Chemoinformatics

P. Baldi, J. Chen, and S. J. SwamidassSchool of Information and Computer Sciences

Institute for Genomics and BioinformaticsUniversity of California, Irvine

2

Overall Outline

1. Introduction2. Molecular Representations 3. Chemical Data and Databases4. Molecular Similarity5. Chemical Reactions6. Machine Learning and Other Predictive

Methods7. Molecular Docking and Drug Discovery

3

1. Introduction

• What is Chemoinformatics

• Resources

• Brief Historical Perspective

• Chemical Space: Small Molecules

• Overview of Problems and Methods

4

What is Chemoinformatics?

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

5

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"

6

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

7

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………

8

Brief Historical Perspective

• Historical perspective: physics, chemistry and biology

• Theorem:

computers/biology or computers/physics>> computers/chemistry

• Proof:

Genbank, Swissprot, PDB, Web (CERN), etc..

9

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.

10

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

11

Chemical Space: Small Molecules in Organic Chemistry

• Understanding chemical space• Small molecules:

– chemical synthesis– drug design – chemical genomics,– systems biology– nanotechnology– etc

12

“A mathematician is a machine that converts coffee into theorems” P. Erdos

13

Cholesterol

14

Aspirin

15

“A chemoinformatician is a machine …..…”

16

Chemical Space

Stars Small Mol.

Existing 1022 107

Virtual 0 1060 (?)

Mode Real Virtual

Access Difficult “Easy”

17

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.

18

Chemo/Bio Informatics

Two Key Ingredients

1. Data

2. Similarity Measures

Bioinformatics analogy and differences:– Data (GenBank, Swissprot, PDB)– Similarity (BLAST)

19

Computational/Predictive Methods

• Spetrum of methods:

– Quantum Mechanics– ….– Molecular Mechanics– ….– Machine Learning

20

Quantum Mechanics

Schrodinger’s Equation (time independent)

Hψ=EψH=(-h2/8π2m)∂2+V = Hamiltonian Operator

E= Energy

V =external potential (time independent)ψ= ψ(x,t) =(complex) wave function = ψ(x)T(t)

(time independent case) Ψ2 = Ψ* Ψ =probability density function (particle at

position x)

21

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

22

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.

23

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.

24

Molecular Mechanics

• Force field approximation

• Ignore electrons

• Calculate energy of a system as a function of nuclear positions

25

Molecular Mechanics

Energy = Stretching Energy + Bending Energy + Torsion Energy + Non-Bonded Interactions Energy

26

Stretching Energy

27

Bending Energy

28

Torsion Energy

29

Non-Bonded Energy

30

Statistical/Machine Learning Methods

NNs and recursive NNsGASGsGraphical ModelsKernels………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.