This document summarizes advances in automatic chemical spelling correction. It discusses using edit distances and dynamic programming to correct spelling errors. Examples are given of correcting errors in chemical names, CAS registry numbers, and protein target names. Benchmarking on patent data shows automatic correction improves recall of chemical entity recognition by around 20-40%. The techniques allow fuzzy matching of chemical structures and nomenclature rules.

1. Advances in Automatic Chemical
Spelling Correction
Roger Sayle and Daniel Lowe
NextMove Software
Cambridge, UK
ACS National Meeting, Philadelphia, USA 19th August 2012

2. example spelling errors
• Sample misspellings of pyrimidine in US patent
grants since the beginning of this year.
Incorrect Name US Patent No. Issue Date
pryimidine 8093264 10th January 2012
pyrmidine 8097728 17th January 2012
pyrimdine 8114996 14th February 2012
pyrimidne 8129897 6th March 2012
pyrimidinc 8148339 3rd April 2012
pyridmidine 8158627 8th May 2012
ACS National Meeting, Philadelphia, USA 19th August 2012

3. previous work
• Roger Sayle, Paul Hongxing Xie and Sorel Muresan,
“Improved Chemical Text Mining of Patents with
Infinite Dictionaries and Automatic Spelling
Correction”, Journal of Chemical Information and
Modeling, Vol. 52, No. 1, pp. 51-62, 2012.
• G.H. Kirby, M.R. Lord, J.D. Rayner, “Computer
Translation of IUPAC Systematic Organic Chemical
Nomenclature. 6. (Semi)automatic name correction,
Journal of Chemical Information and Computer
Science, Vol. 31, pp. 153-160, 1991.
ACS National Meeting, Philadelphia, USA 19th August 2012

4. STring EDIt Distance
• The Levenshtein Distance (Levenshtein 1965) is the
minimum number of edits (insertions, deletions or
substitutions) required to transform one string into
another.
• The Damerau-Levenshtein Distance is an extension of
Levenshtein Distance to include transposition of two
adjacent characters.
• The distances can be efficiently computed with
dynamic programming using the Needleman-
Wunsch-Sellers alignment algorithm (bioinformatics).
ACS National Meeting, Philadelphia, USA 19th August 2012

5. needleman-wunsch-sellers
c l o r a m p h e n i c a l
c
h
l
o
r
a
m
p
h
e
n
i
c
o
l
ACS National Meeting, Philadelphia, USA 19th August 2012

6. needleman-wunsch-sellers
c l o r a m p h e n i c a l
c
h
l
o
r
a
m
p
h
e
n
i
c
o
l
ACS National Meeting, Philadelphia, USA 19th August 2012

7. further complications
• Edit operations can have their own specific penalties.
• The latest implementation supports transpositions,
to catch spelling mistakes such as “chlorofrom”.
• Some dictionaries to match against have tens of
millions of entries, others are infinite.
• The start and end of the input isn’t known in free-
text and are assigned on the quality of the match.
• Correct nesting of parenthesis and brackets needs to
be enforced as part of the matching process.
• In summary - It’s mind bogglingly complicated.
ACS National Meeting, Philadelphia, USA 19th August 2012

8. dictionaries as automata
• Nitrogen containing heterocycles as minimal DFA:
– Pyrrole, Pyrazole, Imidazole, Pyrdine, Pyridazine,
Pyrimidine, Pyrazine
ACS National Meeting, Philadelphia, USA 19th August 2012

9. Example iupac-like grammar
• More generally, still CaffeineFix FSMs can represent
formal grammars, i.e. infinite dictionaries.
alk := “meth” | “eth” | “prop” | “but”
parent := alk “ane”
subst := “bromo” | “chloro” | “fluoro”
locant := “1” | “2” | “3” | “4” /* any digit */
prefix := [ prefix “-” ] [ loc “-” ] subst
| [ prefix ] subst
name := [ prefix [ “-” ] ] parent
ACS National Meeting, Philadelphia, USA 19th August 2012

10. Iupac-like grammar examples
• methane
• chloroethane
• 2-bromo-propane
• chloro-bromo-methane
• 1-fluoro-2-chloro-ethane
• chlorofluoromethane
• 4-bromomethane
• 1-chloro-1-chloro-1-chloro-methane
ACS National Meeting, Philadelphia, USA 19th August 2012

11. Representing grammars as dFAs
Backward edges allow matching an infinite number of words.
ACS National Meeting, Philadelphia, USA 19th August 2012

12. current iupac grammar FSM
• As of July 2012, the current CaffeineFix grammar
contains nearly 1.2 million edges.
• This grammar covers...
– 99.15% (232144/234142) names in the NCI00 database.
– 95.28% (67995/71367) names in the Maybridge catalogue.
– 95.27% (25890/48167) names in the Keyorganics catalogue.
• These figures are comparable to name-to-structure
conversion rates on these names.
ACS National Meeting, Philadelphia, USA 19th August 2012

13. SPelling correction examples
• lH-ben zimidazole → 1H-benzimidazole
• triphenylposhine → triphenylphosphine
• 4- (2-ADAMANTYLCARBAM0YL) -5-TERT-BUTYL-
PYRAZOL-1-YL] BENZOIC ACID →
4-(2-adamantylcarbamoyl)-5-tert-butyl-pyrazol-1-
yl]benzoic acid
• didec-2-ene → dodec-2-ene
• spiro[2.2]hexane → spiro[2.3]hexane
ACS National Meeting, Philadelphia, USA 19th August 2012

14. low cost “frequent” edit ops
• A number of common corrections are so frequent as
to be given a lower (free) cost.
1. Deletion of whitespace.
2. Deletion of a hyphen (where not anticipated)
3. Substitution of “l” (lower case el) for “1” (one).
4. Substitution of “I” (upper case ey) for “l” (el) or “1” (one).
5. Substitution of “rn” by “m”.
6. Substitution of “1” (one) by “l” (el).
7. Substitution of “φ” by “rp” [OCR artifact].
ACS National Meeting, Philadelphia, USA 19th August 2012

15. handle with care
• Alas, introducing automatic spelling correction (fuzzy
matching) to entity recognition often requires the
introduction of white word lists to avoid problems.
– herein → heroin
– aspiring → aspirin
– cranium → uranium
– ability → abilify
• More aggressive correction leads to more problems:
– “be that the line” → methantheline
– “park on a zone” → parconazole
ACS National Meeting, Philadelphia, USA 19th August 2012

16. benchmarking and analysis
• To quantify the benefits of automatic spelling
correction to “real world” chemical text mining we
analysed the first 28 weeks of US patent grants from
2012.
• This corresponds to 145,473 documents, issued
between 3rd January 2012 and 10th July 2012.
• A total of 4,061,670 IUPAC-like systematic names
were identified, with 1,816,317 unique patent/name
pairs.
• OPSIN interprets 3,722,399 and 1,647,402.
ACS National Meeting, Philadelphia, USA 19th August 2012

17. total molecule entities
All Extracted Molecule Entities OPSIN Interpeted Molecule
Entities
434487,
97472, 87719, 12%
2% 552533, 2%
14%
Correct
Correct
Simple
Simple
D=1
D=1
3411665, 3200193
84% , 86%
Using correction retrieves ~19% more entities
and ~16% more OPSIN recognizable names.
ACS National Meeting, Philadelphia, USA 19th August 2012

18. unique molecule entries
All Extracted Molecule Entities OPSIN Interpeted Molecule
Entities
75027,
4%
310359, 67317,
4% 236330,
17% 14%
Correct Correct
Simple Simple
D=1 D=1
1430931,
79% 1343755,
82%
The effect is more pronounced with patent-cmpnd
pairs with a +27% improvement over no correction.
ACS National Meeting, Philadelphia, USA 19th August 2012

19. influence on n2s software (opsin)
Interpretation Count Fraction
Not Interpretable 116216 17.88%
Before not After 11779 1.81%
After not Before 270453 41.61%
Same Before/After 181693 27.95%
Different Before/After 69864 10.75%
Total 650005 100.00%
Although some valid names are lost by correction,
overall the effect is overwhelmingly positive.
ACS National Meeting, Philadelphia, USA 19th August 2012

20. break-down of edit operations
Edit Operation Count Fraction
Deletion 392324 47.50%
Insertion 232493 28.15%
Substitution 198438 24.03%
Transposition 2670 0.32%
Total 825,925 100.00%
ACS National Meeting, Philadelphia, USA 19th August 2012

21. drug dictionary entities
All Drug Dictionary Entities Unique Drug Dictionary Entities
71684, 30687,
6141, 0% 7% 4119, 1% 7%
Correct Correct
Simple Simple
D=1 D=1
1013711, 395482,
93% 92%
Some improvement is seen with drug dictionaries,
but there’s little benefit in fixing simple OCR issues.
ACS National Meeting, Philadelphia, USA 19th August 2012

22. target dictionary entries
• Simple correction of ChEMBL protein target names
• prostaglandin H- 2 synthase- 1 → prostaglandin H2 synthase 1
• Alanine amino-transferase → Alanine aminotransferase
• acetyl cholinesterase → acetylcholinesterase
• cyclooxy-genase-2 → cyclooxygenase-2
• MAP kinase ERK-2 → MAP kinase ERK2
• HEC-GLCNAC-6-ST → HEC-GLCNAC6ST
• Herne Oxygenase → Heme Oxygenase
• Prealburnin → Prealbumin
• p110- delta → p110delta
ACS National Meeting, Philadelphia, USA 19th August 2012

23. non-word spelling correction
• Automatic correction technology can also be applied
to entities other than words or IUPAC-like chemical
nomenclature.
• For example, Chemical Abstract Service’s registry
numbers.
ACS National Meeting, Philadelphia, USA 19th August 2012

24. cas registry number grammar
9
0-9
-
8 -
0-9
7 -
0-9 0-9 0-9 - 0-9
10 11 12 13 14
-
0-9 6
0-9 0-9
1-4 1 3
5
0 0-9 -
5-9
0-9
2 4
-
• Two to seven digits, followed by a hyphen, two digits,
a hyphen and a final check digit
– e.g. 7732-18-5
• Regular Expression: (([1-9]d{2,5})|([5-9]d))-dd-d
ACS National Meeting, Philadelphia, USA 19th August 2012

25. Cas check digit calculation
• More generally CaffeineFix’s finite state machines
can do limited processing...
• The final check digit of a CAS number is calculated by
series term summation modulo 10.
• The last digit time 1, the previous digit times 2, the
previous digit times 3, and computing the sum
modulo 10.
• The CAS number for water is 7732-18-5.
• The checksum 5 is calculated as (1x8 + 2x1 + 3x2 +
4x3 + 5x7 + 6x7) mod 10 = 5.
ACS National Meeting, Philadelphia, USA 19th August 2012

26. Fsm for matching cas check digits
21 31 41 51 61 71 81
11 23 33 43 53 62 72 82
13 25 35 45 55 63 73 83
6 17 27 37 47 57 64 74 84
8 19 29 39 49 59 65 75 85
0
2 16 22 32 42 52 66 76 86
4 18 24 34 44 54 67 77 87
12 26 36 46 56 68 78 88
14 28 38 48 58 69 79 89
30 40 50 60 70 80 90
ACS National Meeting, Philadelphia, USA 19th August 2012

27. cas number correction example
• 7732-18-8? Did you mean...
– 7732-18-5
– 7732-11-8
– 77328-18-8
– 7733-18-8
– 77342-18-8
– 77392-18-8
– 71732-18-8
– 76732-18-8
– 97732-18-8
ACS National Meeting, Philadelphia, USA 19th August 2012

28. take home message
• Adding advanced automatic chemical spelling
correction to an annotation pipeline typically
improves recall by about 20-40%.
– Andrew Hinton, “Benchmarking ChemAxon’s Name-to-Structure batch
tool on Patent Data”, 2011 ChemAxon EUGM, Budapest.
– Sorel Muresan, “Automated Spelling Correction to Improve Recall
Rates of Name-to-Structure Tools for Chemical Text Mining”, 2011
ChemAxon EUGM.
ACS National Meeting, Philadelphia, USA 19th August 2012

29. acknowledgements
• Daniel Lowe, NextMove Software.
• Sorel Muresan and Paul Hongxing Xie, AstraZeneca.
• Thank you for your time.
• Any questions?
ACS National Meeting, Philadelphia, USA 19th August 2012