Chemistry and reactions from non-US patents

248th ACS National Meeting, San Francisco CA, USA 10th August 2014

Chemistry and reactions from non-US patents

Daniel Lowe and Roger Sayle

NextMove Software

Cambridge, UK


Topics

• Coverage of USPTO vs EPO patents

• Extraction of non-chemical-compound data

• Can text-mining provide insights into reaction informatics


What am I missing by only using the USPTO data?


EPO and USPTO background

• USPTO: 303k grants published in 2013

• EPO: 67k grants published in 2013

• EPO patents must be in one or more of English, French or German

• USPTO documents are all in English


Epo/USPTO Compounds (using StdInChI)

3,345,877

2,674,069

445,588

USPTO = 1976-2013 patent grants + 2001-2013 patent applications EPO = 1978-2013 patent grants and applications


Epo/USPTO Reactions (using separately sorted StdInChIs for reactants/agents and products)

706,524

317,533

99,681

USPTO = 1976-2013 patent grants + 2001-2013 patent applications EPO = 1978-2013 patent grants and applications


Effect of different Languages

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

19

78

19

79

19

80

19

81

19

82

19

83

19

84

19

85

19

86

19

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19

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19

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19

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19

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19

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19

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19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

20

12

20

13

Nu

mb

er

of

stru

ctu

res

ext

ract

ed

English

German

French

Translation performed by Lexichem v2014.Jun


First Disclosure Occurrence by Language

Excluding compounds disclosed by USPTO (1976-

2013) patents

All compounds


Timeliness

• Competitive intelligence requires up to date information

• Methodology:

– Consider compounds present in both EPO and USPTO date not disclosed by either prior to 2006

– Compare the earliest publication date for each compound


0

20000

40000

60000

80000

100000

120000

140000

160000

180000

US morethan 5years

earlier

US 3-5years

earlier

US 2-3years

earlier

US 1-2years

earlier

US 6-12monthsearlier

US 3-6monthsearlier

US 1-3monthsearlier

US 1-4weeksearlier

Within aweek

US 1-4weekslater

US 1-3months

later

US 3-6months

later

US 6-12months

later

US 1-2yearslater

US 2-3yearslater

US 3-5yearslater

US morethan 5yearslater

Co

mp

ou

nd

dis

clo

sure

s

Average lag between EPO/USPTO

Compounds first disclosed between 2006-2013

Mean: US 540 days earlier


0

10000

20000

30000

40000

50000

60000

70000

US morethan 5years

earlier

US 3-5years

earlier

US 2-3years

earlier

US 1-2years

earlier


US 3-6monthsearlier

US 1-3monthsearlier

US 1-4weeksearlier

Within aweek

US 1-4weekslater

US 1-3months

later

US 3-6months

later

US 6-12months

later

US 1-2yearslater

US 2-3yearslater

US 3-5yearslater


Co

mp

ou

nd

dis

clo

sure

s

Applications Vs Applications




0

20000

40000

60000

80000

100000

120000

140000

US morethan 5years

earlier

US 3-5years

earlier

US 2-3years

earlier

US 1-2years

earlier


US 3-6monthsearlier

US 1-3monthsearlier

US 1-4weeksearlier

Within aweek

US 1-4weekslater

US 1-3months

later

US 3-6months

later

US 6-12months

later

US 1-2yearslater

US 2-3yearslater

US 3-5yearslater


Co

mp

ou

nd

dis

clo

sure

s

Grants vs Grants




Epo/US 1976-2013 and chembl19 overlap

187,486

1,155,034 6,278,048


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Patents 4-5years earlier





Patents 0-1years later





Average lag between Patents/CHEMbl19


Mean: Patents 345 days earlier


Other data in patents

• Genes/proteins

• Reactions including role of reagents

• Physical quantities

• Spectra

• Diseases

• Organisms

• Companies

• …


Gene/protein identification

• Identify trends in drug target popularity

• Count number of patents (in a year) mentioning a given gene or its gene products and map to the HGNC symbol

• Many genes are referenced by short ambiguous terms hence great care is required to have good precision



(Hepatocyte growth factor receptor) (Epidermal growth factor receptor)

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

0.80%

0.90%

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

1976197819801982198419861988199019921994199619982000200220042006200820102012

Pe

rce

nta

ge o

f p

rote

in/g

en

e m

en

tio

ns

in t

hat

ye

ar (

Ch

EMB

L)

Pe

rce

nta

ge o

f p

rote

in/g

en

e m

en

tio

ns

in t

hat

ye

ar (

Pat

en

ts)

EGFR

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

0.12%

0.14%

1976197819801982198419861988199019921994199619982000200220042006200820102012

Pe

rce

nta

ge o

f p

rote

in/g

en

e m

en

tio

ns

in t

hat

ye

ar (

Ch

EMB

L)

Pe

rce

nta

ge o

f p

rote

in/g

en

e m

en

tio

ns

in t

hat

ye

ar (

Pat

en

ts)

MET



(Mammalian target of rapamycin) (Tissue plasminogen activator)

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

0.40%

0.45%

0.00%

0.02%

0.04%

0.06%

0.08%

0.10%

0.12%

0.14%

1976197819801982198419861988199019921994199619982000200220042006200820102012

Pe

rce

nta

ge o

f p

rote

in/g

en

e m

en

tio

ns

in t

hat

ye

ar (

Ch

EMB

L)

Pe

rce

nta

ge o

f p

rote

in/g

en

e m

en

tio

ns

in t

hat

ye

ar (

Pat

en

ts)

MTOR

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

0.40%

0.00%

0.05%

0.10%

0.15%

0.20%

0.25%

0.30%

0.35%

0.40%

0.45%

0.50%

1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

Pe

rce

nta

ge o

f p

rote

in/g

en

e m

en

tio

ns

in t

hat

ye

ar (

Ch

EMB

L)

Pe

rce

nta

ge o

f p

rote

in/g

en

e m

en

tio

ns

in t

hat

ye

ar (

Pat

en

ts)

PLAT


Melting/Boiling Point Extraction


Melting/Boiling Point Extraction

Over 99k so far!


CHEMICAL reactions


Predicting yield

• Features to consider:

• 2D fingerprints (especially around the reaction centers)

• Reaction type

• Temperature

• Time

• Change in complexity e.g. chiral centres


Data extraction

• Can pull out yields, quantities, times and temperatures

• Can sanity check text-mined yield by calculating from amounts (or even masses)

𝑚𝑎𝑠𝑠 𝑜𝑓 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑(𝑔)

𝑚𝑜𝑙𝑎𝑟 𝑚𝑎𝑠𝑠 [𝑐𝑎𝑙𝑐 𝑓𝑟𝑜𝑚 𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒](𝑔𝑚𝑜𝑙−1)= 𝑚𝑜𝑙𝑠 𝑜𝑓 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑

𝑚𝑜𝑙𝑠 𝑜𝑓 𝑝𝑟𝑜𝑑𝑢𝑐𝑡

𝑚𝑜𝑙𝑠 𝑜𝑓 𝑙𝑖𝑚𝑖𝑡𝑖𝑛𝑔 𝑟𝑒𝑎𝑐𝑡𝑎𝑛𝑡= %𝑦𝑖𝑒𝑙𝑑


Outliers inevitable


sCale vs yield

2001-2013 US applications, Suzuki couplings


Temperatures


Identify Synthetic Routes

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Intermediates 197702 103114 56611 31403 17268 9230 5057 2701 1256 639 301 136 58 15 5 2

Terminal Products 385149 149445 81837 47579 27670 16619 9320 5263 2511 1330 678 373 111 63 8 6 5

0

100000

200000

300000

400000

500000

600000

700000

Occ

urr

en

ces

Number of steps

Intermediates

Terminal Products


Number of steps vs Complexity

ringComplexityScore = log10(nRingBridgeAtoms + 1) + log10(nSpiroAtoms + 1) stereoComplexityScore = log10(nStereoCenters + 1) macrocyclePenalty = log10(nMacrocycles + 1) sizePenalty = natoms1.005 − natoms

∑ Ertl & Schuffenhauer 2009 doi:10.1186/1758-2946-1-8


Number of steps vs Complexity

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 10 11

Nu

mb

er

of

pro

du

ct h

eav

y at

om

s

Number of steps

Average number of heavy atoms


Yield vs Change in Complexity

2001-2013 US applications, all reactions


Trends in Reaction Types

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

19

76

19

77

19

78

19

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19

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19

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19

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Suzu

ki c

ou

plin

gs a

s a

pe

rce

nta

ge o

f re

acti

on

s in

a y

ear

Classification performed using NameRXN


Trends In Solvent Use

0.0%

5.0%

10.0%

15.0%

20.0%

19

76

19

77

19

78

19

79

19

80

19

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Pe

rce

nta

ge o

f re

acti

on

s in

th

at y

ear

Tetrahydrofuran

Dichloromethane

Water

Dimethylformamide

Methanol

Ethyl acetate

Ethanol

1,4-Dioxane

Toluene

Acetonitrile

Acetic acid

Chloroform

Acetone

Benzene


Are solvents getting greener?

1976 2013

Water (21%) Tetrahydrofuran (15%)

Ethanol (11%) Dichloromethane (14%)

Benzene (8%) Water (13%)

Methanol (7%) Dimethylformamide (10%)

Tetrahydrofuran (5%) Methanol (8%)

Dichloromethane (4%) Ethyl acetate (7%)

Dimethylformamide (4%) Ethanol (5%)

Acetic acid (4%) 1,4-Dioxane (4%)

Chloroform (3%) Toluene (3%)

Acetone (3%) Acetonitrile (3%)

Total for top 10: 71% 82%


Conclusions

• A significant amount of novel chemistry, from EPO patents, comes from the non-English patents

• Compounds disclosed by both the USPTO and EPO are on average published earlier by the USPTO but for many compounds an EPO patent will be the earlier disclosure

• Gene/protein identification can identify clear changes in patenting behaviour over time

• Text mining provides the tools to answer many reaction informatics questions


Acknowledgements

• Funding provided by:


Thank you for your time!

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Chemistry and reactions from non-US patents

Technology

Transcript of Chemistry and reactions from non-US patents