Computer-Assisted Retrosynthesis

2018/06/02
M1 Koki Sasamoto1

Introduction
Retrosynthesis

2Introduction
Computer-assisted synthesis planning (CASP)

 It takes less time to devise synthetic routes.
 Proportion of successful synthesis rises.
 Scientists learn from the results of CASP.
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Contents
1. Introduction
2. Rule-based expert system
3. Machine Learning
4. Summary

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LHASA
Interactive system using synthetic tree

E. J. Corey and W. T. Wipke, J. Am. Chem. Soc., 1972, 94, 431. 5

Transform Mechanism
Transform lists
Two-group transform
One-group transform

Functional Group Interchange (FGI) … etc.

Each transformation has data table.
Which bond is cleaved
Rating depend on difficulties … etc.
E. J. Corey and W. T. Wipke, J. Am. Chem. Soc., 1972, 94, 431. 6

Retrosynthesis Example

E. J. Corey and W. T. Wipke, J. Am. Chem. Soc., 1972, 94, 431. 7

Other CASP Programs
Failure of CASP
SECS, SYNCHEM, SYNGEN, IGOR, WODCA, etc…
These provided incompatible synthetic routes.

Lack of computing capacity
only having simplified rule set

Improved machine power solved this problem.
8Chematica
Contains 10 millions reaction dataNOC(Network of Organic Chemistry)

The lowest cost synthetic
pathway of taxol(within 50steps)

B. A. Grzybowski et al. Angew. Chem. Int. Ed. 2016, 55, 5904. 9

Syntaurus
Algorithm for retrosynthesis

B. A. Grzybowski et al. Angew. Chem. Int. Ed. 2016, 55, 5904. 10

Scoring Functions
Chemical Scoring Function (CSF)
Number of rings

MassReaction Scoring Function (RSF)
Necessity of protection
Yield etc…
B. A. Grzybowski et al. Angew. Chem. Int. Ed. 2016, 55, 5904. 11

Problems of Expert System
Forward PredictionDependence of reaction templates…Trouble of template creation…Ignoring the context of molecules

Application of unknown reactions

Retrosynthesis
Design of scoring functions

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Machine Learning

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Machine Learning
Supervised learning
patternanswer“Tiger”

Unsupervised learning

Using unlabeled data
Reinforcement leaning
Maximizing rewards
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Machine Learning
Supervised learning

Classification
output : discrete category
example : estimation of animal type

Regression
output : continuous variable
example : consumption of the entire economy
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Machine Learning
Classification“Tiger”
Feature extraction16

Feature Space17

Neural Network
Learn the best parameter automatically18

Reaction Type Prediction
Predict 17 reaction types from reactants and reagent

A. Aspulu-Guzik et al., ACS Cent. Sci., 2016, 2, 725.

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Reaction Type Prediction
Predicted probability of each reaction type

Teacher signal[0,0,0,0.5,0.5,0.0……..] (reaction type 3 or 4)[0,1,0,0,0,…..] (others)
A. Aspulu-Guzik et al., ACS Cent. Sci., 2016, 2, 725. 20

Reaction Type Prediction
Attempts to solve textbook problems(Wade, Organic Chemistry, 6th ed.)Results

ExampleA. Aspulu-Guzik et al., ACS Cent. Sci., 2016, 2, 725. 21

Extended Reaction Templates
Improvement of Neural Network…dropout, highway network, ELU(activation function)

M. H. S. Segler and M.P. Waller, Chem. Eur. J. 2017, 23, 5966. 22

Prediction Results
Neural Network learned molecular context?

M. H. S. Segler and M.P. Waller, Chem. Eur. J. 2017, 23, 5966. 23

Candidate Generation and Ranking
Reaction type prediction by two frameworks

Forward Enumeration
1689 reaction templates

Candidate Ranking
focus only on changed atoms / bonds
C. W. Corey et al., ACS Cent. Sci., 2017, 3, 434.

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Candidate Ranking
Focus on changed atoms / bonds

C. W. Corey et al., ACS Cent. Sci., 2017, 3, 434.

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Reaction Prediction
Results

Problems of template-based model
coveragescalability

C. W. Corey et al., ACS Cent. Sci., 2017, 3, 434.

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Sequence to Sequence (seq2seq)
Is the chemical reaction similar to translation?

Reactant

Product

P. Schwaller and T. Gaudin, arXiv:1711.04810v2, 201727

Sequence to Sequence (seq2seq)
Reaction templates are not necessary.

P. Schwaller and T. Gaudin, arXiv:1711.04810v2, 201728

Sequence to Sequence (seq2seq)
Datasets
Training
Jin’s USPTO training set … 395496
Test

Jin’s USPTO test set … 38648
Lowe’s test set … 50258

Results

P. Schwaller and T. Gaudin, arXiv:1711.04810v2, 201729

Difficulties in Retrosynthesis
Very huge search space> 10000 reactions
1030 ~ 1050 possible pathways
Necessity of efficient search method

B. A. Grzybowski et al. Angew. Chem. Int. Ed. 2016, 55, 5904. 30

Difficulties in Retrosynthesis
Scoring Function
Heuristic dependenceNecessity to expand synthetic tree to the end31

Monte-Carlo Tree Search (MCTS)
Reinforcement learning to find the best route

UCB1 =(In fact, this parameter is more complicated…)
M. H. S. Segler and M.P Waller, Nature, 2018, 555, 604. 32

Expansion Procedure

M. H. S. Segler and M.P Waller, Nature, 2018, 555, 604. 33

Datasets
Training datasets
12.4 million single-step reactions- rollout rules- expansion rules

Data Augmentation
Generate 100 million negative reactions

M. H. S. Segler and M.P Waller, Nature, 2018, 555, 604. 34

Results – MCTS vs BFS
MCTS was faster than BFS (Breadth First Search).

M. H. S. Segler and M.P Waller, Nature, 2018, 555, 604. 35

Results – MCTS vs Human
MCTS routes were more preferred.

M. H. S. Segler and M.P Waller, Nature, 2018, 555, 604. 36

Summary
Expert system’s problems
・ Troublesome preparation of reaction rules
・ Application to unknown reactions
・ Lack of scoring function
Machine Learning
・ The above problems can be solved.
・ Route design could be done at a level approaching
humans.
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FutureCurrent issues
・ The best model was still unknown.

(Finger Print, seq2seq, MCTS?)
Using images for compound cognition,
Graph Representation,
GAN (Generative Adversarial Network) … ?
・ Reaction conditions are not considered.
New reaction descriptor is required.
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Appendix
ECFP4

https://chembioinfo.com/2011/10/30/revisiting-molecular-hashed-fingerprints/

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Appendix
Rule extraction

M. H. S. Segler and M.P. Waller, Chem. Eur. J. 2017, 23, 5966. 41

Appendix
In this example, chemists preferred literature routes.

M. H. S. Segler and M.P Waller, Nature, 2018, 555, 604.

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