Machine%20Learning%20in%20Drug%20Design

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Machine Learning in Drug Design

• David Page
• Dept. of Biostatistics and Medical Informatics
  and Dept. of Computer Sciences

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Collaborators

• Michael Waddell
• Paul Finn
• Ashwin Srinivasan
• John Shaughnessy
• Bart Barlogie

• Frank Zhan
• Stephen Muggleton
• Arno Spatola
• Sean McIlwain
• Brian Kay

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Outline

• Overview of Drug Design
• How Machine Learning Fits Into the Process
• Target Search Single Nucleotide Polymorphisms
  (SNPs)
• Machine Learning from Feature Vectors
• Decision Trees
• Support Vector Machines
• Voting/Ensembles
• Predicting Molecular Activity Learning from
  Structure

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Drugs Typically Are

• Small organic molecules that
• Modulate disease by binding to some target
  protein
• At a location that alters the proteins behavior
  (e.g., antagonist or agonist).
• Target protein might be human (e.g., ACE for
  blood pressure) or belong to invading organism
  (e.g., surface protein of a bacterium).

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Example of Binding
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So To Design a Drug
Identify Target Protein
Knowledge of proteome/genome
Relevant biochemical pathways
Crystallography, NMR Difficult if Membrane-Bound
Determine Target Site Structure
Synthesize a Molecule that Will Bind
Imperfect modeling of structure Structures may
change at binding And even then
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Molecule Binds Target But May

• Bind too tightly or not tightly enough.
• Be toxic.
• Have other effects (side-effects) in the body.
• Break down as soon as it gets into the body, or
  may not leave the body soon enough.
• It may not get to where it should in the body
  (e.g., crossing blood-brain barrier).
• Not diffuse from gut to bloodstream.

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And Every Body is Different

• Even if a molecule works in the test tube and
  works in animal studies, it may not work in
  people (will fail in clinical trials).
• A molecule may work for some people but not
  others.
• A molecule may cause harmful side-effects in some
  people but not others.

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Outline

• Overview of Drug Design
• How Machine Learning Fits Into the Process
• Target Search Single Nucleotide Polymorphisms
  (SNPs)
• Machine Learning from Feature Vectors
• Decision Trees
• Support Vector Machines
• Voting/Ensembles
• Predicting Molecular Activity Learning from
  Structure

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Places to use Machine Learning

• Finding target proteins.
• Inferring target site structure.
• Predicting who will respond positively/negatively.

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Places to use Machine Learning

• Finding target proteins.
• Inferring target site structure.
• Predicting who will respond positively/negatively.

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Healthy vs. Disease
Healthy
Diseased
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If We Could Sequence DNA Quickly and Cheaply, We
Could

• Sequence DNA of people taking a drug, and use ML
  to identify consistent differences between those
  who respond well and those who do not.
• Sequence DNA of cancer cells and healthy cells,
  and use ML to detect dangerous mutations
  proteins these genes code for may be useful
  targets.
• Sequence DNA of people who get a disease and
  those who dont, and use ML to determine genes
  related to succeptibility proteins these genes
  code for may be useful targets.

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Problem Cant Sequence Quickly

• Can quickly test single positions where variation
  is common Single Nucleotide Polymorphisms
  (SNPs).
• Can quickly test degree to which every gene is
  being transcribed Gene Expression Microarrays
  (e.g., Affymetrix Gene Chips).
• Can (moderately) quickly test which proteins are
  present in a sample (Proteomics).

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Outline

• Overview of Drug Design
• How Machine Learning Fits Into the Process
• Target Search Single Nucleotide Polymorphisms
  (SNPs)
• Machine Learning from Feature Vectors
• Decision Trees
• Support Vector Machines
• Voting/Ensembles
• Predicting Molecular Activity Learning from
  Structure

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Example of SNP Data
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Problem SNPs are not Genes

• If we find a predictive SNP, it may not be part
  of a gene we can only infer that the SNP is
  near a gene that may be involved in the
  disease.
• Even if the SNP is part of a gene, it may be
  another nearby gene that is the key gene.

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Problem Even SNPs are Costly

• Typically cannot use all known SNPs.
• Can focus on a particular chromosome and area if
  knowledge permits that.
• Can use a scattering of SNPs, since SNPs that are
  very close together may be redundant use one SNP
  per haplotype block, or region where
  recombination is rare.

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Why Machine Learning?

• There may be no single SNP in our data that
  distinguishes disease vs. healthy.
• Still may be possible to have some combination of
  SNPs to predict. Can gain insight from this
  combination.

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Outline

• Overview of Drug Design
• How Machine Learning Fits Into the Process
• Target Search Single Nucleotide Polymorphisms
  (SNPs)
• Machine Learning from Feature Vectors
• Decision Trees
• Support Vector Machines
• Voting/Ensembles
• Predicting Molecular Activity Learning from
  Structure

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Decision Trees in One Picture
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Naïve Bayes in One Picture
Age
SNP 3000
SNP 1
SNP 2
. . .
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Voting Approach

• Score SNPs using information gain.
• Choose top 1 scoring SNPs.
• To classify a new case, let these SNPs vote
  (majority or weighted majority vote).
• We use majority vote here.

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Task Predict Early Onset DiseaseFrom SNP Data

• Only 3000 SNPs, coarsely sampled over entire
  genome.
• 80 patients (examples), 40 with early onset.
• Using technology from Orchid.
• Can a predictor be learned that performs
  significantly better than chance on unseen data?

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Results

• Use all data, only top 1 of features, or only
  top 10 of features (according to decision trees
  purity measure).
• Use Trees, SVMs, Voting.
• SVMs with top 10 achieve 71 accuracy.
  Significantly better than chance (50).

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Lessons

• Feature selection is important for performance.
• Methodology note for machine learning
  specialists must repeat this entire process on
  each fold of cross-validation or results will be
  overly-optimistic.
• SNP approach is promising get funding to measure
  more SNPs.
• More work on SVM comprehensibility.

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Outline

• Overview of Drug Design
• How Machine Learning Fits Into the Process
• Target Search Single Nucleotide Polymorphisms
  (SNPs)
• Machine Learning from Feature Vectors
• Decision Trees
• Support Vector Machines
• Voting/Ensembles
• Predicting Molecular Activity Learning from
  Structure

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Places to use Machine Learning

• Finding target proteins.
• Inferring target site structure.
• Predicting who will respond positively/negatively.

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Typical Practice when Target Structure is Unknown

• Test many molecules (1,000,000) to find some that
  bind to target (ligands).
• Infer (induce) shape of target site from 3D
  structural similarities.
• Shared 3D substructure is called a pharmacophore.
• Perfect example of a machine learning task with
  spatial target.

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An Example of Structure Learning
Inactive
Active
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Inductive Logic Programming

• Represents data points in mathematical logic
• Uses Background Knowledge
• Returns results in logic

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The Logical Representation of a Pharmacophore
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Background Knowledge I

• Information about atoms and bonds in the
  molecules
• atm(m1,a1,o,3,5.915800,-2.441200,1.799700).
• atm(m1,a2,c,3,0.574700,-2.773300,0.337600).
• atm(m1,a3,s,3,0.408000,-3.511700,-1.314000).
• bond(m1,a1,a2,1).
• bond(m1,a2,a3,1).

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Background knowledge II

• Definition of distance equivalence
• dist(Drug,Atom1,Atom2,Dist,Error)-
• number(Error),
• coord(Drug,Atom1,X1,Y1,Z1),
• coord(Drug,Atom2,X2,Y2,Z2),
• euc_dist(p(X1,Y1,Z1),p(X2,Y2,Z2),Dist1),
• Diff is Dist1-Dist,
• absolute_value(Diff,E1),
• E1 lt Error.
• euc_dist(p(X1,Y1,Z1),p(X2,Y2,Z2),D)-
• Dsq is (X1-X2)2(Y1-Y2)2(Z1-Z2)2,
• D is sqrt(Dsq).

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Central Idea Generalize by searching a lattice
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Conformational model

• Conformational flexibility modelled as multiple
  conformations
• Sybyl randomsearch
• Catalyst

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Pharmacophore description

• Atom and site centred
• Hydrogen bond donor
• Hydrogen bond acceptor
• Hydrophobe
• Site points (limited at present)
• User definable
• Distance based

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Example 1 Dopamine agonists

• Agonists taken from Martin data set on QSAR
  society web pages
• Examples (5-50 conformations/molecule)

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Pharmacophore identified

• Molecule A has the desired activity if
• in conformation B molecule A contains a
  hydrogen acceptor at C, and
• in conformation B molecule A contains a basic
  nitrogen group at D, and
• the distance between C and D is 7.05966 /-
  0.75 Angstroms, and
• in conformation B molecule A contains a
  hydrogen acceptor at E, and
• the distance between C and E is 2.80871 /-
  0.75 Angstroms, and
• the distance between D and E is 6.36846 /-
  0.75 Angstroms, and
• in conformation B molecule A contains a
  hydrophobic group at F, and
• the distance between C and F is 2.68136 /-
  0.75 Angstroms, and
• the distance between D and F is 4.80399 /-
  0.75 Angstroms, and
• the distance between E and F is 2.74602 /-
  0.75 Angstroms.

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Example II ACE inhibitors

• 28 angiotensin converting enzyme inhibitors taken
  from literature
• D. Mayer et al., J. Comput.-Aided Mol. Design, 1,
  3-16, (1987)

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Experiment 1

• Attempt to identify pharmacophore using original
  Mayer et al. Data (final conformations).
• Initial failed attempt traced to bugs in
  background knowledge definition.
• 4 pharmacophores found with corrected code
  (variations on common theme)

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ACE pharmacophore

• Molecule A is an ACE inhibitor if
• molecule A contains a zinc-site B,
• molecule A contains a hydrogen acceptor C,
• the distance between B and C is 7.899 /-
  0.750 A,
• molecule A contains a hydrogen acceptor D,
• the distance between B and D is 8.475 /-
  0.750 A,
• the distance between C and D is 2.133 /-
  0.750 A,
• molecule A contains a hydrogen acceptor E,
• the distance between B and E is 4.891 /-
  0.750 A,
• the distance between C and E is 3.114 /-
  0.750 A,
• the distance between D and E is 3.753 /-
  0.750 A.

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Pharmacophore discovered
Zinc site H-bond acceptor
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Experiment 2

• Definition of zinc ligand added to background
  knowledge
• based on crystallographic data
• Multiple conformations
• Sybyl RandomSearch

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Experiment 2

• Original pharmacophore rediscovered plus one
  other
• different zinc ligand position
• similar to alternative proposed by Ciba-Geigy

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Example III Thermolysin inhibitors

• 10 inhibitors for which crystallographic data is
  available in PDB
• Conformationally challenging molecules
• Experimentally observed superposition

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Key binding site interactions
Asn112-NH
OC Asn112
S2
Arg203-NH
S1
OC Ala113
Zn
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Interactions made by inhibitors
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Pharmacophore Identification

• Structures considered 1HYT 1THL 1TLP 1TMN 2TMN
  4TLN 4TMN 5TLN 5TMN 6TMN
• Conformational analysis using Best conformer
  generation in Catalyst
• 98-251 conformations/molecule

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Thermolysin Results

• 10 5-point pharmacophore identified, falling into
  2 groups (7/10 molecules)
• 3 acceptors, 1 hydrophobe, 1 donor
• 4 acceptors, 1 donor
• Common core of Zn ligands, Arg203 and Asn112
  interactions identified
• Correct assignments of functional groups
• Correct geometry to 1 Angstrom tolerance

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Thermolysin results

• Increasing tolerance to 1.5Angstroms finds common
  6-point pharmacophore including one extra
  interaction

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Example IV Antibacterial peptides

• Dataset of 11 pentapeptides showing activity
  against Pseudomonas aeruginosa
• 6 actives lt64mg/ml IC50
• 5 inactives

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Pharmacophore Identified
A Molecule M is active against Pseudomonas
Aeruginosa if it has a conformation B such
that M has a hydrophobic group C, M has a
hydrogen acceptor D, the distance between C and
D in conformation B is 11.7 Angstroms M has a
positively-charged atom E, the distance between
C and E in conformation B is 4 Angstroms the
distance between D and E in conformation B is 9.4
Angstroms M has a positively-charged atom
F, the distance between C and F in conformation
B is 11.1 Angstroms the distance between D and F
in conformation B is 12.6 Angstroms the distance
between E and F in conformation B is 8.7
Angstroms Tolerance 1.5 Angstroms
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Ongoing ILP developments (pharmacophores)

• Continue to extend method validation
• Extending to combinatorial mixtures
• Quantitative models
• Mixing different datatypes in background
  knowledge
• Developing graphical front-end

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Ongoing developments (Other)

• Analysis of HTS datasets
• Analysis of drug-likeness
• Derivation of new descriptors
• eg Empirical binding functions