CINF 170: Regioselectivity: An application of expert systems and ontologies to chemical (named) reaction analysis

Regioselectivity: An Application of Expert
Systems and Ontologies to Chemical
(Named) Reaction Analysis
CINF Reaction Analytics. 256th ACS National Meeting, Boston, MA. Thursday 23rd August 2018
Roger Sayle, John Mayfield and Noel O’Boyle
NextMove Software, Cambridge, UK

analysis vs. prediction
• In “Applied Chemoinformatics” (2018), J. Goodman
defines three main problems.
– Reaction Planning: R → ? → P [Database]
– Reaction Prediction: R1 + R2 → ? [Simulation]
– Synthesis Planning: R? + R? + R? → P [Design]
• A corollary is that there’s a distinction between
reactions that have already been observed and
those experiments yet to be performed.

analysis vs. prediction

intuition and counter-intuition
With apologies to Mark Twain:
“Never let the facts get in the
way of a good synthesis plan”.
There are reactions that chemists
expect will happen but don’t &
those they don’t expect but do.
But cheminformaticians are
alchemists that can turn lead into
gold, as easily as “[Pb]>>[Au]”.

Experimental validation
• Synthesis of a novel aromatic
heterocycle previously unreported in
the literature.
• William Pitt et al., “Heteroaromatic
Rings of the Future”, Journal of
Medicinal Chemistry, 52(9):2952-
2963, 2009.

expectations: setting the scope
Goodman Challenges Carey et al. Challenges
Maitotoxin
Difficult to access substituted
aromatic starting materials.
Eribulin Org. Biomol. Chem. 2005, 4,2337-2347.

Which reactions are important?
• NCI/LHASA SAVI (14+6 reactions)
– Suzuki Coupling 36262 out of 1.2M USPTO examples
– Sulfonamide Schotten-Baumann 14348 out of 1.2M USPTO examples
– Buchwald-Harwig 6040 out of 1.2M USPTO examples
– Hiyama coupling 458 out of 1.2M USPTO examples
– Fukuyama coupling 2 out of 1.2M USPTO examples
– Liebeskind-Srogl coupling 0 out of 1.2M USPTO examples
• Hartenfeller/Schneider (58 reactions)
– #1 Pictet-Spengler reaction 7 out of 1.2M USPTO examples
– #10+ #11 Azide-nitrile Huisgen-cycloaddition 5 out of 1.2M USPTO examples
– #17 Pyridone synthesis 2 out of 1.2M USPTO examples
– #20 Phthalazinone synthesis 16 out of 1.2M USPTO examples
– #24 Friedlander quinoline synthesis 30 out of 1.2M USPTO examples
• Enamine REAL 2016 (43 reactions)
– Thiourea to guanidine (14 out of 160M examples).

myth #1: heterocycle formation
• Because almost all drug-like molecules contain a
heterocycle, there’s a belief that heterocycle
formation is important.
• Analysis of both patent data and pharmaceutical
ELNs reveals that heterocycle forming reactions,
even named heterocycle forming reactions, are
relatively rare, with ring systems often being
purchased as building blocks*.
* This analysis might not apply to process development and manufacturing.
https://nextmovesoftware.com/blog/2016/10/24/buying-a-ring-or-making-one-yourself/

Reactions that don’t happen!
• Paal-Knorr Pyrrole Synthesis vs. Aldehydes/Ketones
Of the 430 examples of
Paal-Knorr pyrrole
synthesis reported in
US patent applications
2001-2012, exactly
zero have more than
the two reacting
ketones/aldehydes.

Who let the dogs out?
• Big Data can determine the utility and scope of reactions.

Reactions that do happen!
• Chloro Sonogashira Couplings (@ NCI)
– The LHASA ‘CHMTRN’ rules for Transform 2267, Sonogashira couplings,
(presented by Marc Nicklaus at Sheffield) states “Iodides are usually
more reactive than bromide. Chlorides do not react”.
Code Name Count Yield
3.3.2 Bromo Sonogashira coupling 3717 49.3%
3.3.3 Chloro Sonogashira coupling 429 44.2%
3.3.4 Iodo Sonogashira coupling 2721 64.9%
• Isotopically-labelled Compounds (@Eli Lilly)
– The LAAR reactions used to construct Lilly’s PLC (Nicolaou et al. 2016)
forbid the presence of isotopic labels in reactants.

improving upon synthia™
• As presented earlier, Chematica/Synthia maintains a
manually curated “black list” of interfering functional
groups.
• A more labor-efficient data-driven strategy is to
automatically maintain a “white list” of tolerated
functional groups that can be derived from observed
experiments.
• It’s much easier to track the things you know about
and can see, than the things you can’t see and/or
don’t know about.

handling noisy data (n>1 statistics)
• Kumada couplings incompatible with aldehydes...
• US20050107257A1 [p0196] Syngenta
A solution of 34.3 ml of ethylmagnesium chloride in 50 ml of tetrahydrofuran is added dropwise at −70° C. to
7.3 g of indium trichloride in 200 ml of tetrahydrofuran and, after stirring for 30 minutes, the reaction
temperature is allowed to rise slowly to room temperature. That solution is added to a solution of 14.9 g of 3-
bromo-4-pyridine carbaldehyde and 2.8 g of PdCl2 (PPh3)2 in 240 ml of tetrahydrofuran and the reaction
mixture is heated under reflux for 20 hours. 5 ml of methanol are then added and the mixture is concentrated
in vacuo, stirred thoroughly with diethyl ether, filtered off and concentrated in vacuo once more. The residue is
chromatographed on silica gel using ethyl acetate/hexane (1:1), yielding 3-ethyl-4-pyridine carbaldehyde (I) in
the form of a yellow oil.

c.f. US20140296184A1 [C00008]
M. Segler, M. Preuss and M. Waller, “Planning Chemical Syntheses with Deep Neural
Networks and Symbolic AI”, Nature, 555:604-610, 2018.
A.Gini, M. Segler et al, “Dehydrogenative TEMPO-mediated formation of Unstable
Nitrones: Easy Access to N-Carbamoyl Isoxazolines”, Chem. Eur. J. 21:12053-12060,
2015.
prediction? about the future?

search: monte carlo v. proof num
• Appropriate choice of AI search technique.
Monte Carlo search is inappropriate
for search problems such as mazes.
White to win in 173 ply
Kf1-f2!
OR
AND
0 1 0 0 1 1 1 0
0
1
0 1 0

categorization of reactions
1. J. Carey, D. Laffan, C. Thomson, M. Williams, Org. Biomol. Chem. 2337, 2006.
2. S. Roughley and A. Jordan, J. Med. Chem. 54:3451-3479, 2011.
34%
17%
5%
2%
3%
6%
10%
1%
15%
2%
5% Heteroatom alkylation and arylation
Acylation and related processes
C-C bond formations
Heterocycle formation
Protections
Deprotections
Reductions
Oxidations
Functional group conversion
Functional group addition
Resolution

reaction ontology
• Reactions are classified into a common subset of the
Carey et al. classes and the RSC’s RXNO ontology.
• There are 12 super-classes
– e.g. 3 C-C bond formation (RXNO:0000002).
• These contain 84 class/categories.
– e.g. 3.5 Pd-catalyzed C-C bond formation (RXNO:0000316)
• These contain ~1050 named reactions/types.
– e.g. 3.5.3 Negishi coupling (RXNO:0000088)
• These require ~2200 SMIRKS-like transformations.

Complication: agents matter
From http://en.wikipedia.org/wiki/Diazonium_compound
Sandmeyer reaction
Benzenediazonium chloride heated with cuprous chloride
disolved in HCl to yield chlorobenzene.
C6H5N2
+ + CuCl → C6H5Cl + N2 + Cu+
Gatterman reaction
Benzenediazonium chloride is warmed with copper powder and
HCl to yield chlorobenzene.
C6H5N2
+ + CuCl → C6H5Cl + N2 + Cu+

10 most popular reactions
ID Name Count
2.1.2 Carboxylic acid + amine 26,040
1.3.1 Buchwald-Hartwig amination 22,048
3.1 Suzuki coupling 16,508
1.7.6 Williamson ether synthesis 15,665
2.1.1 Amide Schotten-Baumann 11,016
7.1 Nitro to amino 10,234
6.1.1 N-Boc deprotection 9,821
6.2.2 CO2H-Me deprotection 9,487
6.2.1 CO2H-Et deprotection 6,749
2.2.3 Sulfonamide Schotten-Baumann 6,223

Most/least successful reactions
ID Name Mean Yield Count
1.7.2 Diazomethane esterification 91% 41
9.3.1 Carboxylic acid to acid chloride 88% 704
9.7.14 Bromo to azido 85% 235
1.7.5 Methyl esterification 84% 2918
9.7.19 Bromo to iodo Finkelstein reaction 82% 116
6.1.3 N-Cbz deprotection 81% 1359
…
4.1.11 Larock indole synthesis 47% 55
3.11.3 Ullmann-type biaryl coupling 44% 407
1.7.1 Chan-Lam ether coupling 44% 154
4.1.4 Pinner pyrimidine synthesis 39% 47

rArE named reactions
• Adams decarboxylation
• Angeli-Rimini reaction
• Aza-Baylis-Hillman reaction
• Boyer reaction
• Buchwald-Fischer indole synthesis
• Castro-Stephens coupling
• Chugaev elimination
• Cook-Heilbron thiazole synthesis
• Fischer-Hepp rearrangement
• Fukuyama indole synthesis
• Gasman indole synthesis
• Imine Hosomi-Sakurai reaction
• Koch reaction
• Leuckart reaction
• Liebeskind-Srogl coupling
• Lossen rearrangement
• Ponzio reaction
• Prins reaction
• Reimer-Tiemann carboxylation

Trends in Reaction Types
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Suzukicouplingsasapercentageofreactionsinayear
Leaving Group Mean Yield N Observations
Bromo 58.80% 10817
Chloro 57.96% 2752
Iodo 57.21% 2049
Triflyloxy 65.48% 717

Namerxn reaction naming
• Many reaction classification algorithms are
dependent upon atom-atom mapping assignments.
• Alas MCS-based atom mapping algorithms are often
slow and/or inaccurate [Lowe & Sayle 2012 & 2013].
• NameRXN is a mechanism-based atom-mapper.
• All reactants and reagents are placed in a single pot
(molecule) and sets of SMIRKS applied in turn.
• If the desired product is generated, the reaction (its
mechanism and mapping) is identified.
– Rationalization is easier than prediction!

Example smarts/smirks
# NOZAKI_HIYAMA_KISHI_REACTION
[#6v4+0;X4,X3:1][BrD1h0+0:2].[Ni].[Cr].[OD1h
0+0:3]=[CD2h1v4+0:4]>>[#6:1][C:4]-[Oh1:3]
# PAAL_KNORR_THIOPHENE_SYNTHESIS
[OD1h0+0:1]=[CX3v4+0:2][CX4v4+0:3]([H])[CX4v
4+0:4]([H])[CX3v4+0:5]=[OD1h0+0:6]>>[S:1]1[C
:2]=[C:3][C:4]=[C:5]1
• Writing SMIRKS is both an art and a science.

Smarts pattern compilation
bool atom_1(const RDKit::Atom *aptr) {
return aptr->getAtomicNum() == 6 &&
ringinfo->numAtomRings(aptr->getIdx()) != 0 &&
aptr->getDegree() == 3 &&
aptr->getTotalNumHs(false) == 0 &&
aptr->getExplicitValence()+aptr->getImplicitValence() == 4 &&
aptr->getFormalCharge() == 0;
}
bool atom_28(const RDKit::Atom *aptr) {
if (aptr->getAtomicNum() != 6 ||
aptr->getExplicitValence()+aptr->getImplicitValence() != 4 ||
aptr->getFormalCharge() != 0)
return false;
return (aptr->getDegree() == 2 && aptr->getTotalNumHs(false) == 1) ||
(aptr->getDegree() == 2 && aptr->getTotalNumHs(false) == 2) ||
aptr->getDegree() == 3;
}

Smarts pattern compilation
RDKit::ROMol::OBOND_ITER_PAIR biter[25];
…
case_7:
biter[0] = mol->getAtomBonds(atom[1]);
goto case_9;
case_8:
avisit[atom[0]->getIdx()] = 0;
++biter[0].first;
case_9:
if (biter[0].first != biter[0].second) {
bptr = (*mol)[*biter[0].first].get();
if (bond_1(bptr)) {
aptr = bptr->getOtherAtom(atom[1]);
aidx = aptr->getIdx();
if (avisit[aidx] == 0 && atom_1(aptr)) {
avisit[aidx] = 1;
atom[0] = aptr;
goto case_10;
} else ++biter[0].first;
} else ++biter[0].first;
} else goto case_5;
goto case_9;

Recent improvements insights
• Profiling NameRxn in 2014 to diagnose performance
problems with RDKit revealed that pattern matching
and transformation accounted for <1% of runtime.
• The bottleneck is actually in canonicalization.
• The Ah-ha experience was to use hash filtering.
• Check molecular formula: CiHjBrkCllFmInNoOpPqSr
• Additional cleverness allows pre-sanitization hashing.
– Triple bond count, but not single or double bond count.
– Perhaps there’s something in InChI-style hashing after all.

Pistachio: Siri for chemists

Multi-step Synthetic Routes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Intermediates 197702103114 56611 31403 17268 9230 5057 2701 1256 639 301 136 58 15 5 2
Terminal Products 385149149445 81837 47579 27670 16619 9320 5263 2511 1330 678 373 111 63 8 6 5
0
100000
200000
300000
400000
500000
600000
700000
Occurrences
Number of steps
Intermediates
Terminal Products

Application to planning 1
• Cinnamic Acid (PhCHCHCO2)
1. Bromo Heck reaction (272)
2. Horner-Wadsworth-Emmons reaction (268)
3. Wittig olefination (129)
4. Bromo Heck-type reaction (62)
5. Iodo Heck reaction (49)
6. Triflyloxy Heck[-type] reaction (43)
7. Ester Schotten-Baumann (10)
8. Bromo Suzuki coupling (5)
9. Stille reaction (2)
10. Olefin metathesis (1)

Application to planning 2
• p-Nitrobenzoic acid
1. Nitrile to carboxy (12)
2. CO2H-Me deprot (8)
3. CO2H-Et deprot (5)
4. Ester hydrolysis (1)
5. Nitration (1)
• p-Nitrotoluene
1. Nitration (96)
2. Bromo Suzuki-type (1)
3. Chloro Suzuki (1)

tactical vs. strategic reactions
• Traditional Synthesis
Planning has concentrated
on the Strategic Application
of Named Reactions.
• However, there’s much to
be gained for the Tactical
Application of Unnamed
Reactions.
• Nitro reduction is the 6th
most frequent reaction.

functional group interconversion
• Relative frequency of simple group conversion
From a total of 7,252,419 reactions from USPTO & EPO patents.
Amino Bromo Chloro Fluoro Hydroxy Iodo Sulfanyl Thioxo
Amino 3951 2949 990 3976 3657 143
Bromo 3121 589 637 2390 738 435
Chloro 9424 717 1606 2156 744 798
Fluoro 1549 180 484 826 28 103
Hydroxy 2572 11441 31593 7641 3004 348
Iodo 155 445 47
Nitro 126606 138
Oxo 8419

nextmove’s strategy
• Why expose reaction planning to the end-user at all?
• During our collaboration with ChemSpace, it has become clear
that what was required was not similarity nor superstructure
search by a form of synthesis-aware search.
• This is similar to the challenge faced by traditional
restrosynthesis tools in identifying a leaf/goal state.
• 937M purchasable compounds makes this is non-trivial.
• The usual challenges of functional group, tautomer and
protonation state lookups, now also protecting groups.
• But why not return the carboxylic acid when searching for the
acid chloride, or alcohol when searching for bromo derivative.

building block search
• Query:
Results:

the challenge of regioselectivity
• A tricky benchmark is reactions of 2,4,5-trichloropyrimidine
• The nature of pyrimidine makes the chloro at the 4-position more reactive
than the 2 position which is more reactive than the 5 position.
• Simple quantum mechanical have difficulty discerning this order.

handy’s rule
• Scott Handy and Yanan Zhang, “A Simple Guide for Predicting
Regioselectivity in the Coupling of Polyhaloheteroaromatics”,
Chemical Communications, 3:299-301, Nov 2005.
Abstract
A simple guide for predicting the order and site of coupling (Suzuki, Stille, Negishi,
Sonogashira, etc.) in polyhaloheteroaromatics based upon the 1H NMR chemical shift
values of the parent non-halogenated heteroaromatics has been developed.

electrophilic substitution of
heterocycles with qm methods
• J.C. Kromann et al., “Fast and Accurate Prediction of the
Regioselectivity of Electrophilic Aromatic Substitution
Reactions”, Chem. Sci., 9(3):660-665, Nov 2017.
• M. Kruszyk et al., “Computational Methods to Predict the
Regioselectivity of Electrophilic Aromatic Substitution
Reactions of Heteroaromatic Systems”, J. Org. Chem.
81(12):5128-5134, Jun 2016.

a data-driven strategy
• One possible approach to the challenge of regioselectivity is
to derive preferences by large-scale (statistical) analysis of
reaction data sets.
• Reaction classification can identify the subset of relevant
examples, which can then be used to produce tables of
heterocycles position preferences.
• Directing groups and their influence can also be identified and
tabulated.
• See https://www.scripps.edu/baran/images/grpmtgpdf/Gutekunst_Apr_10.pdf

the semantics of atom mapping
Prof. Goodman poses an interesting question of atom mapping.
4.1.6 Cyclic Beckman Rearrangement
1.2.9 Alcohol + Amine Condensation or 1.1.3 Iodo N-methylation

Acknowledgements
• NextMove Software
– Noel O’Boyle
– John Mayfield
• NextMove Alumni
– Daniel Lowe
• Thank you for you time.
• Questions?
• Thoughts?
• AbbVie
• AstraZeneca
• Bristol-Myers Squibb
• ChemSpace
• Eli Lilly
• GlaxoSmithKline
• Hoffmann-La Roche
• IBM Research Zurich
• Merck
• Novartis
• Royal Society of Chemistry
• Vernalis
• Vertex Pharmaceuticals

Transforms vs. reactions
Importance of Reaction Mechanism
Example: Ullman-type Coupling Reactions
SMIRKS: [H][N:1].[Cl][c:2]>>[N:1][c:2]
The SMIRKS transform alone is insufficient
to predict the products and by-products in
this example. A measure of nucleophility
is desirable for each atom in a molecule.
Without this software may be misled into
believing that protecting groups are required.

Analysis of pharmaceutical elns
• NextMove Software’s HazELNut software is used to
export and analyze ELN content at 6 of the top 10
large pharmaceutical companies.
• In-house analysis of this data, across the industry,
reveals a surprisingly high rate of synthesis failure,
not indicated in the published literature (journal
articles, patent applications or reaction databases).
• Understanding the causes of these failures is perhaps
more significant than attempting to access new
chemistries.

Extracting mps and reactions

Example reaction mining INPUT
Methyl 4-[(pentafluorophenoxy)sulfonyl]benzoate
To a solution of methyl 4-(chlorosulfonyl)benzoate (606 mg,
2.1 mmol, 1 eq) in DCM (35 ml) was added
pentafluorophenol (412 mg, 2.2 mmol, 1.1 eq) and Et3N
(540 mg, 5.4 mmol, 2.5 eq) and the reaction mixture stirred
at room temperature until all of the starting material was
consumed. The solvent was evaporated in vacuo and the
residue redissolved in ethyl acetate (10 ml), washed with
water (10 ml), saturated sodium hydrogen carbonate (10
ml), dried over sodium sulphate, filtered and evaporated to
yield the title compound as a white solid (690 mg, 1.8
mmol, 85%).

Example reaction mining Output

CHEMICAL reactions for free

bond changes in indole synthesis
Synthesis A B C D
Baeyer-Emmerling M
Bartoli M M
Bischler-Möhlau M C M
Fischer M C M
Fukuyama M
Hemetsberger M
Larock M C M
Mandelung M
Nenitzescu M M
Reissert M M

Suzuki coupling leaving groups
Leaving Group Mean Yield N Observations
Bromo 58.80% 10817
Chloro 57.96% 2752
Iodo 57.21% 2049
Triflyloxy 65.48% 717

Beyond drug guru
sildenafil (viagra) vardenafil (levitra)

Eli lilly’s automated synthesis lab
Alexander G. Godfrey, Thierry Masquelin and Horst Hemmerle, “A Remote-Control Adaptive MedChem Lab: An Innovative
Approach to Enable Drug Discovery in the 21st Century”, Drug Discovery Today, Vol. 18, Nos. 17-18, September 2013.

Synthesis failures at lilly
• At the 2013 Sheffield Cheminformatics conference,
Christos Nicolaou highlighted the technical challenge
with predicting compounds potentially accessible by
the Lilly’s Advanced Synthesis Lab (ASL).
• In a proof-of-concept pilot project, only 25 of 90
compounds suggested by Lilly’s Annotated Reaction
Repository (LARR) rule-set could be successfully
synthesized in practice.
• http://cisrg.shef.ac.uk/shef2013/talks/14.pdf

Synthesis failures at gsk
• Pickett et al. 2011 describe the parallel synthesis of a
50x50 library of MMP-12 inhibitors by an iodo-Suzuki
coupling reaction.
• Only 1704 of 2500 could be assayed [566 not made]
Pickett et al., ACS Med. Chem. Lett. 2(1):28, 2011

Learning from failure (logp shift)
• Nadin et al. 2012 [1] hypothesize that low LogP is a
major cause of synthesis failure in parallel synthesis
of combinatorial libraries.
• Analysis confirms that this is indeed a significant
factor for the GSK MMP-12 library.
– 1704 compounds measured, mean logP = 3.56 (1.44)
– 566 compounds not made, mean logP = 2.83 (1.52)
– Student’s t-test for different distributions, p<2x10-22.
1. Nadin, Hattotuwagama and Churcher ,“Lead-Oriented Synthesis: A New Opportunity for
Synthetic Chemistry”, Angew. Chem. Int. Ed, 51:1114 2012.

Example (actionable) insight
The clear trend between Suzuki coupling success rate and
predicted octanol-water partition co-efficient.
Data: Pickett et al., ACS Med. Chem. Lett. 2(1):28, 2011
97 232
340
525
474 202
63
55 119 127 141 80 36 8
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
<1.0 1.0-2.0 2.0-3.0 3.0-4.0 4.0-5.0 5.0-6.0 >6.0
Reaction Product Predicted cLogP
Sucessful Reactions Failed Reactions

big data mining confirmation of
Nadin-churcher hypothesis
On 16,335 Suzuki coupling reactions extracted from US
patent applications between 2001 and 2012.
Nadin, Hattotuwagama and Churcher ,“Lead-Oriented Synthesis: A New Opportunity for Synthetic
Chemistry”, Angew. Chem. Int. Ed, 51:1114 2012.
LogP Mean Yield N Obs
< 1.0 52.89% 196
1.0 – 2.0 56.02% 1155
2.0 – 3.0 56.72% 2881
3.0 – 4.0 58.14% 4071
4.0 – 5.0 57.26% 3186
5.0 – 6.0 59.25% 2126
> 6.0 63.83% 2720

“big data” reaction yield analysis
AZ Data courtesy of Nick Tomkinson, AstraZeneca RDI, Alderley Park, UK.

Functional group changes analyzed
Do the results make sense at all?
Functional Group
Avg in
Reaction
Overall
Average
halogen -0.98 -0.3
alcohol -0.95 -0.12
halogen_notfluorine -0.89 -0.27
alcohol_aromatic -0.67 -0.04
halogen_aliphatic -0.62 -0.15
halogen_notfluorine_aliphatic -0.62 -0.14
carboxylicacid -0.5 -0.23
halogen_bromine -0.42 -0.11
halogen_bromine_aliphatic -0.39 -0.06
halogen_aromatic -0.36 -0.16
alcohol_aliphatic -0.28 -0.08
halogen_notfluorine_aromatic -0.27 -0.13
amine -0.04 -0.3
amine_aliphatic -0.04 -0.27
carboxylicacid_aliphatic -0.04 -0.08
halogen_bromine_aromatic -0.03 -0.05
amine_tertiary -0.02 -0.06
amine_tertiary_aliphatic -0.02 -0.08
carboxylicacid_aromatic -0.02 -0.03
amine_cyclic -0.01 -0.02
halogen_bromine_bromoketone -0.01 0
Functional Group
Avg in
Reaction
Overall
Average
acidchloride 0 -0.07
acidchloride_aliphatic 0 -0.05
acidchloride_aromatic 0 -0.02
aldehyde 0 -0.04
aldehyde_aliphatic 0 -0.01
aldehyde_aromatic 0 -0.03
amine_aromatic 0 -0.03
amine_primary 0 -0.15
amine_primary_aliphatic 0 -0.07
amine_primary_aromatic 0 -0.07
amine_secondary 0 -0.04
amine_secondary_aliphatic 0 -0.07
amine_secondary_aromatic 0 0.03
amine_tertiary_aromatic 0 0
azide 0 0
azide_aliphatic 0 0
azide_aromatic 0 0
boronicacid 0 -0.03
boronicacid_aliphatic 0 0
boronicacid_aromatic 0 -0.03
carboxylicacid_alphaamino 0 0
isocyanate 0 -0.01
isocyanate_aliphatic 0 0
isocyanate_aromatic 0 0
nitro 0 -0.03
nitro_aliphatic 0 0
nitro_aromatic 0 -0.03
sulfonylchloride 0 -0.02
sulfonylchloride_aliphatic 0 -0.01
Compare the average deltas for the >39K instances of
Williamson ether synthesis
These look sensible

CINF 170: Regioselectivity: An application of expert systems and ontologies to chemical (named) reaction analysis

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CINF 170: Regioselectivity: An application of expert systems and ontologies to chemical (named) reaction analysis