1. Chemoinformatics
2017 David Chalmers

2. Section 1: Chemical data

3. Chemical data is special...
How do I find out information about a particular molecule or chemical reaction?
Chemical data is more complex than text or numerical values
How do I describe chemical structures?
Chemical names are important (but inconvenient)
Chemicals can be represented as 2D or 3D structures 3

4. Chemical data - old school
The American Chemical Society indexes all chemical publications in journal articles and creates an index (Chemical Abstracts).
Prior to the general availability of online databases, this was how you searched for information about chemical compounds
The tenth collective index of Chemical Abstracts (right) consisted of 75 volumes and weighed 170 kg. It contained nearly 24 million entries.
Production of paper versions of CA ceased in 2001. 4

5. Chemical data - new school
Chemical Abstracts database 'Scifinder'
Indexes 75 million compounds (2013)
Many other databases containing chemical information are available 5

6. How do we represent molecules in a computer?
We can store a wide range of information about each molecule
Information about the whole molecule:
molecule name
journal article (for crystal structures)
creator or author(s)
Information about each atom:
atomic element (H, He, C, N, O, F, etc.)
atom name (E.g. in an amino acid N, CA, CB, CO O, etc.)
Coordinates in Cartesian format 2D (X,Y) or 3D (X, Y, Z) or Z-matrix format
Atom charge (formal and/or partial)
residue name (E.g. for a protein: Ala, Pro, etc.)
temperature factor and occupancy for crystal structures
Bonding information:
usually stored as a connection table which describes which atoms are bonded together.
information about bond-orders (single, double, aromatic, etc.) is important, but it is not always stored in some file formats (E.g. pdb) 6

7. SD file format Developed by Molecular Design Limited (MDL).
Can store 2D or 3D structures
Atom block contains atom coordinates and properties
Can contain query structures which can contain variable atom and bond types. E.g an atom may be either nitrogen or carbon, or a bond could be either double or aromatic
Connection table contains bonding information
Can store additional information such as biological activity data associated with the molecule
$$$$ marks end of record so multiple records can be included in one file
-ISIS D
V2000 C C C C C C O C
M END
> <Isis_internal_number> (2) 2
> <chemical_name> (2)
Minaprine dihydrochloride
> <smiles_code> (2)
c1(c2ccccc2)(cc(c(NCCN3CCOCC3)nn1)C).Cl.Cl
> <Plate position> (2) 66
$$$$ 7

8. PDB file format
The Protein Data Bank (PDB) file format was developed by the Brookhaven National Laboratory to store protein crystal structure information
Used by many molecular modelling programs
The PDB format has limitations:
Columns are of fixed size
Does not contain information about bond orders (these are recorded in a separate database)
The Databank has developed new formats to replace the PDB format. e.g. the mmCIF format (Macromolecular Crystallographic Information File)
Ref: pdb/info.html. 8

9. Section 2: Line notations

10. Chemical Line Notations
Representations of molecules that fit on a single line. E.g. standard structural formulas. These work well for linear compounds, but less well for rings…
CH3CH2COOH
Line notations are:
Compact
Generally human readable/understandable
Use little computer storage 10

11. There are many line notations
6-Dimethylamino-4-phenylamino-naphthalene-2-sulfonic acid
Wiswesser line notation: 1L66J BMR& DSWQ IN1&1 – An early line notation (1949) that describes molecules as fragments. Used for databases but fell out of use because it is not very computer friendly
Rosdal: 1=-5-=10=5,10-1,1-11N-12-=17=12,3-18S-19O,18=20O,18=21O,8-22N-23,22-24 – A linear representation of a connection table developed by Beilstein
SMILES: CN(C)C1=CC=C2C (C(NC3=CC=CC=C3)=CC(S(=O)(O)=O)=C2)=C1 - Developed by Dave Weninger and Daylight Chemical Systems
InChi – InChI=1S/C18H18N2O3S/c1-20(2) (24(21,22)23)12-18(17(13)11-15) /h3-12,19H,1-2H3,(H,21,22,23) - A compact chemical representation developed by IUPAC
Sybyl Line Notation (SLN, Tripos) 11

12. SMILES
SMILES is the most widely used and most useful chemical line notation
Can be used as input/output in many programs
Simple SMILES strings resemble standard chemical nomenclature. The atoms commonly found in organic molecules (B, C, N, O, P, S, F, Cl, Br, I) are represented by the atomic element symbol.
Single bonds are implied between each atom.
Hydrogen atoms are not usually shown (but can be included if necessary) 12

13. SMILES – simple examples
Compound
SMILES string
Methane C
Ethane CC
Ammonia N
[NH3]
1-hexanol
CCCCCCO 13

14. SMILES – inorganic elements
Non-organic elements must be enclosed in square brackets.
Charged atoms are also enclosed in square brackets.
[Na+]
Sodium ion (+)
[Al+3]
Aluminium ion (3+)
[NH4+]
Ammonium ion (+) 14

15. SMILES - bonds
Single bonds are implied. They can optionally be represented using a dash ‘-‘
Double and triple bonds are represented using ‘=’ and ‘#’
Noncovalent bonds are specified using a full stop ‘.’ 15

16. SMILES - branches
Branches are represented by enclosing the side-chain in parentheses ‘()’
Acetic acid
CC(=O)O
t-Butyl alcohol
OC(C)(C)C 16

17. SMILES - rings
Rings are specified by using numbers to create ‘ring closures’. The number follows after the atom.
Lower case characters are used to specify aromatic rings
cyclohexane
C1CCCCC1
benzene
c1ccccc1
pyridine
n1ccccc1
naphthalene
c1cccc2c1cccc2 17

18. SMILES – you have a go Write a SMILES for phenol Oc1ccccc1
Write a SMILES for this molecule
CN1CCCC1c2cnccc2 18

19. SMILES - Additional notations
SMILES contains additional features which can be used to describe chirality, double bond isomers (E, Z) and metal complexes.
More information about the SMILES notation is available at: 19

20. Generate line notations with Chemdraw
Use: Edit  Copy as  smiles
(Also Copy as SLN or InChi) 20

21. Section 3: Chemical databases

22. Chemical Databases
Chemical databases are important in all stages of medicinal chemistry
Databases may contain:
Chemical structure, reaction and synthetic data (e.g. Chemical Abstracts, Reaxxys, the Merck Index)
Compound structure and synthesis information (e.g. An in-house compound registry)
Biological activity data such as in-house testing data or MDL Drug Data Report (MDDR)
CAS registers 89 million compounds and 39 million patent and journal articles 22

23. ZINC
The ZINC database ( collects together commercially available compounds, converts them to 3D structures and creates a number of useful subsets for drug desing (druglike, leadlike, etc, etc). 23

24. Searching chemical databases
The important distinction between chemical database software and other database programs used for holding text or images is that a chemical database must be able to interpret chemical structures
In a chemical database it is desirable to be able to search for:
Exact compound structures
Compounds containing a particular substructure
Compounds similar to a given structure
In some cases we wish to search for chemical features in 3D (pharmacophores) 24

25. Section 4: Similarity and similarity comparisons

28. I have 3 things. How similar are they?
Automobile
Made in Bavaria
250 hp
1395 kg
4-cylinder normally aspirated engine
6-speed gearbox
Four wheel drive
Built in 2007
Automobile
Made in Bavaria
200 hp
1250 kg
6-cylinder normally aspirated engine
Four-speed gearbox
Front wheel drive
Built in 1973
Automobile
Made in Bavaria
200 hp
1250 kg
6-cylinder turbocharged engine
6-speed gearbox
Rear wheel drive
Built in 2007 28

29. Fingerprints 29 PKW 200 ps 250 ps Made in Bavaria 2,0 Liter Hubraum
Four speed
Six speed
Rear-wheel drive
Front-wheel drive
Four-wheel drive
Normally aspir.
Turbocharged
Built in 1973
Built in 2007 1 29

30. Molecular comparison using molecular features or fragments
Comparison of molecular structures is straightforward for a chemist but is more difficult for a computer
The simplest approach is to compare every atom in one molecule to every atom in a second molecule. This is slow (particularly if we wish to search a database containing thousands or even millions of entries)
We therefore need a quick way (even approximate) way to compare two molecules
Solution: Break each molecule down into smaller pieces (features) and which allow a quick comparison between molecules 30

31. Molecular fragments SMILES: c1ccccc1O n1ccc(O)cc1 num atoms
phenol fragments
2-hydroxypyridine fragments 1
c o
c o n 2
cc cO
cn cc cO 3
ccc cO
ccn cnc ccc ncO ccO 4
cccc cccO
cccc cccn ccnc cccn cccO cncO 5
ccccc ccccO
ccccc ccccn cccnc ccncc ccccO ccncO 6
c1ccccc1 cccccO
c1ccccn1 nccccO cccncO 7
c1ccccc1O
c1cccnc1O 31

32. Molecular fingerprints
A common approach to molecular comparisons uses molecular features created by breaking a molecule down into fragments
The features present in each molecule can be represented as a bit string or binary number using ‘1’ to indicate the presence of a fragment and ‘0’ to denote absence
For example, bit strings for phenol (Ph) and 2-hydroxypyridine (2HPy) might look like this: 32

33. Substructure fingerprints
Fingerprints can be generated for only part of a molecule (a substructure)
Sub 33

34. Matching fingerprints
Then we need to compare the fragment fingerprint with the fingerprints of the two molecules in our small database.
We have a substructure match when all bits set in the substructure are also set in the search molecule.
Here we can see that the substructure matches 2-hydroxypyridine but not phenol
An important aspect of molecular fingerprints – the process of compressing them, will not be discussed here. 34

35. Molecular fingerprints
These bit strings only contain fragments up to three atoms in size. In principal you can include any size fragments
A typical case is keys used by the organisation MDL.
For structure searching they have a set of 166 keys
For similarity searching a set of 960 keys is used
Advantages:
Fingerprints comparison uses Boolean arithmetic (AND, OR, NOT) which is very fast
Two identical molecules will have the same fingerprint regardless of orientation 35

36. Chemical Diversity and Similarity
Molecular similarity (or dissimilarity) means different things in different contexts. What molecular properties do we wish to compare?
Common structures
Common scaffolds
Common functional groups
Common physicochemical properties
Etc. 36

37. Chemical Diversity and Similarity
It is often useful to be able to find molecules that are similar or dissimilar in some respects.
Two important applications are:
Lead hopping – the idea of finding lead molecules with similar biological activity to a known compound, but with a fundamentally different chemical structure
Generating structurally diverse screening libraries – we usually wish to make efficient use of HTS screening and make sure that we are not testing large numbers of very similar compounds (e.g. compounds substituted with different halogens: F, Cl, Br, etc) 37

38. Chemical Diversity and Similarity
Molecular (dis)similarity can be measured in a large number of different ways
One important method, the Tanimoto Coefficient, makes use of molecular fragments
This is a value between 0 and 1 that describes the number of common structural fragments in a molecule
N1 Number of fragments in molecule 1
N2 Number of fragments in molecule 2
Nc Number of fragments common to both molecules 1 and 2 38

39. Example
1 naphthalene
clozapine
piperidine
Matrix of Tanimoto coefficients for the compounds naphthalene clozapine and piperidine 39

40. Searching for chemical features in 3D
We can extend our chemical structure searching to pharmacophores by defining chemical features such as hydrophobic groups, hydrogen bond donors and acceptors, aromatic rings, etc.
These features are arranged in space to form a pharmacophore query
Structures hit if they match the query structure
Different molecular conformations should be taken into account
Not all database systems can provide this capability
Pharmacophore query
Query + hit structure 40

41. Section 5: Application of similarity searching

42. Lead or Scaffold Hopping
Ideas of molecular similarity are interesting to drug designers, because it provides a way to find molecules that are functionally similar to an active compound but might avoid other problems such as low activity, poor ADME properties or competitive patents ($$$$)
The idea of generating new biologically active molecules that contain an active pharmacophore but are built on a different molecular scaffold is known as lead hopping. This can be thought of jumping from one region of chemical space to another 42

43. The statins
Perhaps the best known example of lead hopping is the ‘statins’. This class of drugs inhibit cholesterol biosynthesis by blocking the enzyme HMG-CoA reductase which performs a key step in the cholesterol biosynthetic pathway 43

44. Statins
Bound conformations of (left) atorvastatin and mevastatin and (right) atorvastatin
and cerivastatin. Note the close overlap of the portion of the drug that mimics
the glutaryl portion of HMG-CoA. Note also the overlap of the isopropyl and
fluorophenyl groups in fluvastatin and cerivastatin 44

45. Shape-based lead hopping
In another example, Rush et al. (J. Med. Chem. 2005, 48, ) describe using a shape based similarity method to identify a new class of antibacterial compounds
In this case, the target is the ZipA-FtsZ protein-protein interaction which has an essential role in the formation of the septal ring - a membrane-associated organelle that drives constriction and formation of new cell walls during cell division 45

46. Shape-based lead hopping
Wyeth identified a lead compound by HTS screening and crystallised it in the target protein 46

47. Shape-based lead hopping
Bound structure of the lead compound in ZipA 47

48. Shape-based lead hopping
The researchers then used a shape-based comparison (Rapid Overlay of Chemical Structures or ROCS) to find related compounds that were available for testing.
The comparison between the search structures and the query molecules used a Tanimoto comparison. 48

49. ROCS 49

50. ROCS
ROCS similarity of 0.9 (i.e. mostly shape similarity) does not correspond to closely to similarity calculated using a Tanimoto coefficient (about 0.35) in this case. 50

51. ROCS
A number of active compounds were identified. They report the crystal structure of one of the compounds (ROCS_PART_18) bound to the target protein 51

52. Key learning questions
Chemical data
How does it differ from other types of data (e.g. text)?
How does this complicate finding information (searching)?
How do we store chemical data?
Line notations
What is a line notation? Why might this be useful?
How does the SMILES line notation work?
Chemical databases
Why might these be useful?
Similarity
What is a fingerprint?
How can fingerprints or features be used to find similar things?
How can you use fingerprints to find substructures?
What is a Tanimoto coefficient? How would you use one?
Lead hopping
Why do chemists care about this?
How can it help in a drug development program? 52

53. Good resources SMILES: Daylight SMILES tutorial and theory