Learning Graph Models for Retrosynthesis Prediction
Authors: Vignesh Ram Somnath
Charlotte Bunne
Connor W. Coley
Andreas Krause
Regina Barzilay
Venue: NeurIPS 2021 Poster (2021)
Keywords: Retrosynthesis
Graph-Neural Networks
Synthons
Computational Chemistry

Abstract: 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.

Learning Graph Models for Retrosynthesis Prediction
Vignesh Ram Somnath !
Charlotte Bunne'
Connor W. Coley?
Andreas Krause
Regina Barzilay²
Department of Computer Science, ETH DDpaarmment of Chemical Engineering, MIT *Computer Science and Artificial Intelligence Lab, MIT
1 {vsomnath, bunnec, krausea)@ethz..ce ch, cccoeyymit. edd,, "reginaccsail. satiisaaallllla................e mit.edu
Abstract
Retrosynthesis prediction a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used synthesize target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Building this paper introduces a graph- based approach that capitalizes on the idea that the graph topology of precursor molecules 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 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 top-1 accuracy of 53.7%, outperforming previous template-free and memi--tmmlltee-bee based methods.
this viewpoint,
Introduction
Retrosynthesis prediction, first formalized by E. J. Corey [Corey, 1991] is fundamental problem in organic synthesis that attempts to identify a series of chemical transformations for synthesizing target molecule. In the single-step formulation, the task is to identify set of reactant molecules given a target. Beyond simple reactions, many practical tasks involving complex organic molecules are difficult even for expert chemists. As 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 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., 201 7b, Dai et al., 2019, Segler and Waller, 2017] and template-free [Chen et al., 2019, Zheng et al., 2019] approaches. Template-
methods match 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 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 (NcurIPS 2021).

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Edit Prediction
N
N
Product
Synthons
Synthon Completion
Br
N-N
Synthons
Reactunts
Figure I: Overview of Our Approach. Edit Prediction. We train model to learn distribution over possible graph edits. In this case, the correct edit corresponds to breaking the bond marked in red. Applying this edit produces two synthons. b. Synthon Completion. Another model is trained to pick candidate leaving groups (blue) for each synthon from discrete vocabulary, which are then attached to produce the final reactants.
Another important consideration in building retrosynthesis models is aligning model design with strategics adopted by expert chemists. These strategies are influenced by fundamental properties of chemical reactions, independent of complexity level: (i.) the product atoms are always subset of the reactant atoms', 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-bee Yan et al., 2020], that generate reactants from a product 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 precomputed vocabulary. This vocabulary is constructed during preprocessing by extracting subgraphs that differ between 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 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 manual correction.
methods [Shi al., 2020,
The benchmark dataset for evaluating retrosynthesis models USPTO-50k [Schneider et al., 2016), which consists of 50000 reactions across 10 reaction classes. The dataset contains 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 the position of the atom overestimate performance. We canonicalize the product SMILES and remap the existing dataset, thereby removing the shorteut. On this remapped dataset, GRAPHRETRO achieves top-1 accuracy of 53.7% when the reaction class is not known, outperforming both template- frre and semi-template-bart -ased 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-basee approaches.
Template-Bact Based:: Templates are either hand-crafted by experts [Hartenfeller al., 201 Szymkué 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
'ignoring impurities

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matching procedure. Template-baseer 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-Fres Free: Template-fre methods [Liu al., 2017, Zheng et al., 2019, Chen 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 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 -base models, with additional parameterizations and loss terms to enforce the duality between forward (reaction prediction) and backward (retrosynthesis) prediction.
Semi Weeeeeea---aseeeeee Our work is closely related to recently proposed semi-template-bue -baed methods [Shi al., 2020, Yan et al., 2020], which first identify synthons and then expand synthons into reactants through sequential generation using either graph generative model [Shi et al., 2020] or Transformer (Yan al., 2020]. To reduce the complexity of reactant generation, we instead complete synthons using subgraphs called leaving groups selected from precomputed vocabulary. This allows us to view synthon completion as a classification problem instead of a generative 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 4.8% and 3.3% performance improvement respectively over previous semi-template-bart baased methods.
Reaction Center Identification The reaction center covers 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 be in the reaction center [Coley 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 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 score only for existing bonds and atoms and reduce prediction complexity from O 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 al., 2020] which uses precomputed substructures building blocks for property-condittoned molecule generation. However, instead of precomputing, synthons aanalggous building blocks for reactants are indirectly learnt during training.
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 bonds or hydrogen counts on atoms. We first train neural network to predict score for possible edits (Section 3.1). The edit with the highest score is then applied the product 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.
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Retrosynthesis Prediction A retrosynthesis pair R is described by pair of molecular graphs (G,, G.). where Gp are the products and G, the reactants. molecular graph described as G (V. E) with atoms V as nodes and bonds & as edges. Prior work has focused on the single product case, while reactants can have multiple connected components, i.e. 9, {gr.)==1 Retrosynthesis pairs are atom-mapped so that each product atom has a unique corresponding reactant atom. The retrosynthesis task then, is to infer {Gr JC given Gp.
Edits Edits consist of (i.) atom pairs {(a; aj)} where the bond type changes from products to reactants, and (ii.) atoms {as} 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 their corresponding reactant counterparts.
Synthons and Leaving Groups Applying edits E to the product 9p 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 G3 and leaving groups by Gt. We further assume that synthons and leaving groups have the same number of connected components as the reactants, i.e G.- (G., )C and Gi = [Gt Jc- 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 g.. followed by predicting leaving group Gr, to attach to each synthon G..- The model is defined as
P(G.
Σ ((Gl,) E.G
where G. G. are deterministic given E, Gr, and Gp-
3.1 Edit Prediction
For given retrosynthesis pair = (Gp. Gr), we predict an edit score only for existing bonds and atoms, instead of every atom pair as in [Coley et al., 2019, Jin et al., 2017]. This choice is motivated by the low frequency (-0.1% 1%) of new bond formations in the training set examples. Coupled with the sparsity of molecular graphs, this reduces the prediction complexity from O(N2) to O(N) for product with N atoms. Our edit prediction model has variants tailored to single and multiple edit prediction. Since 95% of the training set consists of single edit examples, the remainder of this section describes the setup for single edit prediction. A detailed description of our multiple edit prediction model can be found in Appendix ??.
Each bond (u, v) in Gp is associated with a label Burk {0, 1} indicating whether its bond type k has changed from the products to reactants. Each atom is associated with a label yı. {0, 1} indicating a change in hydrogen count. We predict edit scores using representations that are learnt using a graph encoder.
Graph Encoder To obtain atom representations, we use a variant of the message passing network (MPN) deseribed in [Gilmer al., 2017]. Each atom has a feature vector x., indicating its atom type, degree and other properties. Each bond (u, v) has feature vector X.v indicating its aromaticity, bond type and ring membership. For simplicity, denote the encoding process by MPN(-) and describe architectural details in Appendix ??. The MPN computes atom representations (c.ul u E 9} via
{c = MPN(G, {x,}. (XwrTeeN((())
(2)
where (u) denotes the neighbors of atom The graph representation Cg is aggregation of atom
representations, i.c. Cg = Euev Cu- When has connected components { we get a set of graph representations {cg,), ). For bond (u, v). we define its representation Cue = (ABS(c.,, c,, ]] c., + C.).
where ABS denotes absolute difference and refers to concatenation. This ensures our representations are permutation invariant. These representations are then used to predict atom and bond edit scores using corresponding neural networks,

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Ua T (W W +
(3)
Suck uk =u T(WKCor bk),
(4)
where 7(-) is the ReLU activation function.
Updating Bond Edit Scores Unlike a typical classification problem where the labels are indepen- dent, edits can have possible dependencies between each other. For example, bonds part of stable system such as an aromatic ring have greater tendency to remain unchanged (label 0). We attempt leverage such dependencies to update initial edit scores. To this end, we build graph with bonds (u, v)) as nodes, and introduce an edge between bonds sharing an atom. We use another MPN(.) on this graph to learn aggregated neighborhood messages muss and update the edit scores Sank manner similar to how LSTMs update representations,
Juok a (W x + W. km m
(W kxx + Wi km m m Muuk Um (W xxx + W
buok o (
km m
mare
Suck fack Suvk + buck myek-
Training
We train by minimizing the cross-entropy loss over possible bond and atom edits
C. - Σ (9p, E) ((UD)),)EE
Σ
Vavklog(Swokk
Σ MEE
yalog(su)
The cross-entropy loss enforces the model to learn distribution possible edits instead of reasoning about each edit independently, as with the binary cross entropy loss used in [Jin et al., 2017, Coley et al., 2019].
3.2 Synthon Completion
Synthons are completed into valid reactants by adding specific functionalities called leaving groups. This involves two complementary tasks: (i.) selecting the appropriate leaving group, and (ii.) attaching the leaving group the synthon. As ground truth leaving groups not directly provided, extract the leaving groups and construct a vocabulary X' of unique leaving groups during preprocessing.
The vocabulary has a limited size (I.Y) 170 for standard dataset with 50. 000 examples, and 72000 synthons) indicating the redundancy of leaving groups used in accomplishing retrosynthetic transformations. This redundancy also allows us to formulate leaving group selection as a classi- fication problem over X, while retaining the ability to generate diverse reactants using different combinations of leaving groups.
Vocabulary Construction Before constructing the vocabulary, we align connected components of synthon and reactant graphs by comparing atom mapping overlaps. Using aligned pairs 9s, (V.,, Es.) and Gr, (Vr, Er.) as input, the leaving group vocabulary X' is constructed by extracting subgraphs Gr, (V,,, EL.) such that Vr, Vr. V... Atoms {a,} in the leaving groups that attach to synthons are marked with a special symbol. We also add three tokens to X' namely START, which indicates start of synthon completion, END, which indicates that there no leaving group to add and PAD, which is used to handle variable numbers of synthon components in minibatch.
Leaving Group Selection For synthon component S C. where C is the number of connected components in the synthon graph, we use three inputs for leaving group selection the product representation cg,, the synthon component representation cg., and the leaving group representation for the previous synthon component, er. 1. The product and synthon representations are learnt using the MPN(.). For each &; E X, representations can be learnt by either training independent embedding vectors (ind) or by treating each v; as a subgraph and using the MPN(-) (shared). In the shared setting, we use the same MPN(-) as the product and synthons.
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The leaving group probabilities are then computed by combining Cg,, Cg.g and Cle-1 via a single layer neural network and softmax function
Ø = softmax (UT (WICe, + W2c0. + ()ee(-)))
(10)
where @t. distribution learnt over X. Using the representation of the previous leaving group er. allows the model to understand combinations of leaving groups that generate the desired product from the reactants, We also include the product representation cg2 as the synthon graphs are derived from the product graph.
Training For step e, given the one hot encoding of the true leaving group qt we minimize the cross-entropy loss
C C. = Σ C(i 9.).
(II)
Training utilizes teacher-forcing [Williams and Zipser, 1989] so that the model makes predictions given correct histories. During inference, at every step, we use the representation of leaving group from the previous step with the highest predicted probability.
Leaving Group Attachment Attaching leaving groups to synthons a deterministic process and not learnt during training. The task involves identification of the type of bonds add between attaching atoms in the leaving group (marked during vocabulary construction), and the atom(s) participating in the edit. These bonds can be inferred by applying the valency constraint, which determines the maximum number of neighbors for each atom. The attachment process does not modify any stereochemistry. Given synthons and leaving groups, the attachment process has 100% accuracy. The detailed procedure is described in Appendix ??.
3.3 Inference
Inference is performed using beam search with a log-liielihood scoring function. For beam width n, we select n edits with highest scores and apply them to the product to obtain n synthons, where each synthon consist of multiple connected components. The synthons form the nodes for beam search. Each node maintains a cumulative score by aggregating the log-likelihoods of the edit and predicted leaving groups. Leaving group inference starts with connected component for each synthon, and selects n. leaving groups with highest logllikellhoods. From the n² possibilities, we select n nodes with the highest cumulative scores. This process is repeated until all nodes have leaving group predicted for each synthon component.
Evaluation
Evaluating retrosynthesis models is challenging as multiple sets of reactants can be generated from the same product. To deal with this, previous works [Coley et al., 2017b, Dai et al., 2019] evaluate the ability of the model recover retrosynthetic strategies recorded in the dataset.
Data We use the benchmark dataset USPTO-500 -0kk [Schneider et al., 2016] for all our experiments. The dataset contains 50, 000 atom-mapped reactions across 10 reaction classes. We use the same dataset version and splits as provided by [Dai et al., 2019]. The USPTO-50K dataset contains shorteut in that the product atom with atom-mapping is part of the edit -55% of the cases. If the product SMILES is not canonicalized, predictions utilizing operations that depend on the position of the atom bond will be able use the shortcut, and overestimate performance. We canonicalize the product SMILES, and reassign mom-mappings to the reactant atoms based the canonical ordering, which removes the shorteut. Details on the remapping procedure can be found in Appendix ??
We use the top-n accuracy (n = 1, 5, 10) as our evaluation metric, defined as the fraction of examples where recorded reactants are suggested by the model with rank S n. Follow- ing prior work [Coley et al., 2017b, Zheng et al., 2019, Dai et al., 2019], compute the accuracy
Evaluation

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Table 1: Top-n exact match accuracy. Best values within each section are highlighted in bold.
Top-n Accuracy
(%
Model
Reaction class known
Reaction class unknown
5
10
10
Template-Based
RETROSIM [Coley 2017b]
52.9
73.8
81.2
88.1
37.3
54.7
63.3
74.1
NEURALSYM [Segler and Waller, 2017]
55.3
76.0
81.4
85.1
44.4
72.4
78.9
GLN [Dai et al., 2019]
64.2
79.1
85.2
90.0
52.5
69.0
75.6
83.7
DUALTB [Sun et al., 2021]
67.7
84.8
88,9
92.0
55.2
74.6
80.5
86.9
Template-Free
SCROP IZheng et al., 2019]
59.0
74.8
78.1
81.1
43.7
60.0
65.2
68.7
LN-TRANSFORMER [Chen et al., 2019]
40.5
65.1
72.8
79.4
DEALTT [Sun et al., 2021]
65.7
81.9
84.7
85.9
53.6
70.7
74.6
77.0
Semi- Soni-Template-Beee
G2Gs [Shi et al., 2020]
61.0
81.3
86.0
88.7
48.9
67.6
72.5
75.5
RETROXPERT [Yan al., 2020]
62.1
75.8
78.5
80.9
50.4
61.1
62.3
63.4
GRAPHRETRO (ours)
63.9
81.5
85.2
88.1
53.7
68.3
72.2
75.5
by comparing the canonical SMILES of predicted reactants the ground truth. Atom-mappinn is excluded from this comparison, but stereochemistry. which describes the relative orientation of atoms in the molecule, is retained. The evaluation is carried out for two settings, with the reaction class being known or unknown.
Baselines For evaluating overall performance, we compare GRAPHRETRO to nine baselines four template-based, three template-free, and two semi-template-based methods. These include:
Template Bamplate-Based:: RETROSIM Coley et al. [2017b] ranks templates for given target molecule by computing molecular similarities to precedent reactions. NEURALSY: [Segler and Waller, 2017] trains a model to rank templates given a target molecule. GLN [Dai et al., 2019] models the joint distribution of templates and reactants in hierarchical fashion using logic variables. DUAL.B [Sun et al., 2021] uses an energy-based model formulation for retrosynthesis, with additional parameterizations and loss terms enforce the duality between forward (reaction prediction) and backward (retrosynthesis prediction). Inference is carried out using reactant candidates obtained the products.
by applying an extracted template
Template-Free SCROP [Zheng et al., 2019], LV- TRANSFOORRE [Chen et al., 2019] and DUALTF [Sun et al., 2021] use the Transformer architecture [Vaswani et al., 2017] to output reactant SMILES given a product SMILES. To improve the validity of their suggestions, SCROP in- clude second Transformer that functions latent variable mixture model to improve diversity of suggestions. DUALTF utilizes additional parameterizations and loss terms enforce the duality between forward (reaction prediction) and backward (retrosynthesis prediction).
syntax correcter. L.Y-TRANSFORMER uses
Semi-Template Basee:: G2Gs [Shi et al., 2020] and RETROXPERT [Yan al., 2020] first identify synthons, and then expand the synthons into reactants by either sequential generation of atoms and bonds (G2Gs), or using the Transformer architecture (RETROXPERT). The training dataset for the Transformer in (Yan et al., 2020] is augmented with incorrectly predicted synthons with the goal of learning correction mechanism.
Results for NEURALSYM are taken from [Dai et al., 2019]. The authors [Yan et al., 2020] report their performance being affected by the dataset leakage". Thus, the most recent results from their website on the canonicalized dataset. For remaining baselines, we directly use the values reported in their paper. For the synthon completion module, the ind configuration given its better empirical performance.

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4.1 Overall Performance
Reaction class unknown As shown in Table 1, when the reaction class is unknown, GRAPHRETRO outperforms G2Gs by 4.8% and and RETROXPERT by 3.3% in top-1 accuracy. Performance improvements are also seen for larger n. except for n = Barring DUALTB, the top-1 accuracy is also better than other template-free and template-based methods. For larger n,, one reason for lower top-n accuracies than most template-base of leaving group patterns. In contrast, model learns to discover these during training. A second hypothesis to this end is that simply adding log-likelihood scores from edit prediction and symthon completion models may be suboptimal and bias the beam search in the direction of the more dominating term. We leave to future work to investigate scoring functions that rank the attachment.
methods is that templates already contain combinations
Reaction class known When the reaction class is known, GRAPHRETRO outperforms G2Gs and RETROXPERT by a margin of 3% and 2% respectively in top-1 accuracy. GRAPURETRO also outperforms all the template-fie free methods in top-n accuracy. for GRAPHRETRO are also better than most template-bel based and template- free methods. When the reaction class is known, RETROSIM and GLN restrict template sets corresponding the reaction class, thus improving performance. The increased edit prediction performance (Section 4.2) for GRAPHRETRO helps outweigh this factor, achieving comparable better performance till 5.
4.2 Individual Module Performance
To gain more insight into the working of GRAPHRETRO, we evaluate the top-n accuracy (n 1, 2. 3, 5) of edit prediction and synthon completion modules, along with corresponding ablation studies, with results shown in Table 2.
Edit Prediction For the edit prediction module, we compare the true edit(s) to top-n edits predicted by the model. We also consider two ablation studies, one where we directly use the initial edit scores without updating them, and the other where we predict edits using atom-pairs instead of existing bonds and atoms. Both design choices lead to improvements in performance, as shown Table We hypothesize that the larger improvement compared to edit prediction using atom-pairs is due to the easier optimization procedure, with lesser imbalance between labels and
Synthon Completion For evaluating the synthon completion module, we first apply the true edits to obtain synthons, and compare the true leaving groups top-n leaving groups predicted by the model. We test the performance of both the ind and shared configurations. Both configurations perform similarly, and are able identify 97% (close to its upper bound of 99.7%) of the true leaving groups in its top-5 choices, when the reaction class is known. The top-1, and 5 accuracies of the synthon completion for unknown reaction classes for G2Gs are 61.1%, 81.5% and 86.7% respectively, while ours are 75.6%. 92.5% and 96.1%, indicating 10-14% performance improvement using a classification formulation over the generative one adopted by G2Gs.
Table 2: Performance Study of edit prediction and synthon completion modules
Top-n Accuracy (%)
Setting
Reaction class known
Reaction class unknown
2
3
5
1
2
5
Edit Prediction
84.6
92.2
93.7
94.5
70.8
85.1
89.5
92.7
without edit score updates
84.3
92.1
93.7
94.5
70.1
84.8
89.4
92.6
predicting on atom pairs
81.9
89.5
90.9
92.1
68.6
83.2
88.3
91.8
Synthon Completion (ind)
77.4
89.5
94.2
97.6
75.6
87.4
92.5
96.1
Synthon Completion (shared)
76.9
89.6
93.9
97.4
74.9
87.7
92.9
96.3

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4.3 Example Predictions
In Figure 2, we visualize the model predictions and the ground truth for three cases. Figure 2a shows an example where the model identifies both the edits and leaving groups correctly. In Figure 2b, the correct edit is identified but the predicted leaving groups are incorrect. We hypothesize this due to the fact that in the training set, leaving groups attaching to the carbonyl carbon (C=O) are small (e.g. -OH, 22 halides). The true leaving group in this example, however, large. The model is unable to reason about this and predicts the small leaving group -I. In Figure 2c, the model identifies the edit and consequently the leaving group incorrectly. This highlights a limitation of our model. If the edit is predicted incorrectly, the model cannot suggest the true precursors.
4.4 Limitations
The simplified and interpretable construction of GRAPHRETRO comes with certain limitations. First, the overall performance of the model is limited by the performance of the edit prediction step. If the predicted edit is incorrect, the reactants cannot be salvaged. This limitation is partly remedied by our model design, that allows for user intervention to correct the edit. Second, our method is reliant on atom-mapping for extracting edits and leaving groups. Extracting edits directly based on substructure matching currently suffer from false positives, and heuristics to correct for these result in correct edits in only ~90% of the cases. Third, our formulation assumes that we have as many synthons as reactants, which is violated in some reactions. We leave it to future work to extend the model realize single reactant from multiple synthons, and introduce more chemically meaningful edit correction mechanisms.
Product
True Renetants
Predicted Reactonts
Br
Br
Product
Predicted
HN
HN
HO
Product
Predicted
Figure 2: Example Predictions. The true edit and incorrect edit (if any) are highlighted in green and red respectively. The true and predicted leaving groups are highlighted in blue. a. Correctly predicted example by the model. b. Correctly predicted edit but incorrectly predicted leaving groups. e. Incorrectly predicted edit and leaving group.
Conclusion
Previous methods for single-step retrosynthesis either restrict prediction to template set, are insensitive to molecular graph structure or generate molecules from scratch. We address these shortcomings by introducing graph-based semi-template-basee model inspired by chemist's

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workflow, enhancing the interpretability of retrosynthesis models. Given a target molecule, we first identify synthetic building blocks (synthons) which are then realized into valid reactants, thus avoiding molecule generation from scratch. Our model outperforms previous semi-template methoos by significant margins on the benchmark dataset. Future work aims to extend the model realize single reactant from multiple synthons, and introduce more chemically meaningful components to improve the synergy between such tools for retrosynthesis prediction and a practitioner's expertise.
Acknowledgements
This research was supported by the Machine Learning for Pharmaceutical Discovery and Synthesis Consortium at MIT. V.R.S. was also supported by the Zeno Karl Schindler Foundation. C.B. was supported by the Swiss National Science Foundation under the National Center of Competence in Research (NCCR) Catalysis under grant agreement SINF40 180544. We thank the Leonhard scientific computing cluster at ETH Zürich for providing computational resources.
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Reviews:
[{"review_id": "uovxc85yqpv", "reviewer": "Reviewer_7Zmw", "summary": "The paper proposes a retrosynthesis model which first predicts the set of graph edits transforming the target into incomplete molecules called synthons, then the model learns to expand synthons into complete molecules by attaching relevant leaving groups. ", "questions": "", "limitations": "", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The paper is overall well written and easy to follow. \n\nPoint of criticism about this paper.\n\n(1) Synthon completion boils down retrosynthesis (a generative problem) to a much simpler classification problem (classify leaving groups).\n\nFirst, it is possible that the leaving groups of the unseen test set is not included in the predefined leaving groups. i.e. not a generative model.\n\nSecond, (Line 163) 50M samples are used to extract leaving group candidates. The testing examples are included in 50M, which is not allowed. \n\nThird, it is not fair to compare a classification problem with generative models (other methods)\n\n(2) The ideas of \"identifying reaction center and then complete the synthons\" is largely the same with previous work, such as G2G, retroXert\n\n(3) The performance is not superior compared with Dual model etc. \n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "yes", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2 hours"}, "extra_text": {}}, {"review_id": "ZndLz8qXzpm", "reviewer": "Reviewer_Ep3z", "summary": "## Summary. \nThe 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.\n", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "## General comments: \nThe proposed method is reasonably novel. It appears technically sound but we found a range \nof statements that are not convincing as detailed below.  Overall the submission is clearly written. \nIn the current form, the significance is strongly limited due to flaws in the method comparison and evaluation.\n\n## Reproducibility\nCode is provided as Supplementary Material which makes the work sufficiently reproducible.\n\n## Originality\nThe proposed method is reasonably novel within the scope of ML methods for chemical \nsynthesis. However, it is unclear how the broader ML community profits from the \nmethodology in this work. \n\n## Clarity\nThe paper is clearly structured and written. Notation is appropriate and \nfollows standards in ML. Figures and tables are well designed. \nFigure 1 could be a bit more informative by displaying architectural\ncomponents, such as the encoder.\n\n## Quality\nThe quality of this work is limited due to several incorrect claims, \nmissing analyses and unclear formulations (see \"Detailed comments\" below). \nFurthermore, the method comparison leaves out many methods that \nshould actually be compared. \n\n## Significance\nIt is unclear whether the proposed method represents and advance at all.\nDue to missing error bars and statistical tests between methods, the \nperformance values could just arise by chance (see \"Detailed comments\" below). \n\n\n### Detailed comments:\n- L9f: “This decomposition simplifies the architecture, making its predictions more interpretable, and also amenable to manual correction.” - It is not clear which architecture is simplified. The method is more interpretable compared to what? How is interpretability measured in the first place? Given any method that predicts precursors from a product one could run an atom-mapping algorithm and also find where the edit happened making it interpretable as well. Any method is amenable to manual correction. Has it been demonstrated that manual correction is easier for this method.\n  \n- L25ff: “Despite their interpretability, these methods fail to generalize to new reactions.” - If generalization means non-trivial performance on a test set this statement is factually wrong. In fact Table 1 shows template-based methods outperform other ones.  \n  \n- L26f: “Template-free methods bypass templates by learning a direct mapping from the SMILES [24] representations of the product to reactants.” - Template-free methods need not use the SMILES representation of the molecules.\n  \n- L27: “Despite their greater generalization potential,..” - It’s unclear how generalization potential is measured or any argument provided why template-free methods have a greater one. For example reaction-templates might be sufficiently flexible to model chemical reactions which would render the mentioned statement false. The argument should either be dropped, extended or a reference should be provided.\n- L38: Typo. Double period\n  \n- L44: “This formulation also simplifies our architecture” - Same as in the abstract. Compared to what is the architecture simplified.\n  \n- L45: “predictions more transparent, interpretable and amenable to manual correction” - I think that these claims are unsupported or not elaborated on enough. Also consider the comment regarding L9f here. What does transparent mean in this context?\n  \n- L46: “The benchmark dataset for evaluating retrosynthesis models is USPTO-50k, ..” - USPTO-50k is not the definitive single benchmark dataset for this task. Maybe add “A commonly used/An established benchmark”.\n  \n- L47ff: “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.” - I think this should be formulated differently. Since the dataset contains the test outputs there obviously is a shortcut to predicting the right reactants. We think a better formulation woud be to state that there is an unexpected way of test set leakage. Minor note on grammar: The predictions do not overestimate performance but lead to overestimated performance.\n  \n- L51f: “.., GRAPH RETRO achieves a top-1 accuracy of 53.7% when the reaction class is not known, outperforming both template-free and semi-template-based methods.” - Why are template-based methods not mentioned at this point? Tetko et al. [1] report an almost identical performance 53.5% with a template-free method and DualTF in Table 1 has almost identical performance and is better at top-n acc. with higher values of n. It makes the impression that the metrics reported here are cherry picked and it’s not clear why a whole top-performing model category is not mentioned here. \n  \n- L56f: “Templates are [...] or extracted algorithmically from large databases” - Templates can also be extracted from single reactions or small databases. We think that a better wording would be “extracted from atom-mapped reactions”.\n  \n- L57f: “Exhaustively applying large template sets is expensive due to the involved subgraph matching procedure.” - This argument is not uncommon in retrosynthesis papers, but usually no primary source is provided that actually compares this to alternative approaches. [2] actually seems to suggest that template-based methods might be faster than Transformers or a method based on GNNs (GLN). We think that data should be presented that speed issues are actually a problem for template-based methods and not for other approaches.\n  \n- L62: “Despite their interpretability, these methods fail to generalize outside their rule set.” - Of course they fail to generalize out of their rule set but if the rule set sufficiently covers the test set, which is attested by superior performance (Table 1) this should not matter. The proposed method could also be said to fail outside the leaving group vocabulary, but it is not a problem because these groups have good coverage. \n  \n- L65: “Linearizing molecules as strings does not utilize the inherently rich chemical structure.” - How can SMILES strings encode a chemical structure without the chemical structure?\n\n- L108: “Retrosynthesis pairs are atom-mapped” - They are not necessarily mapped.\n  \n- L149ff: “Unlike a typical classification problem where the labels are independent, edits can have possible dependencies between each other. For example, bonds part of a stable system such as an aromatic ring have a greater tendency to remain unchanged (label 0). We attempt to leverage such dependencies to update initial edit scores.” - I don’t really understand this sentence. Could you maybe elaborate?\n  \n- L209f: “Evaluating retrosynthesis models is challenging as multiple sets of reactants can be generated from the same product.” - If one is strict I think this should be reformulated as “as a product can be synthesized from different reactant sets”. “no unique solution”, “one-to-many problem” may also be useful phrases.\n  \n- L243: There are more papers that propose methods on this task, see e.g. [2]. These methods should be included in the comparison or there should be a discussion of why they are excluded.\nError bars should be provided in Table 1. If significant improvement cannot be established we think it would be better to tone down “Barring DUALTB, the top-1 accuracy is also better than other template-free and template-based methods. (L252f)” - to only stating comparable performance.\n\n- L253ff: “For larger n, one reason for lower top-n accuracies than most template-based  methods is that templates already contain combinations of leaving group patterns.” - I don’t understand this argument and I think that this is not backed up by data. Could you elaborate?\n  \n- L260: “GRAPH RETRO also outperforms all the template-free methods in top-n accuracy.” - This is not true as DUALTF outperforms GraphRetro for n=1,3 if the reaction class is known.\n  \n- L261: “for GRAPH RETRO are also better than most template-based and template-free methods.” - Most other methods are better than GraphRetro on top-10 accuracy. I also don’t think that “most” is really relevant here as one can always add more models that perform worse. \n\n- L294: “This limitation is partly remedied by our model design, that allows for user intervention to correct the edit.“ - It is not shown why this is easier for this model than for others. If a user intervenes she could also solve the whole task which might be just as hard.\n\n- L302ff: “Previous methods for single-step retrosynthesis either restrict prediction to a template set, are insensitive to molecular graph structure or generate molecules from scratch. We address these shortcomings [...]” - I don’t agree that these things are necessarily shortcomings. A restricted template set is not a problem it if has good coverage of the test set. Coverage is at least 92% in this setting, so I don’t think it’s the limiting factor. I think no algorithm better than random guessing can be insensitive to graph structure. SMILES based models also are sensitive to the graph structure via the linear representation of the graph. It is also not necessarily a shortcoming to construct molecules from scratch. It surely may use up more computational ressources or suffer from worse performance as a result, but the data doesn’t back this up.\n\n- L307f “Our model outperforms previous semi-template-methods by significant margins on the benchmark dataset.” - This statement is too general as performance is not better than that of previous methods for all values of n. \n\nReferences:\n[1] Tetko, I. V., Karpov, P., Van Deursen, R., & Godin, G. (2020). State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nature communications, 11(1), 1-11.\n[2] Seidl, P., Renz, P., Dyubankova, N., Neves, P., Verhoeven, J., Wegner, J. K., ... & Klambauer, G. (2021). Modern Hopfield Networks for Few-and Zero-Shot Reaction Prediction. arXiv preprint arXiv:2104.03279.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Limitations are addressed. \nSocietal impacts are absent. ", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "6"}, "extra_text": {}}, {"review_id": "D8tMAzXMsDK", "reviewer": "Reviewer_1dhj", "summary": "\nThis paper presents GraphRetro, a single-step retrosynthesis model that represents an advance from last years' NeurIPS state of the art model on semi-template-based modeling of the USPTO-50k dataset. GraphRetro uses a graph neural network that predicts edits to transform a target into synthons and then expands the synthons into full molecules by attaching leaving groups (with the synthons and leaving groups learned from the training set). The authors compare the predictions on the USPTO-50k to existing baselines, and show examples of predictions, including wrong ones. Because of the particular construction of the model, the authors can decompose the sources of errors as wrong edit predictions or wrong synthon completions.\n", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The paper represents an improvement on the semi-template based methods for retrosynthesis. The presentation is clear and the model presents significant additions compared to RetroXpert.\n\nAlthough I like the organizational power of classifying retrosynthesis models as template-based, template-free, and semi-template-based, I think that as long as no data leakage happens and the templates are only generated using the training sets of a particular benchmark, then all these methods could be compared against each other in a particular benchmark (and yes, I know that we sorely need additional public benchmarks in this area). In that light, I think that this paper shows that the energy-based views of retrosynthesis outperform GraphRetro by a respectable margin in most categories, both in the template based, but importantly, also in most of the template-free methods. I understand the fundamental hesitation with including template-based methods in the comparison because they might not generalize to new reactions, but wouldn't a template-free method outperforming a semi-template-based model suggest that the former is much better yet for generalizing to unseen domains? Despite this important limitation of the work, I think that the contribution is still notable, and I hope that the authors will manage to amend this paper and publish it soon.\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "\nBecause of the analysis on the errors, the authors do a good job at documenting certain limitations of this model. An important limitation of the paper, however, is the lack of a discussion of the results of the dualTB and dualTF models.\n", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "8"}, "extra_text": {}}]

Author rebuttal:

Meta-review: While the reviewers appreciated the clear presentation they ultimately felt that the authors did not make a strong enough case for why their method should be preferred to template-based and template-free methods. The main argument for rejection is this: it's reasonable to compare all methods against each other, and if we do so this work underperforms. The authors offered a few responses: (i) They pointed to different evaluation metrics for these classes of models, but this seems to be an argument in favor of either template-based methods (as they are more constrained by chemical knowledge) or template-free methods (as they train a single model end-to-end); (ii) They argue that their model is more interpretable which makes it worth underperforming template-free methods. This argument is undercut by the fact that template-based methods are arguably as interpretable and outperform their work; (iii) They argue that their model is able to leverage the graphical structure of molecules. While this is true it doesn't seem like it was worth it, the work needed to sacrifice performance compared to all other work (even other semi-template based work). My recommendation is to either adapt this work to be template-free by extending duality enforcement for graph structures, adapt it to go fully template-based, or submit this to a more specialized venue as recommended by Reviewer 1dhj.
Decision: Accept (Poster)