Prediction of Reaction Conditions

for Michael Additions

G. Marcou, J. Aires de Sousa, A. de Luca, D. Latino, V. Rietsch,

D. Horvath, A. Varnek

ChemAxon UGM, Budapest 19-20 May 2015

• Different scenarios of structure-reactivity modeling

• Descriptors for reactions

- Condensed Graph of Reaction / ISIDA descriptors

- Electron Effects Descriptors

• Michael reaction case

OUTLINE

Chemical reactions are difficult objects

+ +

- many species;

- two types of species: reactants and products;

- multi-step reactions,

- dependent on experimental conditions

• How can I synthesize this structure?

• How can I estimate a yield of a given reaction, its

kinetic and thermodynamic parameters ?

• Which reaction conditions I should choose in order

to obtain desirable product selectively ?

Chemical reactions in Chemoinformatics

𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦 = 𝐟(𝑫𝒆𝒔𝒄𝒓𝒊𝒑𝒕𝒐𝒓𝒔)

• substructural fragments

• topological indices,

• physico-chem. parameters

• etc…

Parameters directly derived from molecular structure

• Support Vector Machine (SVM)

• Multi-Linear Regression (MLR)

• Artificial Neural Networks

• etc…

Mathematical relationship established with machine learning methods

Quantitative Structure-Activity/Property Relationship (QSAR/QSPR)

𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦 = 𝐟(𝒔𝒕𝒓𝒖𝒄𝒕𝒖𝒓𝒆)

• 𝑅𝑒𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 = 𝐟 𝑹𝒆𝒂𝒄𝒕𝒂𝒏𝒕𝒔 𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒

• 𝑅𝑒𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 = 𝐟(𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒔 𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒)

• 𝑅𝑒𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 = 𝐟(𝑹𝒆𝒂𝒄𝒕𝒊𝒐𝒏 𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒)

Quantitative Structure-Reactivity Relationship (QSRR)

• Different scenarios of structure-reactivity modeling

• Descriptors for reactions

- Condensed Graph of Reaction / ISIDA descriptors

- Electron Effect Descriptors

• Michael reaction case

OUTLINE

Conventional bonds: single, double, aromatic, …

Dynamical bonds:created single, broken single, …

CGR could be viewed as a pseudo-molecule representing a given reaction

Condensed Graph of Reaction

Condensed Graph of Reaction

• CGR condenses the structural information about productsand reactants

several graphs into one only graph

• This simplified presentation opens an opportunity toapply to CGR the methods developed inchemoinformatics for individual molecules

A. Varnek, D. Fourches, F. Hoonakker, V. P. Solov’ev, J. Computer-Aided Molecular Design, 2005, 19, 693-703

ISIDA fragment descriptors

Reaction can be encoded by a descriptor vector

which can be used in structure-reactivity modeling

Condensed graph of

reaction

2 1 2 …

…

ISIDA/CGR fragment descriptors

A. Varnek In: "Chemoinformatics and Computational Chemical Biology", J. Bajorath, Ed., Springer, 2010

Sums of property-dependent P(i) contributions from remote atoms i, at tiK

away from the ‘reactive’ center K, modulated by various working hypotheses:

𝐸𝐸𝐷𝑝,𝑒,𝑜,𝑤,𝑐 𝐾 =

𝑖=1

𝑁

𝛿𝑐 𝑖, 𝐾 × 𝑃𝑝𝑒 (𝑖) × 𝑒𝑥𝑝 −1/𝑤 × 𝜏𝑖𝐾 − 𝑜 2

𝜏𝑖𝐾 = 0.5 𝑖𝑓 𝑖 = 𝐾

𝑠ℎ𝑜𝑟𝑡𝑒𝑠𝑡 − 𝑝𝑎𝑡ℎ 𝑡𝑜𝑝𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑖, 𝐾 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

𝛿𝑐(𝑖, 𝐾) = 1 𝑖𝑓 𝑐 = 0 𝑂𝑅(𝑝𝑎𝑡ℎ 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑖 𝑎𝑛𝑑 𝐾 𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠 𝑜𝑛𝑙𝑦 𝑢𝑛𝑠𝑎𝑡𝑢𝑟𝑎𝑡𝑒𝑑 𝑎𝑡𝑜𝑚𝑠)0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

Property type p=1..11

Property power e=1..2

Neighborhood control o=1..4Neighborhood width w=2..5

Conjugation toggle c=0,1

11x2x4x4x2=704 EED terms…

Electron Effect Descriptors

M.Elhabiri, E. Davioud-Charvet, D. Horvath, A. Varnek et al., Chemistry Eur. J, 2014, 20, 1 – 11

Atom Properties Tracked in EED

• Different scenarios of structure-reactivity modeling

• Descriptors for reactions

- Condensed Graph of Reaction / ISIDA descriptors

- Electron Effect Descriptors

• Michael reaction case

OUTLINE

Solvent

Hydrophobic (kerosene, benzene, …)

Polar Aprotic (THF, DMSO, …)

Polar Protic (water, acetic acid ...)

No solvent: reaction occurs in pure solutions of the reagents

Catalyst

Bronsted acids (Hydrochloric acid, …

Lewis acids (transition metal ions, ….)

Basic (pyridine, Na ethanolate, ….)

No catalyst: a catalyst is not needed, or autocatalysis takes place

Reaction occurs in different conditions characterized by solvent and catalyst

Michael reaction

Michael donor

Michael acceptor

For given Nu and R1 – R4, which conditions

(catalyst, solvent) lead to high reaction yield ?

Michael reaction

• to build QSRR models predicting optimal reaction conditions for a query reaction.

Each model will answer a punctual question such as “Is this processfeasible with Brønsted acid catalysts?”, “Is this process feasible in aproticpolar solvents?”, etc.

• to develop a public predictive tool adapted to the Michael-type reaction case

Michael reaction: goals of the modeling

• 53 polar aprotic solvent

• 52 no solvent

• 103 polar protic solvent

• 93 Lewis acid catalyst

• 61 no catalyst

• 40 hydrophobic solvent

• 57 Bronsted acid catalyst

• 45 Basic catalysis

• 24 Decoys (not observed Michael reactions)

Notice that one same reaction may proceed under

different conditions !

Michael reaction: data

The data set consists in 222 reactions:

Non-occuring reaction

Occuring reaction :

condensation of hydroxylamine and aldehyde

Decoy Michael Addition

Compatibility of the Michael reaction in the database with each of the condition

classes is rendered by the condition bitvector.

0 1 1 0 0 0 1 0 0

SolventHydro-phobic

SolventPolar

Aprotic

SolventPolar Protic

No Solvent CatalystBronsted

Acid

CatalystLewis Acid

CatalystBasic

NoCatalyst

Not observed

A bit is set on if the reaction was seen to happen under this condition.

NOTE – if off, it means we don’t know whether it’s feasible!

An additional ‘no-go’ bit is on if the given Michael addition should never be

observed (because of competing carbonyl addition).

Bitvector of reaction conditions

Modeling setup

Goal: preparation of 9 two-class classification models:

- 8 models for reaction feasibility for each catalyst or solvent type,

- 1 model « Michael / non-Michael »

Descriptors: ISIDA/CGR, EED … and also MOLMAP, CDK

Scenarios: reagent-based, product-based and reaction-based

Performance assessment : ROC AUC in 3-fold cross-validation

Machine-learning methods: Random Forest, SVM, Naïve Bayes

CGR for Michael reaction

created single double to single

Reaction descriptors

EED ISIDA

Solv

:A

Solv

:NA

Solv

:P

Cat:L

A

Cat:N

A

Solv

:H

Cat:B

A

Cat:B

Cat:N

O

1

0.9

0.8

0.7

0.6

0.5

1

0.9

0.8

0.7

0.6

0.5

Solv

:A

Solv

:NA

Solv

:P

Cat:L

A

Cat:N

A

Solv

:H

Cat:B

A

Cat:B

Cat:N

O

ROC AUC

ROC AUC = 1 corresponds to an ideal model

G. Marcou, J. Aires de Sousa, D. Latino, A. Deluca, D. Horvath, V. Rietsch, and A. Varnek J. Chem. Inf. Model., 2015, 55, 239−250.

Michael reaction: models performance

EED ISIDA

Reagent descriptors

Michael reaction: models performance

G. Marcou, J. Aires de Sousa, D. Latino, A. Deluca, D. Horvath, V. Rietsch, and A. Varnek J. Chem. Inf. Model., 2015, 55, 239−250.

http://infochim.u-strasbg.fr/webserv/VSEngine.html

WEB-based expert system predicting Michael addition feasibility

Model validation on the set of 52 reactions extracted from the literature

Model # in the set # correctly predicted

Catalyst and solvent 52 8

aprotic solvent 12 2

Protic solvent 21 21

No catalyst 26 14

base-catalyzed 19 8

Brønsted acids 2 1

Model failure or uncomplete data for the modeling ?

Michael reaction: external validation

Both ISIDA/CGR and EED descriptors provide with acceptable models in cross-validation

Reagent, Product and Reaction scenarios perform similarly

The models cross-validate very well, but external testing is inconclusive – more data are needed !

Conclusions

J. Chem. Inf. Model., 2015, 55, 239−250

Campus France for a Franco-Portugese grant “PESSOA”

ChemAxon for the license

Thanks