Learning Graph Models for Retrosynthesis Prediction

Retrosynthesis prediction is a fundamental problem in organic synthesis , where the task is to identify precursor molecules that can be used to synthesize a target molecule . A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists . Building on this viewpoint , this paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction . The model first predicts the set of graph edits transforming the target into incomplete molecules called synthons . Next , the model learns to expand synthons into complete molecules by attaching relevant leaving groups . This decomposition simplifies the architecture , making its predictions more interpretable , and also amenable to manual correction . Our model achieves a top-1 accuracy of 53.7 % , outperforming previous template-free and semi-template-based methods . 1 Introduction . Retrosynthesis prediction , first formalized by E. J. Corey [ Corey , 1991 ] is a fundamental problem in organic synthesis that attempts to identify a series of chemical transformations for synthesizing a target molecule . In the single-step formulation , the task is to identify a set of reactant molecules given a target . Beyond simple reactions , many practical tasks involving complex organic molecules are difficult even for expert chemists . As a result , substantial experimental exploration is needed to cover for deficiencies of analytical approaches . This has motivated interest in computer-assisted retrosynthesis [ Corey and Wipke , 1969 ] , with a recent surge in machine learning methods [ Chen et al. , 2019 , Coley et al. , 2017b , Dai et al. , 2019 , Zheng et al. , 2019 , Genheden et al. , 2020 ] . Computationally , the main challenge is how to explore the combinatorial space of reactions that can yield the target molecule . Largely , previous methods for retrosynthesis prediction can be divided into template-based [ Coley et al. , 2017b , Dai et al. , 2019 , Segler and Waller , 2017 ] and template-free [ Chen et al. , 2019 , Zheng et al. , 2019 ] approaches . Template-based methods match a target molecule against a large set of templates , which are molecular subgraph patterns that highlight changes during a chemical reaction . Despite their interpretability , these methods fail to generalize to new reactions . Template-free methods bypass templates by learning a direct mapping from the SMILES [ Weininger , 1988 ] representations of the product to reactants . Despite their greater generalization potential , these methods generate reactant SMILES character by character , increasing generation complexity . 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . Another important consideration in building retrosynthesis models is aligning model design with strategies adopted by expert chemists . These strategies are influenced by fundamental properties of chemical reactions , independent of complexity level : ( i . ) the product atoms are always a subset of the reactant atoms1 , and ( ii . ) the molecular graph topology is largely unaltered from products to reactants . For example , in the standard retrosynthesis dataset , only 6.3 % of the atoms in the product undergo any change in connectivity . This consideration has received more attention in recent semi-template-based methods [ Shi et al. , 2020 , Yan et al. , 2020 ] , that generate reactants from a product in two stages : ( i . ) first identify intermediate molecules called synthons , ( ii . ) and then complete synthons into reactants by sequential generation of atoms or SMILES characters .. Our model GRAPHRETRO also uses a similar workflow . However , we avoid sequential generation for completing synthons by instead selecting subgraphs called leaving groups from a precomputed vocabulary . This vocabulary is constructed during preprocessing by extracting subgraphs that differ between a synthon and the corresponding reactant . The vocabulary has a small size ( 170 for USPTO-50k ) indicating remarkable redundancy , while covering 99.7 % of the test set . Operating at the level of these subgraphs greatly reduces the complexity of reactant generation , with improved empirical performance . This formulation also simplifies our architecture , and makes our predictions more transparent , interpretable and amenable to manual correction . The benchmark dataset for evaluating retrosynthesis models is USPTO-50k [ Schneider et al. , 2016 ] , which consists of 50000 reactions across 10 reaction classes . The dataset contains an unexpected shortcut towards predicting the edit , in that the product atom with atom-mapping 1 is part of the edit in 75 % of the cases , allowing predictions that depend on the position of the atom to overestimate performance . We canonicalize the product SMILES and remap the existing dataset , thereby removing the shortcut . On this remapped dataset , GRAPHRETRO achieves a top-1 accuracy of 53.7 % when the reaction class is not known , outperforming both template-free and semi-template-based methods . 2 Related Work . Retrosynthesis Prediction Existing machine learning methods for retrosynthesis prediction can be divided into template-based , template-free and recent semi-template-based approaches . Template-Based : Templates are either hand-crafted by experts [ Hartenfeller et al. , 2011 , Szymkuć et al. , 2016 ] , or extracted algorithmically from large databases Coley et al . [ 2017a ] , Law et al . [ 2009 ] . Exhaustively applying large template sets is expensive due to the involved subgraph 1ignoring impurities matching procedure . Template-based methods therefore utilize different ways of prioritizing templates , by either learning a conditional distribution over the template set [ Segler and Waller , 2017 ] , ranking templates based on molecular similarities to precedent reactions [ Coley et al. , 2017b ] or directly modelling the joint distribution of templates and reactants using logic variables [ Dai et al. , 2019 ] . Despite their interpretability , these methods fail to generalize outside their rule set . Template-Free : Template-free methods [ Liu et al. , 2017 , Zheng et al. , 2019 , Chen et al. , 2019 ] learn a direct transformation from products to reactants using architectures from neural machine translation and a string based representation of molecules called SMILES [ Weininger , 1988 ] . Linearizing molecules as strings does not utilize the inherently rich chemical structure . In addition , the reactant SMILES are generated from scratch , character by character . Attempts have been made to improve validity by adding a syntax correcter [ Zheng et al. , 2019 ] and a mixture model to improve diversity of suggestions [ Chen et al. , 2019 ] , but the performance remains worse than [ Dai et al. , 2019 ] on the standard retrosynthesis dataset . Sun et al . [ 2021 ] formulate retrosynthesis using energy-based models , with additional parameterizations and loss terms to enforce the duality between forward ( reaction prediction ) and backward ( retrosynthesis ) prediction . Semi-Template-Based : Our work is closely related to recently proposed semi-template-based methods [ Shi et al. , 2020 , Yan et al. , 2020 ] , which first identify synthons and then expand synthons into reactants through sequential generation using either a graph generative model [ Shi et al. , 2020 ] or a Transformer [ Yan et al. , 2020 ] . To reduce the complexity of reactant generation , we instead complete synthons using subgraphs called leaving groups selected from a precomputed vocabulary . This allows us to view synthon completion as a classification problem instead of a generative one . We also utilize the dependency graph between possible edits , and update edit predictions using a message passing network ( MPN ) [ Gilmer et al. , 2017 ] on this graph . Both innovations together yield a 4.8 % and 3.3 % performance improvement respectively over previous semi-template-based methods . Reaction Center Identification The reaction center covers a small number of participating atoms involved in the reaction . Our work is also related to models that predict reaction outcomes by learning to rank atom pairs based on their likelihood to be in the reaction center [ Coley et al. , 2019 , Jin et al. , 2017 ] . The task of identifying the reaction center is related to the step of deriving the synthons in our formulation . Our work departs from [ Coley et al. , 2019 , Jin et al. , 2017 ] as we utilize the property that new bond formations occur rarely ( ~0.1 % ) from products to synthons , allowing us to predict a score only for existing bonds and atoms and reduce prediction complexity from O ( N2 ) to O ( N ) . We also utilize the dependency graph between possible edits , and update edit predictions using a MPN on this graph . Utilizing Substructures Substructures have been utilized in various tasks from sentence generation by fusing phrases to molecule generation and optimization [ Jin et al. , 2018 , 2020 ] . Our work is closely related to [ Jin et al. , 2020 ] which uses precomputed substructures as building blocks for property-conditioned molecule generation . However , instead of precomputing , synthons —analogous building blocks for reactants— are indirectly learnt during training . 3 Model Design . Our approach leverages the property that graph topology is largely unaltered from products to reactants . To achieve this , we first derive suitable building blocks from the product called synthons , and then complete them into valid reactants by adding specific functionalities called leaving groups . These derivations , called edits , are characterized by modifications to bonds or hydrogen counts on atoms . We first train a neural network to predict a score for possible edits ( Section 3.1 ) . The edit with the highest score is then applied to the product to obtain synthons . Since the number of unique leaving groups are small , we model leaving group selection as a classification problem over a precomputed vocabulary ( Section 3.2 ) . To produce candidate reactants , we attach the predicted leaving group to the corresponding synthon through chemically constrained rules . The overall process is outlined in Figure 1 . Before describing the two modules , we introduce relevant preliminaries that set the background for the remainder of the paper . Retrosynthesis Prediction A retrosynthesis pair R is described by a pair of molecular graphs ( Gp , Gr ) , where Gp are the products and Gr the reactants . A molecular graph is described as G = ( V , E ) with atoms V as nodes and bonds E as edges . Prior work has focused on the single product case , while reactants can have multiple connected components , i.e . Gr = { Grc } Cc=1 . Retrosynthesis pairs are atom-mapped so that each product atom has a unique corresponding reactant atom . The retrosynthesis task then , is to infer { Grc } Cc=1 given Gp . Edits Edits consist of ( i . ) atom pairs { ( ai , aj ) } where the bond type changes from products to reactants , and ( ii . ) atoms { ai } where the number of hydrogens attached to the atom change from products to reactants . We denote the set of edits by E. Since retrosynthesis pairs in the training set are atom-mapped , edits can be automatically identified by comparing the atoms and atom pairs in the product to their corresponding reactant counterparts . Synthons and Leaving Groups Applying editsE to the product Gp results in incomplete molecules called synthons . Synthons are analogous to rationales or building blocks , which are expanded into valid reactants by adding specific functionalities called leaving groups that are responsible for its reactivity . We denote synthons by Gs and leaving groups by Gl . We further assume that synthons and leaving groups have the same number of connected components as the reactants , i.e Gs = { Gsc } Cc=1 and Gl = { Glc } Cc=1 . This assumption holds for 99.97 % reactions in the training set . Formally , our model generates reactants by first predicting the set of edits E that transform Gp into Gs , followed by predicting a leaving group Glc to attach to each synthon Gsc . The model is defined as P ( Gr|Gp ) = ∑ E , Gl P ( E|Gp ) P ( Gl|Gp , Gs ) , ( 1 ) where Gs , Gr are deterministic given E , Gl , and Gp .

Summary: ## Summary.  The authors propose a new method for single-step retrosynthesis prediction. Their method first predicts graph edits using a message passing network yielding synthons which are then completed by adding leaving groups. The leaving groups are chosen from a fixed set using another MPN.