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To develop the intuition and understanding for predicting reactions, a human must take many semesters of organic chemistry and gather insight over several years of lab experience. Over the past 40 years, various algorithms have been developed to assist with synthetic design, reaction prediction, and starting material selection. (1, 2) LHASA was the first of these algorithms to aid in developing retrosynthetic pathways. (3) This algorithm required over a decade of effort to encode the necessary subroutines to account for the various subtleties of retrosynthesis such as functional group identification, polycyclic group handling, relative protecting group reactivity, and functional group based transforms. (4-7)

In the late 1980s to the early 1990s, new algorithms for synthetic design and reaction prediction were developed. CAMEO, (8) a reaction predicting code, used subroutines specialized for each reaction type, expanding to include reaction conditions in its analysis. EROS (9) identified leading structures for retrosynthesis by using bond polarity, electronegativity across the molecule, and the resonance effect to identify the most reactive bond. SOPHIA (10) was developed to predict reaction outcomes with minimal user input; this algorithm would guess the correct reaction type subroutine to use by identifying important groups in the reactants; once the reactant type was identified, product ratios would be estimated for the resulting products. SOPHIA was followed by the KOSP algorithm and uses the same database to predict retrosynthetic targets. (11) Other methods generated rules based on published reactions and use these transformations when designing a retrosynthetic pathway. (12, 13) Some methods encoded expert rules in the form of electron flow diagrams. (14, 15) Another group attempted to grasp the diversity of reactions by creating an algorithm that automatically searches for reaction mechanisms using atom mapping and substructure matching. (16)

While these algorithms have their subtle differences, all require a set of expert rules to predict reaction outcomes. Taking a more general approach, one group has encoded all of the reactions of the Beilstein database, creating a “Network of Organic Chemistry”. (2, 17) By searching this network, synthetic pathways can be developed for any molecule similar enough to a molecule already in its database of 7 million reactions, identifying both one-pot reactions that do not require time-consuming purification of intermediate products (18) and full multistep reactions that account for the cost of the materials, labor, and safety of the reaction. (2) Algorithms that use encoded expert rules or databases of published reactions are able to accurately predict chemistry for queries that match reactions in its knowledge base. However, such algorithms do not have the ability of a human organic chemist to predict the outcomes of previously unseen reactions. In order to predict the results of new reactions, the algorithm must have a way of connecting information from reactions that it has been trained upon to reactions that it has yet to encounter.

Another strategy of reaction prediction algorithm draws from principles of physical chemistry and first predicts the energy barrier of a reaction in order to predict its likelihood. (19-24) Specific examples of reactions include the development of a nanoreactor for early Earth reactions, (20, 21) heuristic aided quantum chemistry, (23) and ROBIA, (25) an algorithm for reaction prediction. While methods that are guided by quantum calculations have the potential to explore a wider range of reactions than the heuristic-based methods, these algorithms would require new calculations for each additional reaction family and will be prohibitively costly over a large set of new reactions.

170, which is on the order of chemical space for medium sized molecules. A third strategy for reaction prediction algorithms uses statistical machine learning. These methods can sometimes generalize or extrapolate to new examples, as in the recent examples of picture and handwriting identification, (26, 27) playing video games, (28) and most recently, playing Go. (29) This last example is particularly interesting as Go is a complex board game with a search space of 10, which is on the order of chemical space for medium sized molecules. (30) SYNCHEM was one early effort in the application of machine learning methods to chemical predictions, which relied mostly on clustering similar reactions, and learning when reactions could be applied based on the presence of key functional groups. (13)

Today, most machine learning approaches in reaction prediction use molecular descriptors to characterize the reactants in order to guess the outcome of the reaction. Such descriptors range from physical descriptors such as molecular weight, number of rings, or partial charge calculations to molecular fingerprints, a vector of bits or floats that represent the properties of the molecule. ReactionPredictor (31, 32) is an algorithm that first identifies potential electron sources and electron sinks in the reactant molecules based on atom and bond descriptors. Once identified, these sources and sinks are paired to generate possible reaction mechanisms. Finally, neural networks are used to determine the most likely combinations in order to predict the true mechanism. While this approach allows for the prediction of many reactions at the mechanistic level, many of the elementary organic chemistry reactions that are the building blocks of organic synthesis have complicated mechanisms, requiring several steps that would be costly for this algorithm to predict.

Many algorithms that predict properties of organic molecules use various types of fingerprints as the descriptor. Morgan fingerprints and extended circular fingerprints (33, 34) have been used to predict molecular properties such as HOMO–LUMO gaps, (35) protein–ligand binding affinity, (36) and drug toxicity levels (37) and even to predict synthetic accessibility. (38) Recently Duvenavud et al. applied graph neural networks (39) to generate continuous molecular fingerprints directly from molecular graphs. This approach generalizes fingerprinting methods such as the ECFP by parametrizing the fingerprint generation method. These parameters can then be optimized for each prediction task, producing fingerprint features that are relevant for the task. Other fingerprinting methods that have been developed use the Coulomb matrix, (40) radial distribution functions, (41) and atom pair descriptors. (42) For classifying reactions, one group developed a fingerprint to represent a reaction by taking the difference between the sum of the fingerprints of the products and the sum of the fingerprints of the reactants. (43) A variety of fingerprinting methods were tested for the constituent fingerprints of the molecules.

In this work, we apply fingerprinting methods, including neural molecular fingerprints, to predict organic chemistry reactions. Our algorithm predicts the most likely reaction type for a given set of reactants and reagents, using what it has learned from training examples. These input molecules are described by concatenating the fingerprints of the reactants and the reagents; this concatenated fingerprint is then used as the input for a neural network to classify the reaction type. With information about the reaction type, we can make predictions about the product molecules. One simple approach for predicting product molecules from the reactant molecules, which we use in this work, is to apply a SMARTS transformation that describes the predicted reaction. Previously, sets of SMARTS transformations have been applied to produce large libraries of synthetically accessible compounds in the areas of molecular discovery, (44) metabolic networks, (45) drug discovery, (46) and discovery of one-pot reactions. (47) In our algorithm, we use SMARTS transformation for targeted prediction of product molecules from reactants. However, this method can be replaced by any method that generates product molecule graphs from reactant molecule graphs. An overview of our method can be found in Figure 1 and is explained in further detail in Prediction Methods

Figure 1 Figure 1. An overview of our method for predicting reaction type and products. A reaction fingerprint, made from concatenating the fingerprints of reactant and reagent molecules, is the input for a neural network that predicts the probability of 17 different reaction types, represented as a reaction type probability vector. The algorithm then predicts a product by applying to the reactants a transformation that corresponds to the most probable reaction type. In this work, we use a SMARTS transformation for the final step.