Chemoinformatics

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Chemoinformatics

• P. Baldi, J. Chen, and S. J. Swamidass
• School of Information and Computer Sciences
• Institute for Genomics and Bioinformatics
• University of California, Irvine

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Overall Outline

1. Introduction
2. Molecular Representations
3. Chemical Data and Databases
4. Molecular Similarity
5. Chemical Reactions
6. Machine Learning and Other Predictive Methods
7. Molecular Docking and Drug Discovery

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1. Introduction

• What is Chemoinformatics
• Resources
• Brief Historical Perspective
• Chemical Space Small Molecules
• Overview of Problems and Methods

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What is Chemoinformatics?

• chemoinformatics encompasses the design,
  creation, organisation, management, retrieval,
  analysis, dissemination, visualization and use of
  chemical information

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What is Chemoinformatics?

• "the mixing of information resources to transform
  data into information and information into
  knowledge, for the intended purpose of making
  better decisions faster in the arena of drug lead
  identification and optimizaton"

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What is Chemoinformatics?

• the set of computer algorithms and tools to
  store and analyse chemical data in the context of
  drug discovery and design projects
• However drug design/discovery is to
  chemoinformatics like DNA/RNA/ protein sequencing
  is to bioinformatics

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Resources

• Books
• J. Gasteiger, T. E. and Engel, T. (Editors)
  (2003). Chemoinformatics A Textbook. Wiley.
• A.R. Leach and V. J. Gillet (2005). An
  Introduction to Chemoinformatics. Springer.
• Journal
• Journal of Chemical Information and Modeling
• Web
• http//cdb.ics.uci.edu
• and many more

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Brief Historical Perspective

• Historical perspective physics, chemistry and
  biology
• Theorem
• computers/biology or computers/physicsgtgt
  computers/chemistry
• Proof
• Genbank, Swissprot, PDB, Web (CERN), etc..

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Caveat Long Tradition

• Quantum Mechanics
• Docking
• Beilstein
• ACS
• Etc
• Gasteiger, J. (2006). "Chemoinformatics a new
  field with a long tradition." Anal Bioanal
  Chem(384) 57-64.

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Possible Causes

• Alchemy
• Industrial age and early commercial applications
  of chemistry
• Concurrent development of modern computers and
  modern biology
• Scientific differences (theory/process)
• Psychological perceptions (life/inert)
• ACM

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Chemical Space Small Molecules in Organic
Chemistry

• Understanding chemical space
• Small molecules
• chemical synthesis
• drug design
• chemical genomics,
• systems biology
• nanotechnology
• etc

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A mathematician is a machine that converts
coffee into theorems P. Erdos
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Cholesterol
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Aspirin
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A chemoinformatician is a machine ..
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Chemical Space
Stars Small Mol.
Existing 1022 107
Virtual 0 1060 (?)
Mode Real Virtual
Access Difficult Easy
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Chemoinformatics

• Historical perspective physics, chemistry and
  biology
• Understanding chemical space
• Small molecules (chemical synthesis, drug design,
  chemical genomics, systems biology,
  nanotechnology)
• Predict physical, chemical, biological properties
  (classification/regression)
• Build filters/tools to efficiently navigate
  chemical space to discover new drugs, new
  reactions, new galaxies, etc.

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Chemo/Bio Informatics

• Two Key Ingredients
• 1. Data
• 2. Similarity Measures
• Bioinformatics analogy and differences
• Data (GenBank, Swissprot, PDB)
• Similarity (BLAST)

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Computational/Predictive Methods

• Spetrum of methods
• Quantum Mechanics
• .
• Molecular Mechanics
• .
• Machine Learning

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Quantum Mechanics

• Schrodingers Equation (time independent)
• H?E?
• H(-h2/8p2m)?2V Hamiltonian Operator
• E Energy
• V external potential (time independent)
• ? ?(x,t) (complex) wave function ?(x)T(t)
  (time independent case)
• ?2 ? ? probability density function
  (particle at position x)

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Schrodinger Equation

• Partial differential eigenvalue equation
• Where are the electrons and nuclei of a molecule
  in space?
• Uncer a given set of conditions, what are their
  energies?
• Difficult to solve exactly as number of particle
  grows (electron-electron interactions, etc)
• Approximate methods
• Ab initio
• Semi empirical
• 3D structures
• Reaction mechanisms, rates

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Ab Initio

• Limited to tens of atoms and best performed using
  a cluster or supercomputer
• Can be applied to organics, organo-metallics, and
  molecular fragments (e.g. catalytic components of
  an enzyme)
• Vacuum or implicit solvent environment
• Can be used to study ground, transition, and
  excited states (certain methods)
• Specific implementations include GAMESS,
  GAUSSIAN, etc.

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Semiempirical Methods

• Semiempirical methods use parameters that
  compensate for neglecting some of the time
  consuming mathematical terms in Schrodinger's
  equation, whereas ab initio methods include all
  such terms.
• The parameters used by semiempirical methods can
  be derived from experimental measurements or by
  performing ab initio calculations on model
  systems.Limited to hundreds of atoms
• Can be applied to organics, organo-metallics, and
  small oligomers (peptide, nucleotide, saccharide)
• Can be used to study ground, transition, and
  excited states (certain methods).
• Specific implementations include AMPAC, MOPAC,
  and ZINDO.

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Molecular Mechanics

• Force field approximation
• Ignore electrons
• Calculate energy of a system as a function of
  nuclear positions

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Molecular Mechanics

• Energy Stretching Energy Bending Energy
  Torsion Energy Non-Bonded Interactions Energy

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Stretching Energy
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Bending Energy
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Torsion Energy
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Non-Bonded Energy
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Statistical/Machine Learning Methods

• NNs and recursive NNs
• GA
• SGs
• Graphical Models
• Kernels
• Representations are essential. Must either (1)
  deal with non-standard data structures of
  variable size or (2) represent the data in a
  standard vector format.