Problems and Opportunities for Machine Learning in Drug Discovery Can you find lessons for Systems B

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Problems and Opportunitiesfor Machine Learning
in Drug Discovery(Can you find lessons for
Systems Biology?)

• George S. Cowan, Ph.D.
• Computer Aided Drug Discovery
• Pfizer Global Research and Development, Ann Arbor
  Labs

CSSB, Rovereto, Italy
19 April 2004
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Working as a Computer Scientist in a Life
Sciences field requires an array of supporting
scientists Thanks to
Project ColleaguesDavid Wild Kjell Johnson
Cheminformatics Mentors and ColleaguesJohn
Blankley Alain Calvet David Moreland
Risk TakersEric Gifford Mark Snow Christine
Humblet Mike Rafferty
Academic Peter Willett Robert Pearlman
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Drug Discovery and Development

• Discern unmet medical need
• Discover mechanism of action of disease
• Identify target protein
• Screen known compounds against target
• Synthesize promising leads
• Find 1-2 potential drugs
• Toxicity, ADME
• Clinical Trials

Biology
Chemistry
Pharmacology
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Lock and Key Model
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Virtual HTS Screening

• Virtual Screening Definition
• estimate some biological behavior of new
  compounds
• identify characteristics of compounds related to
  that biological behavior
• only use some computer representation of the
  compounds
• HTS Virtual Screening is Not QSAR/QSPR
• Based on large amounts of easy to measure
  observations
• Uses early stage data from multiple chemical
  series (no X-ray Crystallography)
• Observations are not refined (Percent
  Inhibition at a single concentration)
• Looking for research direction, not best activity

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Promise of Data Mining

• Data Mining
• Works with large sets of data
• Efficient Processing
• Finds non-intuitive information
• Methods do not depend on the Domain (Marketing,
  Fraud detection, Chemistry, )
• Alternative Data Mining Approaches
• Regression - Linear or Non-Linear - PLS
• Principal Components
• Association Rules
• Clustering Approach - Unsupervised - Concept
  Formation
• Classification Approach - Supervised

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Virtual Screening Challenges to Machine Learning
Overview (1)

• No single computer representation captures all
  the important information about a molecule
• The candidate features for representing molecules
  are highly correlated
• Features are entangled
• Multiple binding modes use different combinations
  of features
• Multiple chemical series / scaffolds use the same
  binding mode
• Evidence that some ligands take on multiple
  conformations when binding to a target
• Any 4 out of 5 important features may be
  sufficient

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More Challenges to Machine Learning
Overview (2)

• Training data and validation data are not
  representative
• Measurements of activity are inherently noisy
• Activity is a rare event target populations are
  unbalanced
• Classification requires choosing cutoffs for
  activity
• There is no good measure for a successful
  prediction
• Many data mining methods characterize activity in
  ways that are meaningless to a chemist
• Data mining results must be reversible to assist
  a chemist in inventing new molecules that will be
  active (inverse QSAR)

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Deep Challenges to Machine Learning
Overview (3)

• No free lunch theorem
• Science is different from marketing

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No Single Computer Representation captures all
the important information

• How do we characterize the electronic face that
  the molecule presents to the protein?
• Grid of surface or surrounding points with field
  calculations
• Conformational flexibility
• 3-D relationships of pharmacophores
• Complementary volumes and surfaces
• Complementary charges
• Complementary hydrogen bonding atoms
• Similar Hydrophbicity/Hydrophilicity
• Connectivity Bonding between Atoms (2-D)
• pharmacophore info is implicitly present to some
  extent
• not biased toward any particular conformation
• Presence of molecular fragments (fingerprints)
• Other Linear (SLN, SMILES)? Free-tree?

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Pharmacophores
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Representation of Chemical Structures (2D)

• Aspirin

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BCI Chemical Descriptors

• Descriptors are binary and represent

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We dont have the right descriptors, but we have
thousands that are easy to compute

• Thousands of molecular fragments
• Hundreds of calculated quasi-physical properties
• Hundreds of structural connectivity indicators
• Much of this information is redundant

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Feature Interaction andMultiple Configurations
for ActivityRequire Disjunctive Models

• Multiple binding modes where different
  combinations of features contribute to the
  activity(including non-competitive ligands)
• Multiple chemical series / scaffolds use the same
  binding mode
• Any 3 out of 4 important features may be
  sufficient
• Evidence that some targets require multiple
  conformations from a ligand in order to bind

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Non-competitive Binding
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Non-competitive Binding
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Unbalanced target populations (activity is a
rare event)

• About 1 of drug-like molecules have interesting
  activity
• Most of our experience in classification methods
  is with roughly balanced classes
• Predictive methods are most accurate where they
  have the most data (interpolation), but where we
  need the most accuracy is with the extremely
  active compounds (extrapolation)
• Warning Your data may look balanced
• True population of interest
• new and different compounds
• Unrepresentative HTS training data
• What chemists made in the past
• Unrepresentative follow-up compounds for
  validation
• What chemists intuition led them to submit to
  testing

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Populations
next
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Cipsline, Anti-infectives
Our models are accurate on the compounds made by
our labs
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Kappa 0.147
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Choosing cutoffs for activity and cutoffs for
compounds to pursue

• Overlapping ranges of Inactive and Active
• Cost of missing an active vs. cost of pursuing
  an inactive

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Ideal vs. Actual HTS Observations
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Virtual Screening of active retrieved vs of
compounds tested
tested
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We use the log-linear graph to compare methods at
different follow-up levelsSee how 3 different
methods perform at selecting 5, 50, or 500
compounds to test
RP
SOM
LVQ
Reference
Random
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Noise in measurement of activity

• Suppose 1 active and 1 error, then our
  predicted actives are 50 false positives
• This is out of the range of data-mining methods
  (but see Identifying Mislabeled Training Data,
  Brodley Friedl, JAIR, 1999)
• Luckily, the error in measuring inactives is
  dampened
• Methods can take advantage of the accuracy in
  inactive information in order to characterize
  actives
• On the other hand, inactives have nothing in
  common, except that they are the other 99

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Mysterious AccuracyORNeural Networks are great,
but what are they telling me?

• We have a decision to make about data mining
  goals
• Do we try toOutperform the chemist or engage
  the chemist
• We need to assist a chemist in inventing new
  molecules that will be active (inverse QSAR)
• We need to characterize activity in ways that are
  meaningful to a chemist

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No Free Lunch Theorem

• Proteins recognize molecules
• Proteins compute a recognition function over the
  set of molecules
• Proteins have a very general architecture
• Proteins can recognize very complex or very
  simple characteristics of molecules
• Proteins can compute any recognition function(?)
• No single data-mining/machine-learning method can
  outperform all others on arbitrary functions
• Therefore every new target protein requires its
  own modeling method
• Cheap Brunch Hypothesis
• Maybe proteins have a bias

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Science, Not Marketing

• We are looking for hypotheses that are worth the
  effort of experimental validation(not
  e-marketing opportunities)
• Data-mining rules and models need to be in the
  form of a hypothesis comparable to the chemists
  hypotheses
• Chemists need tools that help them design
  experiments to validate or invalidate these
  competing hypotheses
• HTS is an experiment in need of a design

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Conclusion

• Machine-learning tools provide an opportunity for
  processing the new quantities of data that a
  chemist is seeing
• The naïve data-mining expert has a lot to learn
  about chemical information
• The naïve chemist has a lot to learn about
  data-mining for information

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If there are so many problemswhy are we having
so much fun? Maybe weve stumbled into the
cheap brunch