The document discusses using semiempirical methods for fast and automated predictions of regioselectivity in electrophilic aromatic substitution reactions of heteroaromatic systems. It describes a workflow that uses RDKit and RegioSQM to predict the site of protonation and substitution in 6 steps: (1) generating protonated isomers, (2) conformational search, (3) finding lowest energy isomer, (4) checking for proton transfer, (5) running calculations, and (6) displaying results. This approach achieved a 90% success rate on a test set of 520 compounds. Lessons learned include asking the "right" questions to improve accuracy, the importance of cheminformatics tools for automation, and ensuring

1. Using semiempirical methods for fast and automated predictions
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@janhjensen
Jan H. Jensen, University of Copenhagen

2. Fast and Accurate Prediction of the Regioselectivity
of Electrophilic Aromatic Substitution Reactions of
Heteroaromatic Systems
Jimmy C. Kromann and Jan H. Jensen
University of Copenhagen
Morten Jørgensen, Monika Kruszyk, Mikkel Jessing
Lundbeck A/S
Contact me for a preprint
N
H
N + Br+
-H+
N
H
N N
H
N N
H
N
Br
Br
Br
or ?

3. Streitwieser: Proton affinity correlated with reaction rate
i.e. reaction occurs at site with highest PA
N
H
N
N
H
N H
H
H
N
H
N
H
N
H
N
169.4182.2 181.1
H HH
H
PM3/COSMOheat of formation
90% success rate for 520 compounds
Kcal/mol

4. Workflow / Automation
Molecule Protonated
isomers
Conformational
search
Find lowest
energy isomer
Display
result
Check for
proton transfer
ChemDraw RDKit
SMILES
c1cnc(cc1c1n(c(c(n1)c1ccc(cc1Cl)Cl)C(=O)OC)COCC[Si](C)(C)C)NC(=O)C
RDKitRDKit
N
N
N
Cl
Cl
O
O
O
Si
N
H
O
1
RegioSQM
23
4
5
6
6 isomers x 20 confs
SMILES

5. Web server
regiosqm.org

6. Lessons learned – Asking the right questions
N
H
N
N
H
N H
H
H
N
H
N
H
N
H
N
169.4182.2 181.1
H HH
H
“Which atom has the highest PA?” instead of “What is the PA?”
“What is the relative pKa?” instead of “What is the pKa?”
Jensen, Swain, Olsen JPCA 2017 (10.1021/acs.jpca.6b10990 )

7. Lessons learned – Newer SQM methods not more accurate
pKa prediction
PM3 AM1PM6 PM7PM6-DH+
Accuracy of solvation energy may play a role
COSMO Errors for MNSOL database
Kcal/mol AM1 PM3 PM6
MAE 3.6 3.4 3.9
ME 0.0 0.5 1.8

8. Lessons learned – Learn cheminformatics
All aspects of calculation must be automated – including “chemical transformation” and analysis
Cheminformatics toolkits, e.g. RDKit, are incredibly useful for “chemical transformation” problems
Datasets must include SMILES. XYZ -> SMILES unsolved problem.
Protonate
here
But not
here

9. Lessons learned - Cheminformatics
Creating intermediate/products as easy as creating protonation states
Setup for TS search?
Must solve atom mapping problem

10. Lessons learned – Use by non-experts
Automation ✔
Interface ✔
(SMILES input/HTML)
Installation ?
(MOPAC, Python, RDKit, oBabel)
Parallel computing/queueing ?
N
N
N
Cl
Cl
O
O
O
Si
N
H
O
~100 geometry optimizations -> 100-200 minutes on 1 core
-> 4-8 minutes on 24 cores
Options
Webserver/dedicated node
Pay-per-use cloud computing

11. Thank you