Uncertainty in Virtual Screening, Elix, CBI 2021

Transcript

1. Uncertainty in Virtual Screening
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   Romeo Cozac, Elix Inc.
   

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2. Dataset
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   ● This project attempted to
   “rediscover”
   pharmaceutical leads that
   are active against the
   Androgen Receptor (AR)
   as antagonists. AR is an
   important receptor in
   treating prostate cancer.
   ● Collected 4810
   compounds from ChEMBL
   that had IC50 assay data
   for AR.
   ● Compound pIC50 values
   and plotted on the left. The
   compounds covered 7
   orders of magnitude,
   indicating a broad range of
   inhibitory activity.
   

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3. Model Architecture
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   ● We used a graph neural
   network to create a
   predictive model.
   ● The model interprets the
   input data as a graph of
   atoms, and assigns features
   to these atoms (atomic
   weight, charge, etc.). The
   connections between atoms
   is recorded in an adjacency
   matrix.
   ● A series of graph
   convolutional layers and fully
   connected layers learn to
   predict IC50 values from the
   atom features. Prediction
   confidence is also calculated.
   

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4. Virtual Screening
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   Dataset
   350K compounds
   IC50 ≤10nM
   σ ≤0.1
   53 compounds
   Shortlist
   53 compounds
   ● Using the predictive model, the IC50
   values of 350,000 molecules were
   predicted. The 350,000 molecules are
   a virtual library that is being used to
   screen for lead candidates.
   ● Based on the predicted values of
   IC50, molecules that had an IC50 <
   10 nM (pIC = 9) and sigma < 0.1
   passed. This filter led to 53
   compounds out of the original
   350,000 compounds.
   ● Of these 53 compounds,
   approximately ⅔ were patented,
   indicating that the compounds are
   used as a drug in some application.
   The remaining ⅓ were not patented,
   suggesting possible lead candidates.
   

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5. Model Performance
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   R2 Score Spearman R MAE RMSE
   0.616 0.776 0.551 0.755
   ● The quality of the predictive model was assessed
   by comparing predicted values of pIC50 with
   ground truth values.
   ● These values are created by separating the original
   AR pIC50 dataset into training and testing sets, and
   assessing the model on the test set.
   ● R2 Score and Spearman R measure correlation
   (1.0 = perfect correlation). MAE (Mean Absolute
   Error) and RMSE (Root Mean Square Error) are
   measures of the distance between the predicted
   and ground truth values (lower is better).
   ● The model has good correlation and error values
   given the heterogeneity of the input data.
   

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6. Model Performance considering uncertainty
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   R2 Score Spearman R MAE RMSE
   0.616 0.776 0.551 0.755
   0.661 0.800 0.518 0.702
   ● The model also gives a measure of prediction
   uncertainty. The plot on this slide shows predictions
   with uncertainty over the 0.1 threshold as red.
   Qualitative examination of the plot shows many of
   the red points show large differences between the
   prediction and ground truth values.
   ● The same correlation and error metrics were
   calculated when the red points are excluded.
   ● The updated metrics (underlined in the table below)
   show that excluding points with high uncertainty
   leads to notable improvements in correlation and
   error values.
   

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7. Uncertainty and recognition of out-of-distribution molecules
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   Ignoring uncertainty Considering uncertainty
   ● This slide shows the
   differences in molecules
   when ignoring and when
   considering uncertainty.
   ● The molecular structures
   when ignoring uncertainty
   tend to have molecules
   that are out of distribution
   for drug-like molecules.
   ● Considering uncertainty
   leads to more drug-like
   molecules.
   

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8. Shortlist
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   ● Based on predicted AR IC50 values, a
   shortlist of lead candidates was
   created from the 350,000 screened
   molecules
   ● After applying pharmacophore and
   drug-likeness filters, the following
   molecules were identified
   ● The molecule in the green box below
   was identified as a novel compound
   for AR targeting
   

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