Pistachio "Fantastic reactions and how to use them" - John Mayfield, Ingvar Lagerstedt and Roger Sayle - NextMove Software
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Pistachio
“Fantastic reactions and how to use them”
John Mayfield, Ingvar Lagerstedt and Roger Sayle
NextMove Software
NIH Virtual Workshop on Reaction Informatics, May 2021
What is Pistachio?
A document centric database of 13.3 million reactions
Automatically extracted from U.S., European and WIPO patents
JSON and SMILES provided for bulk analysis/model building
Containerised WebApp for exploring and querying the data
Aim is to extract reactions as described in the original document,
Warts and all
NIH Virtual Workshop on Reaction Informatics, May 2021
History
DEPARTMENT OF CHEMISTRY
Extraction of chemical
structures and reactions
from the literature
Daniel Mark Lowe
Pembroke College
This dissertation is submitted for the degree of Doctor of Philosophy
June 2012
Daniel’s PhD Thesis (2012)
repository.cam.ac.uk/handle/1810/244727
Original Open-Source Project
dan2097/patent-reaction-extraction USPTO CC-Zero Subset (3.7 million)
Chemical_reactions_from_US_patents_1976-Sep2016_/5104873
We use an internal fork built using Pistachio (13.3 million)
LeadMine instead of OSCAR4. nextmovesoftware.com/pistachio
Primarily improves chemical entity and
physical quantity recognition, spelling
correction, etc.
NIH Virtual Workshop on Reaction Informatics, May 2021
Data Impact
Christos Nicolaou et al. The Proximal Lilly Collection: Mapping, Exploring and Exploiting
Feasible Chemical Space J. Chem. Inf. Model., 2016, 56 (7), pp 1253–1266
Nadine Schneider et al. Big Data from Pharmaceutical Patents: A Computational Analysis of
Medicinal Chemists’ Bread and Butter. J. Med. Chem., 2016, 59 (9), pp 4385–440
Bowen Liu et al. Retrosynthetic reaction prediction using neural sequence-to-sequence
models. ACS Cent. Sci., 2017, 3 (10), pp 1103–111
Philippe Schwaller et al. Molecular transformer: A model for uncertainty-calibrated chemical
reaction prediction ACS Cent. Sci., 2019, 5 (9), pp 1572–158
Connor Coley et al. Prediction of Organic Reaction Outcomes Using Machine Learning. ACS
Cent. Sci., 2017, 3 (5), pp 434–44
Philippe Schwaller et al. Extraction of organic chemistry grammar from unsupervised learning
of chemical reactions. Sci. Adv., 2021, 7 (15
Alessandra Toniato et al. Unassisted noise reduction of chemical reaction datasets. Nat. Mach.
Intell. 202
Amol Thakkar et al. Arti cial intelligence and automation in computer aided synthesis
planning. React. Chem. Eng., 2021, 6
NIH Virtual Workshop on Reaction Informatics, May 2021
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3
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3
3
2Important: The same reaction will occur in application/grant,
related patents, sketches/text and different authorities (WIPO/
EPO/USPTO). Using RInChI without any role normalisation ~4.2
million.
Often identical but not always - different description/yield/actions.
NIH Virtual Workshop on Reaction Informatics, May 2021Important: The same reaction will occur in application/grant,
related patents, sketches/text and different authorities (WIPO/
EPO/USPTO). Using RInChI without any role normalisation ~4.2
million.
Often identical but not always - different description/yield/actions.
NIH Virtual Workshop on Reaction Informatics, May 2021Amy Fried and Robert Wilkening
Merck Sharp & Dohme
Estrogen receptor modulators.
US 7151196 B2 [0236] (19-Dec-2006)
Example 2, Step 2
A solution of 2-(2-hydroxyethyl)-5-methoxy-1-indanone (105 mg, 0.51 mmol) in methanol (2.0 mL) at room temperature was treated with
ethyl vinyl ketone (EVK, 0.102 mL) and 0.5M sodium methoxide in methanol (0.204 mL, 0.1 mmol). The mixture was stirred in a capped
ask and heated in an oil bath at 60° C. for 8 hours. After cooling, the reaction mixture was diluted with EtOAc (25 mL), washed with 0.2N
HCl (15 mL), water (15 mL), and brine (15 mL), dried over MgSO4, ltered, and evaporated under vacuum to a ord 2-(2-hydroxyethyl)-5-
methoxy-2-(3-oxopentyl)-1-indanone as an oil.
Dann Parker, Ronald Ratcli e, Kenneth Wildonger and Robert Wilkening
Merck Sharp & Dohme
Estrogen Receptor Modulators
EP 1257264 B1 [0261] (14-Sep-2011)
EXAMPLE 34, Step 2
A solution of 2-(2-hydroxyethyl)-5-methoxy-1-indanone (105 mg, 0.51 mmol) in methanol (2.0 mL) at room temperature was treated with
ethyl vinyl ketone (EVK, 0.102 mL) and 0.5M sodium methoxide in methanol (0.204 mL, 0.1 mmol). The mixture was stirred in a capped
ask and heated in an oil bath at 60°C for 8 hours. After cooling, the reaction mixture was diluted with EtOAc (25 mL), washed with 0.2N
HCl (15 mL), water (15 mL), and brine (15 mL), dried over MgSO4, ltered, and evaporated under vacuum to a ord 2-(2-hydroxyethyl)-5-
methoxy-2-(3-oxopentyl)-1-indanone (138 mg, 93% yield) as an oil.
NIH Virtual Workshop on Reaction Informatics, May 2021
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ff
ffUSPTO vs Pistachio Data
• Updated quarterly U.S. Grant Text 3,366,399 2021-05-18
• EPO, WIPO patents U.S. Appl. Text 3,629,411 2021-05-13
WIPO PCT Text 1,520,596 2021-05-06
• USPTO sketches Euro. Grant Text 1,074,590 2021-05-12
• NameRxn Classification/AAM Euro. Appl. Text 702,035 2021-05-12
– Improved role assignment U.S. Grant Sketch 1,211,521 2021-05-18
U.S. Appl. Sketch 1,834,132 2021-05-13
– NameRxn 71.5% coverage
• Example/Step Labels
• Solvent Mixtures
• Solvent associations
• Document Assignees, Targets and Diseases
• Continual tweaks based on feedback
NIH Virtual Workshop on Reaction Informatics, May 2021USPTO vs Pistachio Data
Pistachio is a “super-set” of USPTO
but not strictly so…
• NameRxn filtering/mapping
• Improved/changed name-to-
structure, roles, sectioning
• Structure normalisation differences
• Whack-a-mole/pachinko machine
– Obvious sensible change can have
unforeseen consequences
The NextMove’s pachinko machine
NIH Virtual Workshop on Reaction Informatics, May 2021Regression Testing
USPTO vs Pistachio Data
• Reactions from USPTO Application Text 2001-22nd Sep 2016
– 1,939,253 CC-Zero Subset
– 2,568,513 Pistachio
– 458,995 common (-1,480,258,+ 2,109,518) by SMILES
NIH Virtual Workshop on Reaction Informatics, May 2021USPTO vs Pistachio Data
• Reactions from USPTO Application Text 2001-22nd Sep 2016
– 1,939,253 CC-Zero Subset
– 2,568,513 Pistachio
– 1,386,306 common (-552,947,+1,182,207) by ~RInChI
NIH Virtual Workshop on Reaction Informatics, May 2021USPTO vs Pistachio Data
• Reactions from USPTO Application Text 2001-22nd Sep 2016
– 1,939,253 CC-Zero Subset
– 2,568,513 Pistachio
– 1,465,946 common (-473,307,+1,102,567) by norm SMILES
NIH Virtual Workshop on Reaction Informatics, May 2021USPTO vs Pistachio Data
• Reactions from USPTO Application Text 2001-22nd Sep 2016
– 1,939,253 CC-Zero Subset
– 2,568,513 Pistachio
– 1,866,314 common (-72,939,+702,199) by paragraph Id
NIH Virtual Workshop on Reaction Informatics, May 2021Overview of extraction
Text Extraction
Sectioning
Tagging/Tokenization
Parsing
Action Phrases
Reaction Assembly
Example from US20020133011A1 [0070]
NIH Virtual Workshop on Reaction Informatics, May 2021Text Extraction
Sectioning
Missed break Tagging/Tokenization
Parsing
Action Phrases
Reaction Assembly
Extra break
Examples from WO 2020/239862 A1 PatentScope OCR
NIH Virtual Workshop on Reaction Informatics, May 2021Text Extraction
Sectioning
Tagging/Tokenization
Parsing
Action Phrases
Reaction Assembly
UnitType.Percent
UnitType.Mass
UnitType.Percent QuantityType.Purity
QuantityType.Yield
NIH Virtual Workshop on Reaction Informatics, May 2021Text Extraction
Sectioning
Tagging/Tokenization
Parsing
Action Phrases
Reaction Assembly
NIH Virtual Workshop on Reaction Informatics, May 2021Text Extraction
Sectioning
Type Add
Compounds Tagging/Tokenization
• ethyl cyanoacetate (mass=13.56 g)
• ethyl 4-fluorocinnamate (mass=19.4 g)
• sodium ethoxide (mass=2.3 g, vol=50 ml) Parsing
Conditions
• 2-3 minutes
• 60° C. Type Heat Action Phrases
Conditions
• 1 hour
Reaction Assembly
Type Cool
Conditions Type Yield
• 5° C. Compounds
• 2-cyano-3-(flurophenyl)-glutarate
(mass=23 g, yield=74%, purity=98%)
NIH Virtual Workshop on Reaction Informatics, May 2021Text Extraction
Segmentation
Tagging/Tokenization
Parsing
Action Phrases
Reaction Assembly
Preliminary role assignment based on action, surrounding
context and dictionaries (common solvents/catalysts)
NIH Virtual Workshop on Reaction Informatics, May 2021ChEMU 2020 Evaluation Lab
Run Exact matching Relaxed matching
F1-score Precision Recall F1-score Precision Recall
Task 1 0.8983 0.9042 0.8924 0.9240 0.9301 0.9181
Task 2 0.8977 0.9441 0.8556 n/a n/a n/a
end-2-end 0.8026 0.8492 0.7609 0.8196 0.8663 0.7777
end-2-end 0.8255 0.8746 0.7816 0.8420 0.8909 0.7983
(after deadline)
Daniel Lowe and John May eld. Extraction of reactions from patents using grammars. 202
http://ceur-ws.org/Vol-2696/paper_221.pdf
Nguyen D.Q. et al. (2020) ChEMU: Named Entity Recognition and Event Extraction of
Chemical Reactions from Patents. In: Jose J. et al. (eds) Advances in Information
Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12036. Springer, Cham.
https://doi.org/10.1007/978-3-030-45442-5_74
NIH Virtual Workshop on Reaction Informatics, May 2021
fi
0Sketch Extraction
Example 26, US 9718816 B2 Step 1
Step 2
Step 3
NextMove’s Praline
Step 4
etc..
US 09718816 B2 Example 26
John May, et al. Sketchy Sketches: Hiding Chemistry in Plain Sight. Seventh Joint Sheffield Conference on
Cheminformatics. 2016
NIH Virtual Workshop on Reaction Informatics, May 2021Overview of Filtering/Mapping
Reaction Filtering - Text
Rebond
1 >= Precursors = Products = Num PrecursorsReaction Filtering - Sketch
Specific Reaction
1 >= Precursors Reject
1 >= Products
Sane
NameRxn AAM
Mapped
Fix Roles
NIH Virtual Workshop on Reaction Informatics, May 2021ROLE FIX
1) Move all agents to reactants
2) Atom-Atom Mapping - Michael addition (3.11.92)
3) Move unmapped reactants back to agents
NIH Virtual Workshop on Reaction Informatics, May 2021Why NameRxn?
• 1,543 rule based classes - easy to update a mapping disagreement
4.1.6 Cyclic Beckmann rearrangement
• Higher precision/lower recall
• Originally for pharmaceutical ELNs ~80%
• Pistachio coverage is ~71.5%
– >77% USPTO appl. text.
• Fast ~380 reactions per second per core
– A few hours to remap entire database
– Speed depends on backend
NIH Virtual Workshop on Reaction Informatics, May 2021NameRxn - Magic functional groups
NameRxn originally written as classification
tool, AAM is a by product
• For us no answer is better than a wrong answer
• Lowest number of wrong answers (Disagreement
with gold-standard)
• Yellow bar is so called “magic group additions”
where a product atom is unmapped:
• We didn’t know where a group came from
• Where there group came from was missing
• Stoichometry (multiple groups from one reactant)
• Aim to indicate this better in bulk data
• AMAP bench
Arkadii Lin et al. Atom-to-atom mapping: a benchmarking study of popular mapping algorithms and consensus strategies
https://chemrxiv.org/articles/preprint/Atom-to-Atom_Mapping_A_Benchmarking_Study_of_Popular_Mapping_Algorithms_and_Consensus_Strategies/13012679/1
NIH Virtual Workshop on Reaction Informatics, May 2021It’s a kind of magic…
Bromo Grignard + nitrile ketone synthesis (3.7.10)
EP0200736B1 [0072] Example 1, Step 1
RxnMapper/Indigo
RxnMapper
Water comes from the quenching
“The reaction mixture is slowly poured into ice cold 10% hydrochloric acid
“quenched slowly with 2N aq. HCl” (different paragraph)
NIH Virtual Workshop on Reaction Informatics, May 2021
:
”Symmetry/Stoichiometry
NameRxn - 8.2.2 Sulfanyl to sulfonyl
RxnMapper
US20010000511A1 [0357]
NIH Virtual Workshop on Reaction Informatics, May 2021Symmetry/Stoichiometry
Handle by reusing atom-maps in the reactant
US20010000511A1 [0357]
NIH Virtual Workshop on Reaction Informatics, May 2021Symmetry/Stoichiometry
Indigo
RxnMapper
US 03674855 A
NIH Virtual Workshop on Reaction Informatics, May 2021AMAP BENCH
Indigo
AMAP bench: Changed: 23, Broken: 13, C-C Broken: 7
RxnMapper
AMAP bench: Changed: 5, Broken: 3, C-C Broken: 0
Daniel Lowe, Roger Sayle. Evaluating the Quality and Performance of Automatic Atom Mapping
Algorithms. 244th ACS National Meeting & Exposition. Aug 2012
US20200071310A1 [0487] Example 34
NIH Virtual Workshop on Reaction Informatics, May 2021Ambiguous Names
8-(3,5-Bis-tri uoromethyl-benzoyl)-3-furan-2-yl-methyl-1-o-tolyl-1,3,8-triaza-spiro[4.5]decane-2,4-dione
Indigo/RxnMapper
AMAP bench: Changed: 4, Broken: 2, C-C Broken: 1
NIH Virtual Workshop on Reaction Informatics, May 2021
flAmbiguous Names
8-(3,5-Bis-tri uoromethyl-benzoyl)-3-furan-2-ylmethyl-1-o-tolyl-1,3,8-triaza-spiro[4.5]decane-2,4-dione
1.2.9 Alcohol + amine condensation
NameRxn/Indigo/RxnMapper
AMAP bench: Changed: 2, Broken: 1, C-C Broken: 0
NIH Virtual Workshop on Reaction Informatics, May 2021
flCase Study
Example 1 Example 21
Example 2 Example 22
Example 3 Example 23
Example 4 Example 24
Example 5 Example 25
Example 6 Example 26
Example 7 Example 27
Example 8 Example 28
Example 9 Example 29
Example 10 Example 30
Example 11 Example 31
Example 12 Example 32
Example 13 Example 33
Example 14
Example 15
Example 16 US 2020/0087299 A1
Example 17
Example 18
Example 19
Example 20
NIH Virtual Workshop on Reaction Informatics, May 2021Case Study
Example 1 Example 21
Example 2 Example 22
Example 3 Example 23
Example 4 Example 24
Example 5 Example 25
Example 6 Example 26
Example 7 Example 27
Example 8 Example 28
Example 9 Example 29
Example 10 Example 30
Example 11 Example 31
Example 12 Example 32
Example 13 Example 33
Example 14
Example 15
Example 16 US 2020/0087299 A1
Example 17
Example 18 NameRxn 127/154 82.4%
Example 19 Indigo 15/154 9.7%
Example 20
Reject 12/154 7.7%
NIH Virtual Workshop on Reaction Informatics, May 2021Case Study
Example 1 Example 21
Example 2 Example 22
Example 3 Example 23
Example 4 Example 24
Example 5 Example 25
Example 6 Example 26
Example 7 Example 27
Example 8 Example 28
Example 9 Example 29
Example 10 Example 30
Example 11 Example 31
Example 12 Example 32
Example 13 Example 33
Example 14
Example 15
Example 16 US 2020/0087299 A1
Example 17
Example 18 NameRxn 132/154 85.7%
Example 19 Indigo 10/154 6.4%
Example 20
Reject 12/154 7.7%
NIH Virtual Workshop on Reaction Informatics, May 2021Example 27
Typo: “tert-butyl”
Typo: “tert-butyl”
NIH Virtual Workshop on Reaction Informatics, May 2021Example 12
Step 1 Small Product (8 heavy atoms)
Typo: “methyl”
NIH Virtual Workshop on Reaction Informatics, May 2021Data STORAGE TIPS
Hierarchical Data
Hierarchical data is index in the WebApp: NameRxn Tags, Assignees, Diseases (MESH),
Targets (ChEMBL), IPC Codes
A simple way of store and searching for the data is using a nested identifier string, e.g.
LIKE ’11.%’ pulls back all AstraZeneca and related companies:
11 AstraZeneca
11.5 Imperial Chemical Industries
11.7 MedImmune
...
NameRxn is handled slightly different, we pack the three level number into an integer
(lvl1NameRxn concepts and rxno
1 Heteroatom alkylation and arylation
.7 O-substitution
.1 Chan-Lam ether coupling
.2 Diazomethane esterification
.3 Ethyl esterification
.4 Hydroxy to methoxy
.5 Hydroxy to triflyloxy
.6 Methyl esterification
.n
2 Acylation and related processes
.6 O-acylation to ester
.1 Ester Schotten-Baumann
.2 Esterification (generic)
.3 Fischer-Speier esterification
.4 Baeyer-Villiger oxidation
.5 Yamaguchi esterification
.6 Hydroxy to imidazolecarbonyloxy
.7 Imidazolecarbonyl to ester
.8 Hydroxy to acetoxy
.9 Steglich esterification
.n
CINF 13, ACS Fall 2017, Washington, D.C.NameRxn concepts and rxno
1 Heteroatom alkylation and arylation Esterification (7)
.7 O-substitution
.1 Chan-Lam ether coupling Chan-Lam coupling (3)
.2 Diazomethane esterification
.3 Ethyl esterification
.4 Hydroxy to methoxy
.5 Hydroxy to triflyloxy
.6 Methyl esterification
.n
2 Acylation and related processes
.6 O-acylation to ester
.1 Ester Schotten-Baumann Schotten-Baumann
.2 Esterification (generic) Reaction (9)
.3 Fischer-Speier esterification
.4 Baeyer-Villiger oxidation
.5 Yamaguchi esterification
.6 Hydroxy to imidazolecarbonyloxy
.7 Imidazolecarbonyl to ester
.8 Hydroxy to acetoxy
.9 Steglich esterification
.n
RXNO: http://github.com/rsc-ontologies/rxno
CINF 13, ACS Fall 2017, Washington, D.C.Summary
We always welcome feedback if you spot a mistake!
• It’s a long tail but many things are simple changes that are fixed when rerun
• Lot’s of people “cleaning” the data, We’d rather know what was wrong and can we
fix it
Plans
• Reaction sketch compound numbers
• Better quality indication
• Integrate RxnMapper, AMAP bench indicators, Boot-strapping
sequences
• Handled reactions from non-english patents
• General procedures/example references, currently only resolve
compounds
Acknowledgements
Daniel Lowe (MineSoft)
Richard Gowers (NextMove Software)
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