Source-linked AI summary
Neural networks for the prediction organic chemistry reactions
Jennifer N. Wei, David Duvenaud, Alán Aspuru-Guzik
TL;DR
Organic reaction prediction remains difficult because existing approaches rely on expert rules or matching known reactions, limiting prediction of previously unseen reactions. The paper uses neural networks with reaction fingerprints to predict reaction types and combines them with SMARTS transformations for product prediction. The system achieves 85% accuracy on test reactions and 80% on selected textbook questions, while product prediction remains limited by SMARTS transformations.
Problem
Existing reaction-prediction methods rely on expert rules or database matches, whereas predicting previously unseen reactions requires connecting learned information to new reactions.
Method
The system concatenates reactant and reagent fingerprints, feeds them to a neural network for reaction-type prediction, and applies SMARTS transformations to predict products.
Results
85% accuracy was achieved on test reactions and 80% on selected textbook questions; the algorithm predicted product structures for a little more than half of the problems.
Takeaways & Limitations
Fingerprint-based neural networks identified correct reaction types and could further predict product structures for more than half of the evaluated problems.
Takeaways & Limitations
The main limitation in product prediction is the capability of SMARTS transformations to accurately describe the reaction.
Abstract
from arXiv · showhide
Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and re- actants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook.
Introduction
Organic reaction prediction has traditionally depended on expert rules or database matches, limiting performance on previously unseen reactions. This work explores neural-network prediction using reaction fingerprints learned from reactants and reagents.
- Existing approaches: Earlier synthetic-design and reaction-prediction algorithms encoded expert rules, specialized subroutines, reaction mechanisms, or database transformations.These approaches included CAMEO, EROS, SOPHIA, KOSP, electron-flow methods, and mechanism-search algorithms.
- Existing approaches: Database-based methods can accurately predict chemistry for queries matching reactions in their knowledge bases but cannot predict previously unseen reactions like human chemists.New reactions require connecting information from trained examples to reactions not yet encountered.
- Existing approaches: Quantum-guided prediction can explore broader reaction spaces, but new calculations for each reaction family make large-scale application prohibitively costly.The computational burden increases when expanding beyond previously treated reaction families.
- Machine learning motivation: Statistical machine learning offers a more general strategy because it can sometimes generalize or extrapolate to new examples.The paper relates this possibility to successful applications in image recognition, games, and other large search spaces.
- This work: The proposed system concatenates reactant and reagent fingerprints, uses a neural network to classify reaction types, and applies corresponding transformations to predict products.The initial exploration is limited to aliphatic, non-stereospecific molecules, with broader chemical space requiring enough example reactions.
- This work: The approach could be extended with more reactions, experimental conditions, and training data toward multistep reaction prediction and retrosynthetic planning.The authors describe these as future extensions of the current system.
Results and Discussion
The fingerprint methods were evaluated on balanced reaction data and textbook problems, with Morgan and neural fingerprints generally outperforming the baseline in reaction-type and product prediction.
- Cross-validation set: Morgan and neural fingerprint confusion matrices nearly perfectly matched predicted and true reaction types, except for reaction types 3 and 4.Those two reactions were assigned 50% probabilities because the training setup assumed they occurred together, producing a 2x2 ambiguity.
- Cross-validation set: The baseline frequently overpredicted the NR class and overgeneralized other reaction types, producing horizontal bands in its confusion matrix.The fingerprint-based matrices showed substantially more specific correspondence between predicted and correct reaction types.
- Exam reaction-type prediction: On Wade problem 8-47, Morgan predicted 12 of 15 answers correctly, while neural and baseline methods each predicted 11 of 15.Both fingerprint methods assigned higher probabilities to correct answers than the baseline.
- Product prediction: Morgan predicted 8 of 15 products correctly and achieved an average Tanimoto score of 0.793, compared with 7 products and 0.776 for the neural fingerprint.Product accuracy and similarity were evaluated against the answer-key products using probability-weighted predictions.
- Product prediction: The main product-prediction limitation was that SMARTS transformations could not accurately describe every reaction-type transformation for all input reactants.Consequently, a reaction type could be predicted correctly while its product was not.
Conclusion
The fingerprint-based neural network identified reaction types from reactants and reagents, then used SMARTS transformations to predict products. It achieved 85% accuracy on test reactions and 80% on selected textbook questions, while product prediction succeeded for slightly more than half of problems.
- The algorithm learns probabilities across a range of reaction types and can expand to new types by adding curated reactions to the training set.
- 85% accuracy was achieved on test reactions, while selected textbook questions reached 80% accuracy for reaction-type prediction.
- The system predicted product structures for a little more than half of the textbook problems after identifying reaction types.
- The main product-structure limitation was that SMARTS transformations could not completely describe reaction mechanisms.
- The approach can be extended to reaction conditions and ultimately to multistep synthetic pathways optimized for conditions, material cost, and step count.
- The work represents a step toward machine-learning-based automatic synthesis planning for organic molecules.
Methods
The methods construct a labeled reaction dataset, encode substrates and reagents with molecular fingerprints, predict reaction-type probabilities using a neural network, and apply corresponding SMARTS transformations to generate products.
- A dataset of 16 reaction types was generated, comprising four alkylhalide and twelve alkene reactions.
- Reaction labels were assigned from expert-defined conditions, with unmatched combinations designated Null Reactions and encoded as target probability vectors.
- Methyl-shift elimination and substitution cases involving bulky alkylhalides were assigned 50% probability to each corresponding reaction type.
- Products were generated by applying each reaction type’s SMARTS transformation to the two reactants when transformation requirements were satisfied.
- The reaction fingerprint concatenated molecular fingerprints for the substrate, secondary reactant, and reagent before neural-network prediction.
- Both Morgan circular fingerprints and differentiable neural fingerprints were evaluated, with a one-hidden-layer network containing 100 units.
- The method may learn reagent or secondary-reactant associations rather than broader reaction characteristics when those components are required.
- A baseline fingerprint represented the 20 most common secondary reactants and 30 most common reagents using one-hot features.
Supporting Information Available
The supporting information provides reaction-similarity comparisons, source code, complete training datasets, and access information for the released materials.
- SI Table 1 compares the similarity of examination-question reactants and reagents with training-set reactants and reagents.
- The code and full training datasets are made available through the project’s GitHub repository.
- The supporting material is available free of charge via the Internet.