Deep Learning

for QSAR

rl403[at]cam.ac.uk

About Me

  • My name is Rich :)
  • 3rd year PhD student studying Chemistry at Cambridge University 
  • My PI is Dr. Andreas Bender
  • We have ~20 PhD students and ~5 postdocs
  • Work on many cheminformatics and bioinformatics problems, including
    • Gene expression data analysis
    • Drug repurposing and repositioning
    • Drug toxicity modelling
    • Mode of action analysis
    • Biological network analysis
    • Drug combination modelling
  • Collaborate widely with industry and academia, and growing quickly!

 

PyData

  • I learned to program 3 years ago
  • I mainly use 3 languages:
  • Python evangelist!

Deep Learning for QSAR

Deep Learning for QSAR

It's unreasonably good

  • Image recognition
  • Speech recognition
  • NLP
  • and many more...

Automatic Captioning

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, Xu et al.

arXiv:1502.03044 

 

Deep Learning for QSAR

Deep Learning for QSAR

Quantitative Structure Activity Relationship

The basic assumption for all molecule based hypotheses is that similar molecules have similar activities. This principle is also called [Quantitative] Structure–Activity Relationship ([Q]SAR).

(thanks wikipedia)

Deep Learning for QSAR

Quantitative

Regression rather than classification

Deep Learning for QSAR

Structure

atoms

bonds

Caffeine

carbon atom

Deep Learning for QSAR

Activity

Activity is a broad term...

  • Protein affinity, inhibition or activation 
  • Cell toxicity
  • Phenotypic responses
  • Physical properties (QSPR)

How are these measured?

IC50

Deep Learning for QSAR

Relationship

7.4

A QSAR model is a mapping between a chemical structure and a number.

This may also include a why as well as a what.

What is it good for?

  • Testing new drugs is expensive
  • It would be good to virtually screen out compounds that are unlikely to work
  • It would also be good to predict the mode of action of a drug

Why should we use deep learning to build QSAR models?

Feature Learning

Cat detector

Atoms

Functional Groups

Pharmacophores

Raw pixels

Edges

Facial features

Faces

Increasing complexity

Multitask learning

  • Recognising each number is its own task
  • Sharing weights between tasks allows statistical power to be shared between tasks

x 1000s

The data

Cheminformatics Data Sucks!

  • The data is almost always sparse.  Compounds are unlikely to be tested on all outputs.
  • The data is usually very noisy. Errors are often on the scale of an order of magnitude!
  • Data is sometimes wrong.  A compound can interfere with how an assay works, giving false readings.
  • Data is often unevenly distributed. Many compounds tend to be derived from a common scaffold.
  • Data is inconsistent.  Even for the same task, data is usually from different assays, measured using different techniques.

ChEMBL

  • ChEMBL collects open access data.
  • Cleans the data.
  • Links to standard identifiers.
  • Provided in a relational format:
  • 250 000 compounds
  • 500 000 activities
  • 710 proteins
  • 0.24% density

number of compounds per protein

Data Summary

Featurization

0: C

1: C, N, N

2: C, C, C, C, N

8201

5

Repeat for all atoms!

Circular Fingerprints

hash

modulo-2048

1

1

The Modelling

Classification Model

from keras.models import Sequential
from keras.layers import Dense, Dropout, BatchNormalization
from keras.regularizers import WeightRegularizer
from keras.optimizers import Adam

model = Sequential([
        Dense(2000, input_dim=2048, init='he_normal', activation='relu'),
        BatchNormalization(),
        Dropout(0.5),
        Dense(2000, init='he_normal', activation='relu'),
        BatchNormalization(),
        Dropout(0.5),
        Dense(581, init='he_normal', activation='sigmoid')])

model.compile(optimizer=Adam(lr=0.0005), loss='binary_crossentropy')
L(\theta) = y_{true} \log(y_{pred}) + (1 - y_{true}) \log(1 - y_{pred})
L(θ)=ytruelog(ypred)+(1ytrue)log(1ypred)L(\theta) = y_{true} \log(y_{pred}) + (1 - y_{true}) \log(1 - y_{pred})

Binary crossentropy:

hist = model.fit(X_train, Y_train, 
                 nb_epoch=250, batch_size=2048, 
                 class_weight=Y_train.sum(axis=0),
                 callbacks=[ModelCheckpoint('classification.h5')],
                 validation_data=(X_valid, Y_valid))

Training

precision: 0.6243
   recall: 0.6702
       f1: 0.6465
      mcc: 0.6464
  roc_auc: 0.9785
   pr_auc: 0.6654

Comparison to other techniques

Did it really work?

  • Caffeine is in the test set, so the model has never seen it before.
  • Let us try to predict for it:
>>> caff_pred = pd.Series(model.predict(caff_fp)[0], index=Y.columns)
>>> caff_pred = caff_pred.sort_values(ascending=True)
target_id
P29275    0.809085     # Adenosine receptor a2b
P29274    0.428097     # Adenosine receptor a2a
P21397    0.295609     # Monoamine oxidase A
P27338    0.241524     # Monoamine oxidase B
P33765    0.076975     # Adenosine receptor A3
dtype: float64

Identifying features

Extracting information from a black box, using a black box!

0       0
1       0
2       0
3       1
4       0
       ..
2045    0
2046    1
2047    0
Name: paclitaxel, dtype: uint8
0       0
1       0
2       0
3       0
4       0
       ..
2045    0
2046    1
2047    0
Name: caffeine, dtype: uint8

0.809

0.740

+0.069 difference

Adenosine a2b

Monoamine oxidase

Regression Model

from keras.models import Sequential
from keras.layers import Dense, Dropout, BatchNormalization
from keras.regularizers import WeightRegularizer
from keras.optimizers import Adam

model = Sequential([
        Dense(2000, input_dim=2048, init='he_normal', activation='relu'),
        Dropout(0.5),
        BatchNormalization(),
        Dense(2000, init='he_normal', activation='relu'),
        Dropout(0.5),
        BatchNormalization(),
        Dense(710, init='he_normal', activation='linear')])

model.compile(optimizer=Adam(0.0001), loss=sum_abs_error, metrics=[r2])
L(\theta) = \sum_{t \in tasks} \big|y_{true} - y_{pred}\big|
L(θ)=ttasksytrueypredL(\theta) = \sum_{t \in tasks} \big|y_{true} - y_{pred}\big|

Sum absolute error:

Only one problem...

NaN

Fixing the loss function

K.is_nan = T.isnan                       # tf.is_nan
K.logical_not = lambda x: 1 - x     # tf.logical_not

def sum_abs_error(y_true, y_pred):
    valids = K.logical_not(K.is_nan(y_true))
    costs_with_nan = K.abs(y_true - y_pred)
    costs = K.switch(valids, nan_cost, 0)
    return K.sum(costs, axis=-1)
  • Missing values in our target variables cause the NaN.  
  • These should not contribute to the cost (the loss for those targets should be zero).
hist = model.fit(X_train, Y_train, 
                 nb_epoch=250, batch_size=2048, 
                 class_weight=Y_train.sum(axis=0),
                 callbacks=[ModelCheckpoint('classification.h5')],
                 validation_data=(X_valid, Y_valid))

train

valid

test

Comparison to other techniques

Neural Network Random Forest
R2 0.722 0.528
MSE 0.546 0.707

Only for Dopamine D2 - receptor

Estimated error in the data ~0.4, so this is good!

Transfer Learning

model = Sequential([
        Dense(2000, input_dim=2048, init='he_normal', activation='relu'),
        Dropout(0.5),
        BatchNormalization(),
        Dense(2000, init='he_normal', activation='relu'),
        Dropout(0.5),
        BatchNormalization(),
        Dense(20, init='he_normal', activation='linear')])

model.compile(optimizer=Adam(0.0001), loss=sum_abs_error)

model.fit(X_train, Y_train[selected_targets])

new_model = Model(inputs=model.inputs[0], outputs=model.layers[-4].output)

new_model.compile('sgd', 'use')

X_deep = new_model.predict(X_train)

Acknowledgements

Andreas Bender

Günter Klambauer

The Bender group

PyData sponsors

Questions?

Deep Learning for QSAR Prediction

By Rich Lewis

Deep Learning for QSAR Prediction

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