imputation of assay activity data using deep learninggjc29/talks/basel2019.pdf · 2019-01-25 ·...
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Imputation of assay activity datausing deep learning
Tom Whitehead, Peter Hunt, Matt Segall, Gareth Conduit
Alchemite™ machine learning tool to
Reduce the need for experiments and accelerate drug discovery
Utilise all available information: computer simulations and real-life measurements
Impute values from sparse data
Broadly applicable with proven applications in drug design and materials discovery
Novartis dataset to benchmark machine learning
159 kinase proteins, 10000 compounds, data 5% complete
159proteins
100
00
com
po
un
ds
Data from ChEMBLMartin, Polyakov, Tian, and Perez,
J. Chem. Inf. Model. 57, 2077 (2017)
Validate imputation of missing entries
Realistically split holdout data set, extrapolate to new chemical space
Impute missing entries in new chemical space
Random Realistic
Data from ChEMBLMartin, Polyakov, Tian, and Perez,
J. Chem. Inf. Model. 57, 2077 (2017)
Training
Validation
QSAR: quantitative structure-activity relationships
x3 x1 x1
Molecular weight=183 Da
Train off one column at a time
Standard methods learn descriptor-protein correlations
Train and predict one column at a time
Alchemite™ uses all available data
Include protein-protein correlations
Validate imputation of missing entries
Realistically split holdout data set, extrapolate to new chemical space, and calculate the accuracy
Alchemite™ outperforms other methods
Alchemite™
Random forest
pQSAR2
Neural net
Matrix factorize
1 10 100
-0.2
0
0.2
0.4
0.6
0.8
1
% of missing data imputed
Acc
ura
cy
Calculate probability distribution
Activity
Pro
bab
ility
de
nsi
ty
Mean prediction
Less confident prediction
Activity
Pro
bab
ility
de
nsi
ty
Activity
Mean prediction
Focus on most confident predictions
Activity
Pro
bab
ility
de
nsi
ty
Activity
Mean prediction
Reporting on only most confident predictions
Alchemite™
Random forest
pQSAR2
Neural net
Matrix factorize
1 10 100
-0.2
0
0.2
0.4
0.6
0.8
1
% of missing data imputed
Acc
ura
cy
1 10 100
-0.2
0
0.2
0.4
0.6
0.8
1
% of missing data imputed
Acc
ura
cySelect performance level
Alchemite™
Random forest
pQSAR2
Neural net
Matrix factorize
Taking Alchemite™ to market
Summary
Alchemite™ trains across all endpoints to capture
activity-activity correlations
Understand and exploit probability distribution to focus on most confident results
Impute results of missing assays to high accuracy, enabling computational screening of compounds to
identify new hits
Take Alchemite™ to market with Optibrium
gareth@intellegens.ai
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