3853
  • Double Counting Structures?
    The dependent and independent variables both encode structure.
    But pretty low correlations between individual pairwisedescriptors and the SALI values
  • Model Summaries
    Original pIC50
    RMSE = 0.97
    SALI, AbsDiff
    RMSE = 1.10
    SALI, GeoMean
    RMSE = 1.04
    All models explain similar % of variance of their respective datasets
    Using geometric mean as the descriptor aggregation function seems to perform best
    SALI models are more robust due to larger size of the dataset
  • Test Case 2
    Considered the Holloway docking dataset, 32 molecules with pIC50’s and Einter
    Similar strategy as before
    Need to transform SALI values
    Descriptors show minimal correlation
    Holloway, M.K. et al, J Med Chem, 1995, 38, 305-317
  • Model Summaries
    Original pIC50
    RMSE = 1.05
    SALI, AbsDiff
    RMSE = 0.48
    SALI, GeoMean
    RMSE = 0.48
    The SALI models perform much poorer in terms of % of variance explained
    Descriptor aggregation method does not seem to have much effect
    The SALI models appear to perform decently on the cliffs – but misses the most significant
  • Model Summaries
    Original pIC50
    RMSE = 1.05
    SALI, AbsDiff
    RMSE = 9.76
    SALI, GeoMean
    RMSE = 10.01
    With untransformed SALI values, models perform similarly in terms of % of variance explained
    The most significant cliffs correspond to stereoisomers
  • Test Case 3
    38 adenosine receptor antagonists with reported Ki values; use 35 for training and 3 for testing
    Random forest model on the SALI values performed reasonable well (RMSE = 7.51, R2=0.62)
    Upper end ofSALI rangeis better predicted
    Kalla, R.V. et al, J. Med. Chem., 2006, 48, 1984-2008
  • Test Case 3
    • The dataset does not containing really big cliffs
    • Generally, performance is poorer for smaller cliffs
    For any given hold out molecule, range of error in SALI prediction is large
    Suggests that some form of domain applicability metric would be useful
  • Model Caveats
    Models based on SALI values are dependent on their being an SAR in the original activity data
    Scrambling results for these models are poorer than the original models but aren’t as random as expected
  • Conclusions
    SALI is the first step in characterizing the SAR landscape
    Allows us to directly analyze the landscape, as opposed to individual molecules
    Being able to predict the landscape could serve as a useful way to extend an SAR landscape
  • Acknowledgements
    John Van Drie
    Gerry Maggiora
    MicLajiness
    JurgenBajorath