Pistoia Alliance USA Conference 2016

3DS.COM/BIOVIA©DassaultSystèmes|ConfidentialInformation|10/21/2016|ref.:3DS_Document_2015
• The use of data to build statistical models yielding predictions that help make decisions
• Model: equation or rule set to predict a response Y from a set of descriptors {X1,…,Xp}
• Y is continuous: Regression model [e.g., pIC50 for hERG channel inhibition]
• Y is categorical: Classification model [e.g., compound is “mutagenic” or “nonmutagenic”]
• Model is built from a training data set using a particular learning method
• Linear Regression, Genetic Function Approximation (GFA), PLS, …
• Bayesian Classification, RP Forest, Support Vector Machine (SVM), …
• The predictions are never perfect. How much can we trust them?
• Prediction quality depends on
• General quality of model (assessed by test data)
• Specific applicability of model to the sample being predicted
Predictive Analytics – What is it?

Basic Terminology
• Supervised learning is the type of
learning that takes place when
the training instances are labeled
with the correct classification.
• Unsupervised learning, the goal is
harder because there are no pre-
determined categorizations
• Clustering. In this type of learning, the
goal is to find similarities in the training
data.
• Semi -
G1 Mitosis
???
???

Unsupervised Leaning K-means Clustering
• Typically used to Separate Signal from Noise

Update Cluster Centers at Iteration 2
8 10 12 14 16 18 20
7
8
9
10
11
12
13
14
Updated Cluster Centers at Iteration 2
Calculate New Cluster Center
mean_k = average of vectors x(i) assigned to cluster k

Update Memberships (Iteration 2)
8 10 12 14 16 18 20
7
8
9
10
11
12
13
14
Updated Memberships and Boundary at Iteration 2
X Variable
YVariable
assign each data point to the closest cluster
center

Basic Terminology
• Ensemble Model
• Averaging various models together for better
prediction (RF…)
• Model Validation
• Ensure model adequately fits the training and
non-training
• Ensure model is not overfitting (fitting noise)
• When making predictions, ensure test samples
are within model’s applicability domain (MAD)

Modeling Applications in Life Science Research
• Drug / Target interaction
• Drug Efficacy and Safety
• Mechanism of Action
• Formulation
• Clinical Trial Simulation
• Imaging
• High Content Screening – Pathology – DICOM
• Crystal Detection
• ……

Evolution of Image Processing Technology
• Hand-tuned engineered systems that could match or surpass human
performance
• Supervised and semi supervised machine learning. Avoiding hand-tuning or
user intervention (with the exception of marking things for training purposes),
with a primary goal to produce a simple actionable answer
• Fully automated production of models of systems. This includes a shift toward
unsupervised machine learning, and especially toward structure learning
methods (learning the structure of the model, not just it’s parameters)
“A New Era in Bioimage Informatics”, Editorial by RF Murphy, Bioinformatics. May 15 2014

10
Compound Dose Mechanism_of_action Distance
Taxol 0.3 Microtubule 4.481
Epothilone B 0.01 Microtubule 5.887
Taxol 1 Microtubule 6.337
Taxol 3 Microtubule 8.708
Docetaxel 1 Microtubule 11.370
Docetaxel 0.01 Microtubule 11.744
Cytochalesin B 30 Actin cytoskeleton 12.820
Trichostatin 1 Standard chemotherapeutic 13.222
Latrunculin B 1 Actin cytoskeleton 13.249
Monastrol 100 Microtubule 13.320
Emetine 1 Protein synthesis 13.607
Cytochalesin B 3 Actin cytoskeleton 13.794
DMSO
Taxol @ 0.03μM
Epothilone B @ 0.01μM
Cell based Multi parametric
KNN Example MOA

Challenge: Crystal Detection
• Current crystallisation detection methods based on visual inspection
• Customers have tried “all other methods” and found none to be
reliable
• The best efforts in the scientific literature seem to produce an accuracy
rate of 80-85%.
• Customers needs a system be free of false negatives
• Customers requested a system that prioritizes high confident clear
and high confident crystals with a manual re-classification capability for
the “unsure” result.
FTE-based bottle-neck to process

Label: clear = 0
• Clear Droplet
Label= 0

Label: Crystal = 1
Label = 1
• Crystal

Image Processing
1. Use find corners on
image (corners0 [object
count])
3. Apply Linear filter, Learn K
means (filtered1_)
2. Apply custom
filters and the use
find corners
(corners1)

Learn RP Forest Model
• Property to Learn = Label
• User defined set of descriptors
Model Parameters
Append Score= 0 or 1

RP Forest Model Results

Deep Learning
• Neural Networks (1980’s)
• A set of algorithms in machine learning that is based on
learning several layers of representations, corresponding to a
hierarchy of features or factors or concepts, where higher
level concepts are derived from lower-level concepts.
• While the learning process is very computationally intensive
and requires specialized hardware, the inference
(classification) is very fast.
• These methods have dramatically improved the state of the
art in visual object recognition, object detection and many
other domains such as speech recognition, and even drug
discovery and genomics.

Image Net Winner Error Rate
Trained human annotator
Using deep learning

Why Deep Learning Now?
• Big Data
• Meta data
• Algorithmic advances
• Working on unlabeled data
• Unsupervised pre-training
• Supervised back propagation of errors
• Successive layers
• Computer processing
• hardware now available to handle the computational load
• synapse
• neural nets 1-10m
• Google Brain 1B

Deep Learning on Images
• Conventional machine learning solution always involves feature engineering, a time
consuming process that requires considerable domain expertise. Once useful hand-designed
features are generated, a machine learning algorithm is used to perform classification.
• In imaging , deep convolutional neural networks (DNN’s) learn multiple layers of features
from raw pixel data. The key advantage of deep learning is that these features are not
designed by hand: they are learned from data using a general-purpose learning procedure.

Facial Recognition

Challenges Training on Small Samples
• Training a large convolutional neural network requires vast amount of data
• Recent results show that a well chosen starting point for optimization can result
in convergence to a good minimum and improve system performance
dramatically.
• Unsupervised feature learning such as sparse coding is one approach to
initializing the model parameters

Challenges Overfitting and Regularization
• The CNNs have millions of parameters and overfitting them to the training data
is a big problem
• Regularization methods reduce the likelihood of overfitting
• We are using dropout, the technique of dropping out” some unit activations in a given
layer, i.e. setting them to zero. This prevents co-adaptation of units. For each training
example a different set of units to drop is randomly chosen.

Deep Learning
• Deep Convolutional Neural Network (DCNN) on NVidia DIGITS Dev Box
• Crystallization pharma Radiogenomics Intel / MD Anderson

How do we get there?
• Data – collaboration
global, not-for-
profit alliance of life
science
companies,
vendors,
publishers, and
academic groups
that work together
to lower barriers to
innovation in R&D.

Acknowledgments
• Dr.. Roman Hillig Bayer Schering Pharma AG
• Joerg Benz Roche
• Ivana Mikic
• Dana Honeycutt
• https://www.youtube.com/watch?v=IPu28vUcZzI

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