1. Daylight and Discovery
How do I impress the boss when I get back?
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2. What is Discovery?
A constant fight against the hedgehogs!!
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3. What have I learned this week?
Above all you have learned new languages that allow you to communicate chemical concepts to, and between, machines.
These languages also allow you to communicate these concepts via machines to your colleagues.
You have also learned about other descriptions of a molecular structure, such as fingerprints.
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4. Language recap
SMILES
SMARTS
SMIRKS
(FINGERPRINTS)
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5. SMILES
SMILES contains the same information as might be found in an extended connection table.
The primary reason SMILES is more useful than a connection table is that it is a linguistic construct, rather than a computer data structure.
SMILES is a true language, albeit with a simple vocabulary (atom and bond symbols) and only a few grammar rules.
SMILES can be canonicalised. I.e. there is a unique, universal “name” for a structure
SMILES representations of structure can in turn be used as “words” in the vocabulary of other languages designed for storage and retrieval of chemical information .E.g HTML, XML or query languages such as SQL.
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6. SMILES syntax [atom]bond[atom] etc
atom : ‘[‘ <mass> symbol <chiral> <hcount> <sign<charge>> <‘:’class> ‘]’ ;
bond : <empty> | ’-’ | ‘=‘ | ‘#’ | ‘:’ | ‘.’
Common elements, in the organic subset B,C,N,O,P,S,F,Cl,Br,I, in their lowest common valence state(s), can be written without brackets. If bonds are omitted, they default to single or aromatic, as appropriate, for juxtaposed atoms.
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7. Example SMILES
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8. SMARTS
In the SMILES language, there are two fundamental types of symbols: atoms and bonds. Using these SMILES symbols, one can specify a molecule's graph (its "nodes" and "edges") and assign "labels" to the components of the graph (that is, say what type of atom each node represents, and what type of bond each edge represents).
The same is true in SMARTS: One uses atomic and bond symbols to specify a graph. However, in SMARTS the labels for the graph's nodes and edges (its "atoms" and "bonds") are extended to include "logical operators" and special atomic and bond symbols; these allow SMARTS atoms and bonds to be more general. For example, the SMARTS atomic symbol [C,N] is an atom that can be aliphatic C or aliphatic N; the SMARTS bond symbol "~" (tilde) matches any bond
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9. Example SMARTS
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10. Useful SMARTS Heavy atom [!$([#6,#7,#8,#9,#15,#16,#17,#35,#53])]
Rotatable bonds
Secondary amides
H-donors [!#6;!H0]
H-acceptors [$([!#6;+0]);!$([F,Cl,Br,I]);!$([o,s,nX3]);!$([Nv5,Pv5,Sv4,Sv6])]
Isolating carbons [#6;!$(C(F)(F)F);!$(c(:[!c]):[!c]);!$([#6]=,#[!#6]);!$([#6;!+0])]
Stereo atoms [$([X4&!v6&!v5;H0,H1]),$([SX3]([#6])([#6])~O)]
Stereo bonds [CX3;!H2]=[CX3;!H2]
Stereo allenes [CX3;H0]=C=[CX3;H0,H1]
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11. Rotatable bonds [!$(*#*)&!D1]-&!@[!$(*#*)&!D1]
An atom which is
NOT triply bonded to another atom
AND NOT 1-connected ( I.e. Not terminal )
Bonded by
A single bond
AND NOT a ring bond
to the same type of atom
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12. Chemical Information Concepts in Discovery
Matching
Total
Partial
Similarity
Qualitative
Quantitative
Both matching and similarity are opinions as they depend on descriptors.
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13. Filtering
Quite often you may wish to eliminate compounds which are inappropriate for some activity or test.
E.g. Delete any molecule from a list which contains a “heavy metal” i.e. a non-common element
> $CONTRIB/smarts_filter -v \ ‘[!$([#6,#7,#8,#9,#15,#16,#17,#35,#53])]’
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14. Counting things Count matches to patterns defined in SMARTS
Molecular formula
H-donors
H-acceptors
Rotatable bonds
Chiral centres
Rings
Fragments
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15. Example Molecular formula C13H22N4O3S H-donors 2 H-acceptors 6
Rotatable bonds 8
Chiral centres 1
Rings 1
Fragments 6
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16. Estimating Measured Properties
Any property which is an additive constitutive property of a molecule can be calculated by
counting the matches of the constituent patterns
lookup the weight for the pattern
summing the products of the count and individual pattern weights.
apply any correction factors
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17. Examples of properties to calculate
Molecular Weight
logP
Parachor
Molar Volume
Molar Refractivity
……….
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18. Molecular weight: a simple example
Molecular formula
(count(atom(i))*atomic_weight(atom(i)))
Accuracy depends on accuracy of atomic weights ( IUPAC)
C13H22N4O3S
(average molecular weight )
( accurate mass of commonest isotope)
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19. CLOGP: A more complicated example
Algorithmic definition of fragment
Pattern = NOT an isolating carbon
Match the pattern to find all the fragments
Look up the fragment value(s) ( if it exists ) using the unique string(s) from the match.
Accumulate the values for fragments and non-fragments (isolating carbons).
Correct for proximity
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20. CLOGP example 2 * Cl +1.880 guanidyl –1.930 2 * C +0.390 6 * c +0.780
7 * H
Proximity –0.984
Total
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21. Estimating values for concepts
Flexibility
Ratio of number of rotatable bonds to total number of bonds
Rigidity
Molecular similarity between original molecule and molecules formed by breaking all rotatable bonds
Difficulty of synthesis
Ratio of number of potential chiral centres weighted for rings to total number of heavy atoms in a molecule
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22. Example Flexibility 0.38 Rigidity 0.3819 Difficulty of synthesis 0.05
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23. Example Flexibility 0.38(0.00) Rigidity 0.3819(1.00)
Difficulty of synthesis (0.85)
Figures in parentheses for morphine
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24. Relationships between compounds
Compound sets
Molecular descriptors
Fingerprints etc
Similarity measures
Tanimoto etc
Clustering
Jarvis-Patrick etc
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25. Relationships between compounds
Mixtures
Molecular descriptors
Modal Fingerprints etc
Similarity measures
Tanimoto etc
Prototypes
Family Resemblance
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26. Relationships between compounds
Reactions
Molecular descriptors
Fingerprints
Rôles
Schemes/pathways
Similarity and clustering
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27. Examples Creating a spreadsheet of properties.
Non-standard fingerprinting and similarity.
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28. Don’t let the hedgehogs take over…..
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29. Don’t let the hedgehogs take over…..
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