Presentation from the 10th International Conference on Chemical Structures / 10th German Conference on Chemoinformatics in Noordwijkerhout

1. Gregory Landrum
NIBR Informatics, Basel
Novartis Institutes for BioMedical Research
10th International Conference on Chemical Structures/
10th German Conference on Chemoinformatics
Large scale classification of chemical
reactions from patent data

2. Outline
2
§ Public data sources and reactions
§ Fingerprints for reactions
§ Validation:
• Machine learning
• Clustering
§ Application: models for predicting yield

3. Public data sources in cheminformatics
3
an aside at the beginning
§ Publicly available data sources for small molecules and
their biological activities/interactions:
• PDB, PubChem, ChEMBL, etc.
§ Publicly available data sources for the chemistry behind
how those molecules were actually made (i.e. reactions):
• pretty much nothing until recently
§ Plenty of data locked up in large commercial databases,
and pharmaceutical companies’ ELNs, very very little in
the open
The “public/open” point is important for
collaboration and reproducibility

4. A large, public source of chemical reactions
4
Not just what we made, but how we made it
§ Text-mining applied to open patent data to extract chemical reactions :
1.12 million reactions[1]
§ Reactions classified using namerxn, when possible, into 318 standard
types : >599000 classified reactions[2]
[1] Lowe DM: “Extraction of chemical structures and reactions from the literature.” PhD
thesis. University of Cambridge: Cambridge, UK; 2012.
[2] Reaction classification from Roger Sayle and Daniel Lowe (NextMove Software)
http://nextmovesoftware.com/blog/2014/02/27/unleashing-over-a-million-reactions-into-the-
wild/

5. More about the classes
5
Frequency of reaction classes:
44675 2.1.2 Carboxylic acid + amine reaction
39297 1.7.9 Williamson ether synthesis
28194 2.1.1 Amide Schotten-Baumann
26739 1.3.7 Chloro N-arylation
22400 1.6.2 Bromo N-alkylation
20465 7.1.1 Nitro to amino
20405 1.6.4 Chloro N-alkylation
17226 6.2.2 CO2H-Me deprotection
16602 6.1.1 N-Boc deprotection
16021 6.2.1 CO2H-Et deprotection
12952 1.2.1 Aldehyde reductive amination
12250 2.2.3 Sulfonamide Schotten-Baumann
10659 11.9 Separation
8538 3.1.5 Bromo Suzuki-type coupling
7261 1.7.7 Mitsunobu aryl ether synthesis
7102 6.3.7 Methoxy to hydroxy
7071 3.3.1 Sonogashira coupling
6472 3.1.1 Bromo Suzuki coupling
6383 1.8.5 Thioether synthesis
5791 9.1.6 Hydroxy to chloro
20 most common classes:

6. Got the reactions, what about reaction fingerprints?
6
Criteria for them to be useful
§ Question 1: do they contain bits that are helpful in
distinguishing reactions from another?
Test: can we use them with a machine-learning approach to build a
reaction classifier?
§ Question 2: are similar reactions similar with the
fingerprints
Test: do related reactions cluster together?

7. Our toolbox: the RDKit
§ Open-source C++ toolkit for cheminformatics
§ Wrappers for Python (2.x), Java, C#
§ Functionality:
• 2D and 3D molecular operations
• Descriptor generation for machine learning
• PostgreSQL database cartridge for substructure and similarity searching
• Knime nodes
• IPython integration
• Lucene integration (experimental)
• Supports Mac/Windows/Linux
§ Releases every 6 months
§ business-friendly BSD license
§ Code: https://github.com/rdkit
§ http://www.rdkit.org

8. Similarity and reactions
8
What are we talking about?
§ These two reactions are both type: “1.2.5 Ketone reductive amination”
It’s obvious that these are the same, right?

9. Similarity and reactions
9
What are we talking about?
§ These two reactions are both type: “1.2.5 Ketone reductive amination”
It’s obvious that these are the same, right?

10. Got the reactions, what about reaction fingerprints?
10
Start simple: use difference fingerprints:
Similar idea here:
1) Ridder, L. & Wagener, M. SyGMa: Combining Expert Knowledge and Empirical Scoring in the Prediction of
Metabolites. ChemMedChem 3, 821–832 (2008).
2) Patel, H., Bodkin, M. J., Chen, B. & Gillet, V. J. Knowledge-Based Approach to de NovoDesign Using Reaction
Vectors. J. Chem. Inf. Model. 49, 1163–1184 (2009).
FPReacts = FPi
i∈Reactants
∑
FPProducts = FPi
i∈Products
∑
FPRxn = FPProds − FPReacts

11. Refine the fingerprints a bit
11
Text-mined reactions often include catalysts,
reagents, or solvents in the reactants
Explore two options for handling this:
1. Decrease the weight of reactant molecules where too many
of the bits are not present in the product fingerprint
2. Decrease the weight of reactant molecules where too many
atoms are unmapped

12. Are the fingerprints useful?
12
§ Question 1: do they contain bits that are helpful in
distinguishing reactions from another?
Test: can we use them with a machine-learning approach to build a
reaction classifier?
§ Question 2: are similar reactions similar with the
fingerprints
Test: do related reactions cluster together?

13. Machine learning and chemical reactions
13
§ Validation set:
• The 68 reaction types with at least 2000 instances from the patent
data set
- “Resolution” reaction types removed (e.g. 11.9 Separation and 11.1 Chiral
separation)
- Final: 66 reaction types
§ Process:
• Training set is 200 random instances of each reaction type
• Test set is 800 random instances of each reaction type
• Learning: random forest (scikit-learn)

14. Learning reaction classes
14
Results for test data
Overall:
• Recall: 0.94
• Precision: 0.94
• Accuracy: 0.94
For a 66-class classifier, this looks pretty good!

15. Learning reaction classes
15
~94% accuracy
much of the
confusion is
between related
types
Confusion matrix for test data
Bromo Suzuki coupling
Bromo Suzuki-type coupling
Bromo N-arylation

16. Are the fingerprints useful?
16
§ Question 1: do they contain bits that are helpful in
distinguishing reactions from another?
Test: can we use them with a machine-learning approach to build a
reaction classifier?
§ Question 2: are similar reactions similar with the
fingerprints
Test: do related reactions cluster together?

17. Clustering reactions
17
§ Reaction similarity validation set:
• The 66 most common reaction types from the patent data set
• Look at the homogeneity of clusters with at least 10 members
1.2.5 Ketone reductive
amination
1.2.5 Ketone reductive
amination
1.2.5 Ketone reductive
amination
Integration
Interpretation: <30% of clusters are <90% homogeneous
Interpretation: <40% of clusters are <80% homogeneous

18. Using the fingerprints
18
Can we help classify the remaining 600K reactions?
§ Apply the 66 class random forest to generate class predictions for the
unclassified compounds in order to find reactions we missed
§ Cluster the unclassified molecules, look for big clusters of unclassified
molecules, and (manually) assign classes to them.
§ Both of these approaches have been successful

19. Predicting yields
19
§ The data set includes text-mined yield information as well as
calculated yields.
§ For modeling: prefer the text-mined value, but take the calculated one
if that’s the only thing available
§ Look at stats for the 93 reaction classes that have at least 500
members with yields, a min yield > 0 and a max yield < 110 %:

20. Predicting yields
20
§ Look at the most populated classes:

21. Try building models for yield
21
§ Start with class 7.1.1 “nitro to amino”
§ Break into low-yield (<50%) and high-yield (>70%)
classes.
14% are low-yield

22. § Try building a random forest using the atom-pair based
reaction fingerprints
Try building models for yield
22
things that don’t work
That’s performance on the training set

23. § Try building a random forest using the atom-pair based
reactant fingerprints
Try building models for yield
23
things that don’t work
That’s performance on the training set

24. § Look at the ROC curve for the training-set data
Try building models for yield
24
things that don’t work?
first wrong “low-yield” prediction
nine wrong “low-yield” predictions
The model is doing a great job
of ordering compounds, but a
bad job of classifying
compounds

25. Unbalanced data and ensemble classifiers
25
an aside
§ Usual decision rule for a two-class ensemble classifier:
take the result that the the majority of the models (decision
trees for random forests) vote for.
§ That’s a decision boundary = 0.5
§ If the dataset is unbalanced, why should we expect
balanced behavior from the classifier?
§ Idea: use the composition of the training set to decide
what the decision boundary should be.
For example: if the data set is ~20% “low yield”, then assign “low
yield” to any example where at least 20% of the trees say “low yield”

26. § Try building a random forest using the atom-pair based
reactant fingerprints
§ What about moving the decision boundary to 0.2 to reflect
the unbalanced data set ?
Try building models for yield
26
Getting close to working
That’s performance on the training set
Starting to look ok. What about the test set?

27. § Results from a random forest using the atom-pair based
reactant fingerprints with the shifted decision boundary
Try building models for yield
27
Getting close to working
Not too terrible.
test set

28. § Aldehyde reductive amination (no shift):
§ Williamson ether synthesis (boundary 0.3)
Try building models for yield
28
Some more models
test set
test set

29. § Chloro N-Alkylation (no shift):
§ Chloro N-Alkylation (0.4 shift)
Try building models for yield
29
Some more models
test set
test set

30. Wrapping up
30
§ Dataset: 1+ million reactions text mined from patents
(publically available) with reaction classes assigned
§ Fingerprints: weighted atom-pair delta and functional-
group delta fingerprints implemented using the RDKit
§ Fingerprint Validation:
• Multiclass random-forest classifier ~94% accurate
• Similarity measure works: similar reactions cluster together
§ Combination of clustering + functional group analysis
allows identification of new reaction classes
§ We’re also able to use the fingerprints to build reasonable
models for yield

31. § NextMove Software:
• Roger Sayle
• Daniel Lowe
§ NIBR:
• Anna Pelliccioli
• Sereina Riniker
• Mike Tarselli
31
Acknowledgements

32. Advertising
32
3rd RDKit User Group Meeting
22-24 October 2014
Merck KGaA, Darmstadt, Germany
Talks, “talktorials”, lightning talks, social activities, and a hackathon on
the 24th.
Registration: http://goo.gl/z6QzwD
Full announcement: http://goo.gl/ZUm2wm
We’re looking for speakers. Please contact greg.landrum@gmail.com