Artificial intelligence in chemistrySYNTHETIC ORGANIC CHEMISTRY Synthesis is the process of creating new molecules designated as a target through controlled stepwise chemical reactions

ARTIFICIAL INTELLIGENCE IN CHEMISTRY Blaine Berrington

OVERVIEW

• Provide an overview of synthesis and its challenges

• Discuss past approaches of AI to synthesis

• Discuss solutions that modern AI poses for synthesis

• Synthesis planning

• Reaction prediction

• summarize other applications of AI to the field of chemistry

SYNTHETIC ORGANIC CHEMISTRY

Synthesis is the process of creating new molecules designated as a target through controlled stepwise chemical reactions.

• Used in a huge array of industries from pharmaceuticals and dyes to superconductors and plastics

• Involves a number of problem solving strategies

• Requires meticulous planning and skill to carry out

https://techtransfer.cancer.gov/aboutttc/successstories/taxol

https://en.wikipedia.org/wiki/Paclitaxel

SYNTHESIS

• Intermediate approach

• Direct Associative approach

• Logic-centered approach

• Reduction of chemical complexity

• Formation of a “tree” of paths

• Time consuming

https://www.organic-chemistry.org/totalsynthesis/totsyn04/quinine-woodward-williams.shtm

LOGIC-CENTERED SYNTHESIS

• Perception of structurally important features within a target molecule: • Functional groups • Stereocenters • Regional reactivity (instability and sensitivity)

• Reductions of molecular complexity (goal) • Internal connectivity scission • Chain/appendage reduction • Functionality removal • Stereochemistry simplification • Instability removal

• Subgoals • Functional group interchange • Protecting groups/positional groups • Rearrangement

RETROSYNTHESIS OF A NATURAL PRODUCT (PENICILLIN)

https://chemistonthekeys.wordpress.com/2012/03/12/classic-synthesis-i-penicillin-v/

SYNTHESIS OF A NATURAL PRODUCT (PENICILLIN)

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https://www.youtube.com/watch?v

=rh0Tn_oPS30

DEVELOPING NEW MOLECULES

• You guess

• Quantitative structure–activity relationship (QSAR) approach

SOFTWARE FOR SYNTHESIS THEN(1960’S)

• Started DENDRAL, LHASA(Corey and Wipke), and SEC (WIPKE) nearly 50 years ago

• Retrosynthesis oriented programs for determining reaction routes were SEC and LHASA

• DENDRAL was used in characterization of unknown molecules via spectral data

• How do computers relate?

RETROSYNTHESIS TO COMPUTERS

LHASA

• Programmed and maintained by Corey as a tool of efficiency for synthesis design

• Interactive with chemist to yield reaction pathways of interest

• Chemist input of target molecule

• boundary conditions with goals are defined

• Inverse synthetic operations that satisfy goals are computed and unlikely solutions are deleted

• Chemist then assesses the outputted precursors

LHASA

• Graphical Module

• Chemist draws a structure

• Represented as a connection table for atoms and bonds

• Coupled with a list of coordinates for atom positions

• Other representations fall short (line notation)

LHASA

• Perception Module

• Recognizes functional groups, chains, rings, symmetry, redundancy, and related atoms

• For Rings: A is the origin atom and path is grown along network until it doubles back on itself. (An appears before Ai) The ring is then added to a list of rings.

Path: A1, A2,..., Ai,..., An, An-1

Sequence: Ai, Ai+1,... An-1

*Allows for set operations

https://www.onlinemathlearning.com/union-set.html

LHASA

• Strategy and control module

• Heuristics applied: introduction of reactive functionalities, mechanistic disconnections, transforms that lead to disconnections

• Knowledge based rules are applied

• Requires a knowledge base of fundamental reactions

LHASA

• Modification module

• Subroutines operating on the connection table are applied to introduce the transforms necessary for generating the precursors

• Making and breaking of bonds, loss/addition of atoms, loss/addition of charge.

LHASA

• Evaluation Module

• Bulk of evaluation is done by the chemist

• Program evaluates valence violations, etc..

• Structural simplicity is evaluated (rings, appendages, etc.)

*ring system simplicity

LHASA

LHASA

DENDRAL (1965)

• Utilized a heuristic based approach to determining molecular structure based off spectral data

• Isomers, alcohols, and ketones were problematic

DENDRAL

• Heuristic approach

LHASA

• Problems with LHASA and DENDRAL

• Memory limitations

• Difficult to add new reactions

• Backtracking the rationale of solutions is difficult

• Doesn’t scale well

AI AND CHEMISTRY TODAY

• Synthesis planning

• Prediction of Organic reaction outcomes

• Robot chemists

• Chemical property prediction

SYNTHESIS PLANNING

• Deep neural networks trained on fundamental organic chemistry retrosynthesis rules

• Trained program run in collaboration with monte carlo search tree algorithm

• Selection

• Expansion

• Exploration

• Updating

• Reaction prediction indistinguishable from a human’s

SYNTHESIS PLANNING

• Training for fundamental rules of organic synthesis

• Neural networks can be trained to recognize and apply retrosynthetic fundamentals

• Use reaction records as a training basis

SYNTHESIS PLANNING

• Monte Carlo Search tree algorithm

• Selection • Child node with greatest probability of succeeding is selected

• Expansion • Successor nodes to the previously selected node are expanded

• Exploration • Reinforcement learning to make random decision further down from children nodes • Children nodes are explored at random assigning a “reward” to each one based on

the proximity of its output to the desired solution

• Updating • Parent nodes are updated based on the scores of the children nodes • A pathway is then selected after updating nodes “reward” states based on selection

of a node that satisfies the query

MONTE CARLO SEARCH

PREDICTION OF REACTION OUTCOMES

• Determine reaction outcome based on reactants and conditions

• The term “reaction” is an abstraction

• Prediction is based on three approaches

• Physical laws

• Rule based expert systems

• Inductive machine learning


• Rule based expert systems

• Employs heuristics, graph rewrite patterns, and constraints

• Drawbacks

• Large knowledge base

• Not scalable

• Confined in its ability


• Physical laws

• reactions are modeled as minimum energy paths between stable configurations on a high-dimensional potential energy surface, where saddle points represent transition states.

• Schrodinger’s equation cannot be solved for exact solutions


• Mechanistic

• Easier to predict

• *preferred representation


• A novel approach

• Incorporates idealized graph based MO’s

• Trained on “productive” reactions

• MO constructive interaction is statistically ranked

• (electron filled/unfilled MO)


• Reaction prediction model

• Requires training set of reactions

• mechanistic construction of MOs

• Ranking of productive mechanisms


• Construction of the molecular orbitals

• For a molecule “m” a connection graph is generated

• Vertices Am represent labeled atoms and the edges Bm

• Quadruples of the filled and unfilled orbitals are generated

• Each atom can have multiple MO designations

m = Gm(Am,Bm)

f := (a, tf , nf , cf )


• Trained neural network: Reaction site filtering

• Reaction explorer system provided training data

• Reactivity (l) is assessed based on the learned model for (a,c) where l=1 or 0

• Trained neural networks using sigmoidal activation functions in a single hidden layer and a single output node

• Gradients on the weights of the neural network are calculated with standard back-propagation

• provide a probabilistic prediction of an (a ,c ) tuple being labeled reactive

• Determines the possible reactions based on electron sources and sinks

• Possibilities involving unfilled unreactive MOs are disregarded

• Orbital interaction computed


• Orbital interaction is computed


• Orbital interaction ranking

• training on ordered pairs of productive and unproductive orbital interactions

• pair of shared weight artificial neural networks, each with a single sigmoidal hidden layer and a linear output node

• sigmoidal output layer with fixed weights of +1,-1

• Yields a ranked set of rational reaction outcomes based on reactants and conditions


• Conclusion

• Huge amounts of data exist for training

• Scalable

• Accurate

OTHER AI APPLICATIONS IN CHEMISTRY

• Robot chemists

• Property prediction

• Electron density prediction

• A lot more...

FUTURE OF AI IN CHEM

• AI will not replace chemists but will be a tool added to the chemist toolkit

• Automated reactions

REFERENCES

• Yanaka, M.; Nakamura, K.; Kurumisawa, A.; Wipke, W. T. Automatic Knowledge Base Building for the Organic Synthesis Design Program (SECS). Tetrahedron Computer Methodology 1990, 3 (6), 359–375.

• Proceedings of the 2019 Workshop on Network Meets AI & ML - NetAI19. 2019.

• Wipke, W.; Ouchi, G. I.; Krishnan, S. Simulation and Evaluation of Chemical Synthesis—SECS: An Application of Artificial Intelligence Techniques. Artificial Intelligence 1978, 11(1-2), 173–193.

• Peiretti, F.; Brunel, J. M. Artificial Intelligence: The Future for Organic Chemistry? ACS Omega 2018, 3 (10), 13263–13266.

• Kayala, M. A.; Azencott, C.-A.; Chen, J. H.; Baldi, P. Learning to Predict Chemical Reactions. Journal of Chemical Information and Modeling 2011, 51 (9), 2209–2222.

QUESTIONS

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Artificial intelligence in chemistrySYNTHETIC ORGANIC CHEMISTRY Synthesis is the process of creating new molecules designated as a target through controlled stepwise chemical reactions