Metro: Memory-Enhanced Transformer for Retrosynthetic Planning via Reaction Tree
Authors: Songtao Liu
Zhitao Ying
Zuobai Zhang
Peilin Zhao
Jian Tang
Lu Lin
Dinghao Wu
Venue: Submitted to ICLR 2023 (2023)
Keywords: Retrosynthetic Planning
Transformer
Memory Network
Reaction Database
Reaction tree

Abstract: Retrosynthetic planning plays a critical role in drug discovery and organic chemistry. Starting from a target molecule as the root node, it aims to find a complete reaction tree subject to the constraint that all leaf nodes belong to a set of starting materials. The multi-step reactions are crucial because they determine the flow chart in the production of the Organic Chemical Industry. However, existing datasets lack curation of tree-structured multi-step reactions and fail to provide such reaction trees, limiting models' understanding of organic molecule transformations. In this work, we first develop a benchmark curated for the retrosynthetic planning task, which consists of 124,869 reaction trees retrieved from the public USPTO-full dataset. On top of that, we propose Metro: Memory-Enhanced Transformer for RetrOsynthetic planning. Specifically, the dependency among molecules in the reaction tree is captured as context information for multi-step retrosynthesis predictions through transformers with a memory module. Extensive experiments show that Metro dramatically outperforms existing single-step retrosynthesis models by at least 10.7% in top-1 accuracy. The experiments demonstrate the superiority of exploiting context information in the retrosynthetic planning task. Moreover, the proposed model can be directly used for synthetic accessibility analysis, as it is trained on reaction trees with the shortest depths. Our work is the first step towards a brand new formulation for retrosynthetic planning in the aspects of data construction, model design, and evaluation.

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METRO: MEMORY-ENHANCED TRANSFORMER FOR RETROSYNTHETIC PLANNING VIA REACTION TREE
Anonymous authors
Paper under double-bling review
ABSTRACT
Retrosynthetic planning plays critical role in drug discovery and organic chem- istry. Starting from target molecule the root node, aims find complete reaction tree subject to the constraint that all leaf nodes belong to a set of starting materials. The multi-step reactions are crucial because they determine the flow chart in the production of the Organic Chemical Industry. However, existing datasets lack curation of tree-structured multi-step reactions, and fail to provide such reaction trees, limiting models' understanding of organic molecule transformations. In this work, we first develop a benchmark curated for the retrosynthetic planning task, which consists of 1 124,869 reaction trees retrieved from the public USPTO-full dataset. On top of that, we propose Metro: Memory-Enhanced Transformer for RetrOsynthetic planning. Specifically, the dependency among molecules in the reaction tree is captured as context information for multi-step retrosynthesis predic- tions through transformers with a memory module. Extensive experiments show that Metro dramatically outperforms existing single-step retrosynthesis models by at least 10.7% in top-1 accuracy. The experiments demonstrate the superiority of exploiting context information in the retrosynthetic planning task. Moreover, the proposed model can be directly used for synthetic accessibility analysis, as it is trained reaction trees with the shortest depths. Our work is the first step towards brand new formulation for retrosynthetic planning in the aspects data construction, model design, and evaluation.
INTRODUCTION
Retrosynthetic planning is fundamental problem in organic chemistry (Coley et al. 2018a Genheden ctt al. 2020). The goal of retrosynthetic planning is to find a series of starting molecules that go through sequence of reactions, which can also be represented as reaction tree, to synthesize the target molecule. Retrosynthetic planning can be decomposed into multi-step retrosynthesis reactions through which we find all starting molecules that meet the requirements. The multi-step reactions outline the transformation direction of organic molecules and the transformation target. In the production of the Organic Chemical Industry, it requires us design efficient organic synthesis routes to synthesize our desired target products at low cost. Therefore, given a target molecule, predicting reasonable and efficient reaction routes synthesize this molecule is very crucial problem in both machine learning and organic chemistry (Segler et al. 2018).
To tackle this problem, past works, including MCTS SSeller et al. 2018). DFPN-E (Kishimoto et al. 2019), Retro (Chen Retroo((Chen et al. 2020), self-improve retrosynthetic planning (Kim et al.) [021]_ RetroGraph (Xie 20.22) and Grasp (Yu et al. 2022). model the retrosynthetic planning as search problem (Xie et 2022). Specifically, they first utilize reactions to train a template-basee MI.P retrosynthesis model (Segler et al.] 2017) and then learn a search algorithm to perform backward search to transform the molecules through retrosynthesis predictions until all the reactants are starting materials (Chen et al. 2020). The current benchmark for test evaluation of retrosynthetic planning models consists of 189 test routes (Chen al. 2020).
These approaches have the following limitations: 1) the training dataset of single-step reactions limits the understanding of the transformation of organic molecules sequence of chaining chemical reactions. 2) past works use single-step retrosynthesis models, which neglect the context information in the reaction tree. 3) the test set is too small comprehensively evaluate the performance. 4) the

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evaluation unit of existing benchmark is the reaction route which is one path from the root node to the leaf node in the reaction tree.
In this work, we address these limitations by first constructing new benchmark with 124,869 reaction trees retrieved from the public USPTO dataset and leverage the retrosynthesis transformer with additional memory module to capture reaction tree information for retrosynthetic planning.
Benchmark. SCScore (Coley et al. 2018b) concludes that the number of steps required to synthesize a molecule is an accurate metric for estimating molecule synthetic accessibility. Based on this observation and inspired by the prediction of synthesis accessibility with reaction knowledge graph (Li & Chen 2022). we construct a reaction graph from the existing reactions in the database. On the reaction graph. directed edges represent retrosynthesis reactions where the starting point denotes the product molecule and the ending point represents the reactant molecule to synthesize this product. Given a target molecule, we can search the shortest routes form an efficient reaction tree from the reaction graph, while the ending points of these routes are the starting molecules that satisfy the requirements. By constructing the reaction trees for target molecules, we can obtain a new benchmark for our retrosynthetic planning task.
Metro. In this work, propose Metro: Memory-Enhanced Enhanced Transformer for RetrOsyythetic planning by extending Transformer with an additional memory module. Our proposed Metro can capture the dependency among the molecules the reaction route as context information. By taking the context information into consideration, we can control the search within a reasonable reaction space specified for the reaction route. Extensive experimental results on retrosynthetic planning show that Metro achieves up to 13.2%, 14.5%, 1111%, 10.5%, and 10.0% over transformer on top-1, top-2, 1op-3, top-4, and top-5 accuracy, which demonstrates the superiority of exploiting context information for retrosynthetic planning task.
2 PRELIMINARIES
In this section, we formally define important terminologies used the rest of the paper, including SMILES representation, starting material, and reaction tree.
SMILES Representation. The simplified molecule-ing imput line-entry system (SMILES) (Weininger 1988) is a chemical specification for describing the structure of chemical compounds using strings Organic compounds can be denoted by SMILE representations like in Fig, D which is well suited for machine learning models to process. We denote the SMILES representation of molecule x (I), where s(x;; is the character at the i-th position of the string s(x). Given reaction
+ 72 + +ra + p, the SMILES representation of this reaction is as follows:
s(r1).s(22).. s(rw) + s(p), where multiple reactants are concatenated by "." in the SMILES representation.
Starting Material. We denote the space of all chemical molecules as M. The starting materials are
special set of molecules, denoted as S S .. AiZynthFinder (Genheden et al.] 2020) defines the starting material
commercially purchasable compound. ZINC (Sterling Irwin (015)) releases the open source databases of purchasable compounds. We define this list of compounds in these databases as our starting materials.
Reaction Tree and Reaction Routes. Given the above definitions, we can denote a reaction tree (Shibukawa et al., 2020 Nguyen & Tsuda 2021) T [T, R.I. where T e M \ S is the product molecule we desire to synthesize (A in Fig. T. R (ri r....., rn
C S is the set of starting materials (E, F, G. H in Fig. [ that go through series of reactions to synthesize A, and I = {m,, ,,,...,, } C M' \ S is the set of intermediate products (B, C, D in Fig. I where intermediate products are formed from reactants or intermediate products and then react further to give the final product or produce intermediate products. A reaction tree consists of multiple reaction routes. A reaction route is a path from the target molecule According to the definition, the number of reaction routes is equal to the number of starting materials. We denote reaction route the set of reaction routes as (41, 12, .e......yy ). and we have
starting material in the reaction tree.
T T4 UTL, U.. U TC,
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F
D-G@Netee(C--DNNtaaeeaa beceetheejeletthhhcCllttt ercCCeJeeeccHeee
NelectCr-ONacele
O-Ciclea(X-]]-OOO-
E O+C[Chelce@[Ve1-o0)
DecINiCCoereeecceeeeel [0-]eed
Figure I: Reaction Given the definition in Eq. B), the depth of this tree which means the depth of the longest reaction route is 3. A is the desired product molecule to be synthesized. B, C, and D are the intermediate product molecules. E, G, and H are the starting molecules.
where is the set of reactions accompanying this reaction route 4. As illustrated in Fig. | A->B- >D->E one the reaction routes in this tree. We denote the depth Dy of a reaction as the length of the longest reaction route in this tree, where
Dj max Dr:-
The depth of a reaction tree is also the number of steps required to synthesize molecule from fixed set of commercially purchasable compounds. Note that this paper, the default order the reaction route is the retrosynthesis order.
3 PROBLEM FORMULATION OF RETROSYNTHETIC PLANNING
In this section, we formally give the problem formulation and the goal of retrosynthetic planning.
Single-Step Retrosynthesis. Given target product molecule T M, the goal of retrosynthesis to predict set of reactants R fri, .2,, .M that can react to synthesize this product, which can be formulated as:
T
Retrosynthetic Planning, Given a target molecule E MM, the goal retrosynthetic planning is to search for the starting materials R {r1,72,... T,........ ..... r'J J S that synthesize the target molecule through a set of chemical reactions т = ={ [R, R2,... Ry J. which can be formulated as follows:
T I R,
where I S M \ is the set of intermediate product molecules.
The Goal of Retrosynthetic Planning. Our goal of retrosynthetic planning is to find the reaction tree with minimum depth to synthesize the target molecule. The reaction routes for retrosynthetic planning we construct follow the principle of finding starting materials as early as possible. This principle guides the transformation direction of the molecules, thus enabling machine learning models to make predictions on the best retrosynthesis direction. Moreover, what needs to declared that molecule synthesis accessibility is part of our work due to the construction of the dataset. By predicting the reaction tree with minimum depth, we can also make a prediction of the number of steps needed to synthesize target molecule.
NEW BENCHMARK BASED ON REACTION TREES
In this section, we describe the details of how to construct the new benchmark. Current bench- mark (Chen et al.] 2020) has the two limitations: 1) the test size is too small to evaluate the performance of models, 2) the dataset is based on reaction route instead of reaction tree. Therefore, we build a new benchmark based on reaction trees. The benchmark construction consists of three steps: reaction graph construction, reaction tree traversal, and dataset split.
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Figure 2: Model overview. Our model consists of three modules: encoder, memory module, and decoder. The three styles of - mean the three different retrosynthesis reactions on the reaction route.
Reaction Graph Construction. We extract all the reactions from USPTO-all dataset (Lowe 2012)
and build directed reaction graph. This is partially inspired by the construction of the reaction knowledge graph (Li & Chen 2022), which is used identify the minimum steps needed to synthesize molecules for synthesis accessibility analysis. Let G {V, E} denote the directed graph, where V is set of nodes {v1, V/2- ... UN} with [ = N and E is the set of edges. On this graph G, v the molecule that exists in the reaction database and (u,, 12 (2; - vy) is the directed edge which means v; and exists in the same retrosynthesis reaction where v is the product molecule and "j is one of the reactants. One molecule be a product reactant the graph.
Reaction Tree Traversal. We perform reaction tree traversal after reaction graph construction. We treat the nodes whose in-degree is O as our desired product molecules, which can be represented as Vp. Given one node v E Vpp. we perform dynamic programming and backtracking to find all the reaction trees (T TT(2) T where the leaf nodes are starting materials for v. For two trees which have different retrosynthesis reactions for the same product, we think these are different trees.
Dataset Split. We first build the reaction graph based on all the reactions in the database, in order to identify reaction trees with smaller depth (Chen et al.) (020). We then split the reaction trees into vilninn/validationn/ess datasets. This procedure aligns with the scenario in real-world organic chemical production, where have access to a huge database of reactions and we can find the reaction tree with the shortest depth with our dataset construction method. Although the reaction tree of the training and test datasets will have overlapping reactions, we only select the target molecule that does not exist in the training dataset in the testing phase and do not provide any information of this target molecule avoid information leakage. Moreover, many organic
about the reaction synthesis routes share basic chemical reactions. Therefore, it is reasonable that the reaction trees in the training and test datasets have overlapping reactions. Note that there may be several reactions to synthesize a target molecule, leading to multiple reaction trees. We extract the reaction trees with the minimum depth as our dataset according to the synthetic accessibility criteria (Li & Chen 2022).
5 METRO
In this section, we describe the details of our proposed Metro. Our model based on single-step retrosynthesis transformer (Vaswani et al. 2017 Karpov al. 2019). We further propose memory module that can capture the dependencies between molecules on the retrosynthesis route as context information. We don't rely a template to formulate the reaction pattern labeling number (Lin et al.) 2022) to extract the reaction center. The training and inference can be done in an end-to-end manner. Fig. outlines the sketch of our model.
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5.1 OVERVIEW OF METRO
5.1.1 ARCHITECTURE
As shown in Fig 2 our model consists of three components: encoder, memory module, and decoder. The input for the encoder is the SMILES representations of the product molecules (A, B, D) on the same reaction route. The SMILES representations of molecules have been processed. We add the start symbol A and the end symbol $ for the input. We also put the output in the decoder. But there is difference between the output SMILES fed into the decoder and the output SMILES predicted by the decoder. We add the start symbol A (right shift) on the former output SMILES and the end symbol $ (left shift) on the later output SMILES. So there is not any leakage problem with the supervised information. The encoder transforms the embedding matrices of product molecules into latent representations (matrices), which is also called the encoder output. We take the encoder output as input to obtain the context information (memory oulput) through the memory module, After that, concatenate the encoder output and memory output and feed into the decoder.
5.1.2 SEQUENCE GENERATION
Metro models the prediction of reaction route as sequence of molecule generation. Given the SMILES representations of (A, B, D) from the same reaction route as input, the output of our model the SMILES representations of (B+C, D, E+F). When we predict B+C, the input A and we will not consider the information of (B, D). When we complete the prediction of (B, C), we add B to the reaction route and use (A, as input to predict D. At the same time, we also add reaction route as input to predict (G. H) as shown in Fig. | We repeat this step until all reactants are starting materials, So during the training stage, we do not consider the information of later molecules when making the current prediction. The attention mechanism ensures that our predictions of all reactions along the same route are parallelized at the training stage. This can be implemented by masking the input of later molecules.
another
5.2 MEMORY MODULE
In this section, we will describe the details of the memory module. We employ attention mech- anism (Vaswani al. 2017) and memory network (Weston al. 2015 Sukhbaatar et al. 2015 Ramsauer et al., 2020) to build our memory layer to capture the context information on the reaction route. We formally deseribe the blocking components of one memory layer as follows. Our memory module takes series of encoder output Xx, .X.............................. X,, as input, where is the depth of the reaction route. Each of X, E Rexxd is the latent embedding matrix corresponding to the i-th molecule of the encoder input (A, B, and D in our illustrated case). We first use the Linear Projection of Flattened Patches employed in ViT (Dosovitskiy et al., 2020) to transform matrices X X2, X,, into vectors U1 U2 ....... Un follows:
02 X.WP
(5)
where W. E (I-dd_) and / is the length of SMILES. We then use es compute the query and key and use X, to compute the value as follows:
Q = [U1, U2 "n
K [U1, U2
2nl
I TW
(6)
V [X1 ... ..," '','" V
where Wº E Idxdd is the query matrix, W E Rdxds the key matrix, and WV e Iddd, the value matrix. Then we compute the memory embedding matrices as follows:
QKT
Attention(Q,KK) softmax
V
VTL
The output Attention(Q, K, V) is computed as weighted sum of the values V, where the weight assigned to each value is the attention score between two molecules.
Retrosynthetic-Planniig Architecture Design. The difference between our attention mechanism in our memory layer with that of standard transformers is that compute the attention scores of
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the two embedding matrices instead of vectors due the embedding matrix representation of the molecules. We also utilize multi-head attention to jointly compute the context information from different subspaces and then (FFN) to get the memory output M1, M2.... M,, We treat the memory output [M, M2 ... MM,'" context information. M; captures the dependencies between X, and X1, X2,-.- X,-1. After getting the embedding matrices of context information, we concatenate them with the encoder output feed the decoder.
residual connection (He et al. 2016) and fully connected layer
5.3 WHY CONTEXT INFORMATION
The intuition of exploiting the reaction tree (context information) instead of single-step prediction is that we can better prune the reaction search space. The single-step retrosynthesis reaction model searches the entire chemical reaction space of molecule, but some candidates, although valid, do not align with the synthesis goal of the current synthetic route and should be discarded when taking the entire reaction tree into account. Experimental results show that our proposed Metro improves by a large margin over Transformer.
5.4 INFERENCE
In the inference stage, start from the target molecule T, and perform backward chaining to do series of one-step retrosynthesis predic- tions until the reactants are all starting materials. This backward method is also adopted by Chen et al. (2020). After getting the predicted reac- tant molecules for the retrosynthesis reaction at each step, we refer to the set of starting mate-
Algorithm 1 Inference of the reaction tree given target molecule
1: Input: Target molecule T, starting material Set
2: Initialize reactant set R = 0) reaction route
set C)
3: Put the initial route [2'] into L
rials check whether the reactant molecules
4: while C is not empty set do
are starting materials. If they are starting materi- als, we add them into the prediction reactant set. Otherwise, we get new reaction route and pre- dict the next step's ' output. Once we obtain the predicted reactant set, compare it with the ground truth reactant set and get the inference accuracy. The inference process is outlined in the Algorithm|] Note that we perform a Depth First Search (DFS) for all models in paper.
5: Take an route from C
6: Predict the reactants r given
7:
for reactant r/ in do
(8) S then
8:
ri
Put (6) into R
9:
10:
else
Generate rote 1' = + [r[0]
11:
12:
Put into L
return predicted reactant set R
EXPERIMENTS
In this section, we evaluate the performance of different base models on our proposed benchmark for the retrosynthetic planning task.
6.1 EXPERIMENT SETUP
Dataset. We utilize the public dataset USPTO-full to construct the benchmark for the retrosynthetic planning task. The USPTO-full dataset consists of 1,808,937 reactions. After removing invalid and duplicate reactions, we obtain 906,164 reactions. Based on these reactions, we construct reaction graph. We treat the molecules whose out-degree is as our desired target molecules. And we use dynamic programming and backtracking to find all the reaction trees for each target molecule. There are 124,869 molecules for which we can find the reaction trees where the leaf nodes are all starting materials. We extract molecules with shortest depths greater than 1 and split these molecules into training, validation, and test datasets. For those molecules with shortest depths between and 10, they are randomly split into traning/validatiion/ess datasets following For those molecules with shortest depths larger than 10, we put them into the test dataset to evaluate the performance of models the molecules which need too many steps synthesize. The number
proportions.

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Table Top-k exact match accuracy.
Top-k & accuracy %
Methods
Template Vemplate-bused
RETROSIM (Coley 2017)
23.2
27.2
28.9
30.0
30.5
NEURALSYM (Segler Waller 2017
26.8
32.2
34.1
35.2
35.8
GLN (Dai et 2019
25.9
32.7
35.0
36.5
37.2
Semi templat--asedd
G2Gs (Shi al. 2020
4.0
6.1
7.2
8.2
8.8
GRAPHRETRR Sommath et al.
2021
14.4
Template-free
TRANSFORMER (Karpov et al.
2019
24.3
33.1
37.2
39.7
41.2
MEGAN (Sacha et 2021
20.1
29.5
34.9
38.3
40.4
METRO (Ours)
37.5
47.6
48.3
50.2
51.2
of samples in training/validationneee datasets are 46,458, 5,803, and 5,838 respectively. The dataset statistics can be found in Appendix A
Evaluation Protocol. Inspired by the evaluation metric of single-step retrosynthesis reaction, we decide whether a prediction is accurate by comparing our predicted starting material set with the ground truth starting material set is an exact match. Note that for specific target molecule, there may be multiple reaction trees in the test set. material set hit one of the multiple ground truths. Besides, perform a pruning search, when the length of the reaction route predicted by the model exceeds the depth of the ground truth reaction tree, we stop the search. By using our evaluation metric, can obtain the test accuracy of the our model and the baselines, laying benchmark for future works and enabling intuitive comparison.
an accurate match when the predicted starting
Baselines. We focus on the evaluation of base models on the retrosynthetic planning task, and adopt the DFS algorithm to perform the search demonstrate the immediate gain of our model. One future work is to combine and compare with other retrosynthetic planning search algorithms. We evaluate our proposed method against existing single-step retrosynthesis models, which can be classified into three categories: template- based, template-fret frree and semi memiittemlate---e Based.. All single-step methods are trained on the single-step retrosynthesis reactions independently the training dataset. When training is completed, we perform DFS to search the reactants in Algorithm [ Note that the single- step models only rely on the last product molecule on the reaction route to make the prediction. For the baselines, we follow their experimental setup such as hyper-paramend parameter and data processing in their paper and conduct the experiments with their released codes except for TR MFA
Template-Basedd Based: RETROSIM (Coley al. 2017) computes similarity between the fingerprints of product and reactants in the training dataset to rank the templates for a given target molecule. NEURALSYM (Segler& & Waller 2017) trains MLP to model the template selection as classification problem. GLN (Dai et al., 20199 exploits conditional graphical model to build the connection between probabilistic models and reaction templates.
Template-Free TRANSFORMER (Karpov et al., 2019) models the retrosynthesis prediction sequence-to-sequee problem based on Transformer (Vaswani et al., (017). MEGAN (Sachal
et al. 2021) models the retrosynthesis a sequence of graph edits.
G2Gs (Shi et al. 2020) and GRAPHRETRO (Somnath et al. 2021 are two-stage models, which first identify the reaction center, break the products into synthons, and then expand the synthons into reactants. G2Gs model the expansion of synthons reactants as sequential generation of atoms and bonds. GRAPHRETRO exploits the leaving groups extracted from dataset model the expansion a classification problem.
Implementation Details. Based on the vanilla TRANSFORMER (Karpov et al. 2019), we only introduce additional memory layers and do touch other components. For the hyper-parameters,
We implement TRANSFORMER using Pytorch (Paszke
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Top-1
Top-3
Top-5
Sirede Uagen
9.4
Aocurapy
Accuracy
03
0.
81
00
12 0.0
03
10
12
10
12
Depth
Depth
Depth
Figure 3: The top-1, top-3, and top-5 test accuracy in terms of depth (the minimum steps required to synthesize a target molecule). There is no beam search for GRAPHRETRO because of missing probabilities of each answer in their released codes. The cases whose depth is have too few data points for the results to be convincing, although there is jump at depth 10 for NEURALSYM.
we directly followed the reported setting in their released codes, and did not perform additional hyperparameter tuning. We still achieve a dramatic improvement for the planning task, which well demonstrates the effectiveness of our proposed method. The details of aperrparramters can also be found in Appendix B.T METRO is trained on NVIDIA Tesla V100 GPUs.
6.2 RESULTS
Main Results. Table [ reports the main results. From the table, we observe that our proposed METRO achieves the best performance and outperforms the baselines by at least 10.7% of top-1 test accuracy. Besides, the results demonstrate that our proposed model achieves a better performance against TRANSFORMER. More specifically, we can improve upon TRANSFORMER by margin of 13.2%, 14.5%, 11.1%, 10.5%, and 10.0% on top-1, top-2, top-3, top-4, and top-5 accuracy respectively. It demonstrates that the context information on the reaction route captured by our introduced memory module enables our model to search the reactants for single-step retrosynthesis reaction in a reasonable reaction space specified for this reaction route. Context information also preventing us searching through the whole reaction space, thus avoiding some wrong answers. Moreover, the results of baselines on our retrosynthetic planning task do not match well with single-step retrosynthesis prediction. Existing semi-template-baat the template-basee based and template-free models on the single-step retrosynthesis prediction but have poor performance on our task. A reasonable reason is that approximately 95% of the reactions in the USPTO-50K dataset used for single-step retrosynthesis reactions have only one reaction center. But in our constructed dataset, approximately 30% of reactions have multiple reaction centers. G2Gs only can handle cases with one reaction center, and so perform badly our task. Template-free models, which do not need to extract templates or find the reaction center using the labeling number, model the retrosynthesis as sequence-to-sequeeee problem or sequence of graph edits, which has better scalability and perform better on retrosynthetic planning tasks.
models outperform or match
Synthetic Accessibility Analysis. We group by the length the reaction tree the target molecules and report the top1, top3, and top5 test accuracy for each group. We report the results in Fig The results show that the more steps required to synthesize a target molecule, the lower the accuracy of all models' predictions. When the number of steps required is greater than 9, our model cannot predict the starting materials to synthesize the target molecule. This result is also consistent with molecule synthetic accessibility. The molecules are hard synthesize when the number of reaction steps required is larger in Li & Chen (0022). Our results further support this view. From Fig 3 we can observe that our model still performs better than all baselines when predicting the starting materials of the target molecules which need 2-7 reaction steps to synthesize. When testing on the 49 cases which need 8-10 steps. only GLN and NEURALSYM M outperform or match our model. When testing on cases that need more than 10 steps, all models can not make an accurate prediction. One future work is to add the template information to our model to improve the performance of the cases which need too many steps to synthesize.
6.3
CASE STUDY
In Fig. we visualize the predictions from METRO and TRANSFORMER. The top of Fig. 4 are the correct predictions from METRO, and the bottom are the wrong predictions from TRANSFORMER.
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2
3
1
2
3
4
Figure 4: Case Study. We split the predicted reaction tree into retrosynthesis reactions. On the top is the correct reaction tree predicted by METRO, and on the bottom is the predicted reaction tree predicted by TRANSFORMER.
observe that TRANSFORMER makes wrong prediction the third-step retrosynthesis reaction. Since the whole reaction space on which TRANSFORMER searches is too large to get correct predictions. Due the dependency of molecules on the reaction route context information, our METRO can search the reactants on a reasonable space. So our model can make correct predictions.
From Figure
RELATED WORK
Retrosynthesis Model. Existing machine learning models for retrosynthesis prediction can be
classified into Template-based, Semi-templatt--bseed and Template-free models. Template-based retrosynthetic algorithms (Chen al.,
2020 Coley al. 2017 Dai al. 2019 Segler & Waller 2017; Chen & Jung, 2021 Seidl et al.
2021 extract patterns from the training data which encode atoms and bonds change during the reaction. semi-template based models (Shi et al. 2020 Yan et al.] 2020 Somnath et al.) 2021) predict reactants via two stages: reaction center identification and reactants generation. Template-free algorithms (Liu et al. 2017 Zheng et al. 2019 Chen et al.. 2019 Karpov et al. 2019) model the retrosynthesis as sequence-to-sequeeec problem. Our work is closely related to the single-step retrosynthesis transformer. To capture the context information the reaction route, we introduce the memory module (Sukhbaatar et al. 2015).
Retrosynthetic Planning Search Algorithm. Existing Retrosynthetic Planning Search Algorithms model the retrosynthetic planning search problem. MCTS (Segler et al.) 2018) employs Monte Carlo tree search, DFPN-E (Kishimoto et al. (209) combines Depth-First Proof-Number (DFPN) with Heuristic Edge Initialization, Retro (Chen et al. 2020) proposes a neural-based A *-llike algorithm, and RetroGraph (Xie et al.] (2022) proposes a graph-based search policy. Our solution focuses on the base model which differs greatly from search algorithms.
Synthetic Accessibility Some machine learning models have been proposed for the estimation of the synthetic difficulty of molecules, such as SAscore (Ertl & Schuffenhauer 2009). PGFS (Gottipati) cet al. 2020), SCScore (Coley et al.) (2018)), SYBA (Voršilák et al. 2020). RAscore (Thakkar et al. 202T), ,, and knowledge graph (Li & Chen 2022). These approaches divide drug molecules into easy and hard-to-symtie to-st-es---tt categories, where hard-to-synthhsize molecules require longer reaction routes to synthesize.
CONCLUSION
In this work, we build a reaction- tree- based benchmark. We also propose a new retrosynthetic planning base model by extending the single-step retrosynthesis transformer with additional memory module. Our memory module can capture the context information the reaction route, enabling make better prediction for the retrosynthesis reaction on this route.

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Under review as a conference paper at ICLR 2023
REFERENCES
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Peter Ertl and Ansgar Schuffenhauer. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of Cheminformatics, 1(1):1-11, 2009.
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Arkadii Lin, Natalia Dyubankova, Timur I Madzhidov, Ramil I Nugmmaov, Jonas Verhoeven, Timur R Gimadiev, Valentina A Afonina, Zarina Ibragimova, Assima Rakhimbekova, Pavel Sidorov, et al. Atom-to-atom mapping: A benchmarking study of popular mapping algorithms and consensus strategies. Molecular Informatics, 4 41(4):2100138, 2022.
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Philipp Seidl, Philipp Renz, Natalia Dyubankova, Paulo Neves, Jonas Verhoeven, Jörg K Wegner, Sepp Hochreiter, and Günter Klambauer. Modern hopfield networks for few-and zero-shot reaction prediction. arXiv preprint XxXiv:2104.0327, 2021.
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Ryosuke Shibukawa, Shoichi Ishida, Kazuki Yoshizoe. Kunihiro Wasa, Kiyosei Takasu, Yasushi Okuno, Kei Terayama, and Koji Tsuda. Compret: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration. Journal of Cheminformatics, 12(1): 1-14, 2020.
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Systems, 2017.
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Shufang Xie, Rui Yan, Peng Han, Yingce Xia, Lijun Wu, Chenjuan Guo, Bin Yang, and Tao Qin. Retrograph: Retrosynthetic planning with graph search. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022.
Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, Jinyu Yang, Yang Yu, and Junzhou Huang. Retroxpert: Decompose retrosynthesis prediction like chemist. Advances in Neural Information Processing Systems, 2020.
Yemin Yu, Ying Wei, Kun Kuang, Zhengxing Huang, Huaxiu Yao, and Fei Wu. Grasp: Navigating retrosynthetic planning with goal-driven policy. Advances Neural Information Processing Systems, 2022.
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A
DATASETS DETAILS
Table 2: The number of target molecules in training/validationnness datasets in term of the shortest depths to synthesize the target molecules.
#Molecules
Depth
2
3
4
5
6
7
9
10
11
12
13
Dataset
Training
22,903
12,004
5,849
3,268
1,432
594
276
107
25
0
0
0
Validation
2,862
1,500
731
408
179
74
13
2
0
0
0
Test
2,862
1,500
731
408
179
74
34
13
2
32
2
1
B REPRODUCIBILITY
B.1 IMPLEMENTATION DETAILS
We use Pytorch Passke al. 2019 to implement Metro. The codes of baselines are implemented referring to the implementation of RETROSIM NEURALSYN GLN G2Gs GRAPHRETRO TRANSFORMER and MEGAN All the experiments in this work are conducted on single NVIDIA Tesla V100 with 32GB memory size. The software that use for experiments are Python 3.6.8, pytorch 1.9.0, pytorch-scatter 2.0.9, pytorch-sparse 0.6. 12, numpy 1.19.2, torchvision 0.10. CUDA 10.2.89, CUDNN 7.6.5, einops 0.4.1, and torchdrug 0.1.3.
B.2 HYPERPARAMETER DETAILS
Table 3: The hyper-parameters for Metro.
max length 200
embedding size
64
encoder layers
3
decoder layers
3
memory layers
attention heads
10
FFN hidden
512
dropout
0.1
epochs
4000
batch size
64
warmup
16000
Ir factor
20
C DETAILS OF SYNTHETIC ACcEsSIBILITY ANALYSIS
com/connorcolley/cttoooiiii
'https://githubb https///ggguuu..o//hcccc///aaa//
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Table 4: Top-k exact match accuracy of RETROSIM in terms of depth of reaction tree.
Top-k accuracy (%)
Depth
4
5
28.3
33.2
35.3
36.7
37.4
21.0
24.5
26.1
27.1
27.7
18.9
22.0
22.8
23.9
23.9
14.5
17.2
19.1
19.1
19.1
6
12.3
14.0
14.0
14.5
14.5
10.8
13.5
13.5
14.9
14.9
8
11.8
11.8
14.7
17.6
17.6
0
0
10
0
0
0
0
0
0
0
12
0
0
0
O
13
0
O
0
Table 5: Top-k exact match accuracy of NEURALSYM terms of depth of reaction tree.
Top-A accuracy (%)
Depth
1
2
4
5
32.4
39.0
41.2
42.5
43.4
24.7
30.1
31.7
33.0
33.7
20.5
24.4
26.0
26.7
27.2
18.9
22.5
23.8
24.0
24.0
14.5
15.1
16.2
16.8
16.8
9.5
10,8
12.2
12.2
12.2
23.5
23.5
23.5
23.5
23.5
0
0
0
0
0
0
0
11
0
0
0
12
0
0
0
0
13
0
0
O
Table 6: Top-k exact match accuracy of GLN in terms of depth of reaction tree.
Top-k accuracy (%)
Depth
2
4
5
33.2
41.1
43.7
45.1
45.8
22.1
29.7
31.7
33.7
34.6
18.1
23.1
26.1
26.9
27.6
16.4
20.8
21.8
23.0
23.3
9.5
12.8
15.6
16.8
17.3
8.1
8.1
8.1
8.1
9.5
8.8
8.8
8.8
8.8
8.8
15.4
15.4
15.4
15.4
15.4
10
0
0
0
0
11
0
0
0
0
0
0
0
0
13
0
0
0
0
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Under review as a conference paper at ICLR 2023
Table 7: Top-k exact match accuracy of G2GS in terms of depth of reaction tree.
Top-k accuracy (%)
Depth
4
6.5
10.1
11.9
13.5
14.4
2.7
3.9
4.6
5.1
5.8
4
1.0
1.1
1.5
1.8
1.8
0.2
0.5
0.5
0.5
0.5
0
0
0
0
0
0
8
0
0
0
0
0
10
0
0
0
0
11
0
12
0
0
0
13
0
0
Table 8: Top-k exact match accuracy of GRAPHRETRO in terms of depth of reaction tree.
Top-k accuracy (%)
Depth
1
4
5
21.3
11.6
6.0
3.7
0
0
0
0
10
0
11
12
0
0
Table 9; Top-k exact match accuracy of MEGAN in terms of depth of reaction tree.
Top-k accuracy (%)
Depth
2
4
5
26.2
37.8
44.5
48.6
51.0
18.4
27.3
32.6
35.3
37.5
13.0
20.0
23.1
26.1
27.4
8.6
14.5
18.1
19.9
21.1
6
10.6
14.5
18.4
21.8
24.0
1.4
1.4
2.7
5.4
5.4
0
0
2.9
0
0
0
0
10
0
0
0
0
11
0
0
0
0
0
0
0
13
0
0
0
0
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Under review as a conference paper at ICLR 2023
Table 10: Top-k exact match accuracy of TRANSFORMER in terms of depth of reaction tree.
Top-k accuracy (%)
Depth
2
4
5
33.2
44.4
49.1
51.6
53.0
22.1
30.8
35.1
38.4
40.0
13.4
17.9
21.3
23.3
24.9
6.9
10.3
12.5
13.5
14.2
7.3
14.5
19.0
22.3
24.0
0
0
0
1.4
1.4
0
2.9
5.9
0
0
0
0
0
10
0
0
0
0
11
0
0
0
12
0
0
0
0
0
0
Table 11: Top-k exact match accuracy of METRO terms of depth of reaction tree.
Top-k accuracy (%)
Depth
1
2
4
5
42.5
50.2
53.6
55.9
57.0
37.5
45.2
48.4
50.0
50.9
28.5
36.5
39.4
40.8
42.0
28.2
35.5
39.5
40.4
41.1
33.0
39.1
40.2
43.6
44.7
28.4
32.4
35.1
39.2
39.2
14.7
17.6
23.5
23.5
26.5
7.7
7.7
7.7
7.7
7.7
10
0
0
0
0
11
0
0
0
0
12
0
0
0
0
13
0
0
0
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Under review as a conference paper at ICLR 2023
Output (left shift)
Embodding Matrix
Embodding Matrix
ogn costicion
Encoder Layer
Memory Layer
Decoder Layer
FNV
FiN
Adc
Nem
Encoder Layer
Memory Layer
Decoder Layer
Encoder Layer
Memory Layer
Decoder Layer
Hill Gualial
Positional Encoding
wosicciemdd beck Teet
(U-catonjh- padis Scohenudis
ctuntisiis
seprigss escidiime
hilematt
Stans
1
1
800
"CCD Output (right shift)
Input
Figure 5: The details of encoder, decoder, and memory module.
D DETAILS OF ARCHITECTURE
Fig. 5 loutlines the details of three modules: encoder, decoder, and memory module. The encoder and decoder consist of several stacked attention layers. Each attention layer comprises multi-attention heads and a feed-forward layer. The attention heads of each attention layer perform attention mechanism in parallel and then are concatenated and projected into the final embedding. Specifically, an attention head (Scaled Dot-Product Attention) consists of three matrices: the quires Q, the keys K, and the values V. We get the attention scores by multiplying Q and K, and then get the output by multiplying the attention scores and V. The computation process can be written as
QKT VTL
Attention (Q,K,V) V) = softmax
V,
where die the scaling factor. As illustrated in Fig. we use the self attention layers in the encoder to transform the embeddings of input SMILES into the latent representations, e.g. encoder output. The Q, K, and V are the same hidden states from the previous layer in the encoder. But for the attention layer in the decoder, P corresponds the embeddings or the hidden states of the output SMILES (right shift), while Q and correspond the concatenation of the outputs of the encoder and the memory module. RetroXpert Yan et al. 2200 calls this type of attention encoder-decoder attention. This type of attention enables the decoder to combine the information of the input SMILES and output SMILES to capture the relationship between the product molecule and reactant molecules. By encoding the information of the relationship. we can model the retrosynthesis reaction and make reasonable predictions. Note that the input SMILES given the decoder are different from the output SMILES predicted by the decoder. The former is the right shift of the output SMILES. In addition, masked attention is used to avoid information leakage during training.
E COMPARISON OF DATASETS
We compare our proposed benchmark with Retro* Chen et al. 2020 and PaRoutes (Genheden &
Bjerrum 2022
As we have discussed in the Introduction, the benchmark of Retro only consists
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Under review as a conference paper at ICLR 2023
of 189 routes in the test set, which is too small to comprehensively evaluate the performance. The test set of our benchmark consists of 5838 reaction trees, which can comprehensively evaluate the performance. PaRoutes does not use the purchasable compounds in eMMolculc/ZINC stop condition. But in our opinion, we think using purchasable compounds in eMolecule/ZINC as stop condition is better solution with the following reason: If we use the reactions of other patents to build routes, the stock molecules which are constructed with the method proposed in PaRoutes may be different, which is inconsistent and tricky for the evaluation of machine learning models. Using the purchasable compounds in the eMolecule/ZINC databases as the stop condition has better scalability. We can train the machine learning model in one dataset and can apply this model for inference on other datasets directly.
18


Reviews:
[{"review_id": "yFK9nBlIFW", "reviewer": "Reviewer_1qQ1", "summary": "", "questions": "", "limitations": "", "rating": 5, "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": "", "summary_of_the_paper": "The authors propose a transformer with memory of reaction-trees for a new multi-step-retrosynthesis benchmark.", "summary_of_the_review": "The paper introduces a new interesting benchmark for multi-step retrosynthesis that might be influential in the community of retrosynthesis, however no code or dataset is provided.\nThe architecture seems interesting, but lacks connection to related work, as well as further clarifications.", "strength_and_weaknesses": "## Strengths:\n - Propose a new Benchmark for Multi-Step-Retrosynthesis\n - Propose Metro: Memory-Enhanced Transformer for RetrOsynthetic planning by extending Transformer with an additional memory module\n    - reaction route as context information\n    - can control the synthesis path\n\n## Weaknesses:\n - Missing Important Related Work: \n   - Section for Memory Enhanced Architectures, completely missing [1]\n   - Missing Template-Based Methods in Related Works section [2] [3]\n - More clarification on why extracted Reaction-Trees are seen as optimal\n - No code for reproducibility is provided\n - Architecture needs further explanations, e.g. Decoder, Tokenizer, \"Concatenated Embedding\" + residual connection, ...\n - The proposed dataset is a subset of USPTO-Full, please provide a comparison e.g. a scatter plot, of performance differences between e.g. USPTO-Full results and the results obtained on your benchmark set\n\n## Minor Comments\n - Was SMILES augmentation [4] performed or any other type of augmentation?\n - 5.1.1 paragraph about SMILES representation 2 might fit more in the subsequent paragraph 5.1.2, but is nothing inherent about the architecture\n - Please provide variance for point estimate in Table 1 as well as the significance\n - Is any standardization performed on molecules?\n - Please further describe the meaning behind A B C D E F in Figure 2\n - Appendix B.2.: Also describe the hyperparameter search space, as well as how they were obtained\n - Further discussion on why extracted Reaction-Trees are seen as optimal/better; e.g. there might exist faster/more optimal reaction routes\n\n### References\n - [1] Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., ... & Hochreiter, S. (2020). Hopfield networks is all you need. \n - [2] Chen, S., & Jung, Y. (2021). Deep retrosynthetic reaction prediction using local reactivity and global attention. JACS Au, 1(10), 1612-1620.\n - [3] 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.\n - [4] 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.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "iPjBqvqWObw", "reviewer": "Reviewer_tdV3", "summary": "", "questions": "", "limitations": "", "rating": 3, "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": "", "summary_of_the_paper": "The paper proposes a new benchmark and transformer model for retrosynthesis prediction. The model particularly makes use of the context of the retrosynthesis routes and is hence rather different from existing ones. It does so by using a memory module and an extension of transformer, using matrices in place of the values (i.e., the value matrix is a tensor here).\n", "summary_of_the_review": "The paper proposes a new benchmark and model for retrosynthesis prediction, yet it's missing an evaluation which would justify those. \nAltogether, I do not think that the paper can be accepted at the current stage and that it would need quite some revision.\n", "strength_and_weaknesses": "(+) It is true that existing route data is lacking, so the initiative to propose new data is good. Similarly the idea of including context information makes totally sense to me.\n\n(+) The results look promising.\n\n(-) From an ML perspective, the paper/model contains few novelty. \n \n(-) The paper proposes a new dataset as benchmark and one of the main contributions, but it does neither give much detail about the data nor does it provide a detailed comparison to existing works, apart from two sentences. There are recent datasets and benchmarks, e.g. [1], which should be mentioned and compared to. The related work section does not even contain a paragraph about datasets.\n\n[1] Samuel Genheden and Esben Bjerrum, PaRoutes: towards a framework for benchmarking retrosynthesis route predictions, https://pubs.rsc.org/en/content/articlehtml/2022/dd/d2dd00015f\n\n(-) The evaluation is lacking in that the comparison is with single-step models only. If additional (context) information is included, it is likely that the results will be better. It is interesting to see the magnitude of the improvement, however, to learn about the practical effectiveness I think other experiments are needed. Single-step models are usually applied within a planning algorithm. Since the authors focus on the routes, instead of single steps, I would suggest to evaluate the model also in a more realistic setting. For example, it would be interesting to see how close the proposed model (i.e., simple chaining of the transformer predictions) gets to Retro* with the MLP as single-step model, or how it performs when replacing the MLP by the transformer.\n\n================================================================================\n\nMinor Comments\n\n- sounds wrong to me:\n\"For two trees ..., we think these are different trees.\" \n\"Moreover, what needs to be declared is that molecule synthesis...\"\n- There are some typos in Algorithm 1\n- Why do G2Gs and Graphretro perform that bad?\n- What does this mean?: \n\"2) the dataset is based on reaction route instead of reaction tree.\"\n\"Note that in this paper, the default order of the reaction route is the retrosynthesis order.\"\n- The case study in the evaluation, to me it is unclear what it shows specifically, apart from the fact that the proposed model improves upon transformer.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}, {"review_id": "VjV3i-Hfz3", "reviewer": "Reviewer_r9kB", "summary": "", "questions": "", "limitations": "", "rating": 3, "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": "", "summary_of_the_paper": "- a benchmark for retrosynthesis is proposed\n- a model for retrosynthesis is presented\n- the authors claim state of the art\n\nthere are only limited novel contributions in this paper", "summary_of_the_review": "The proposed benchmark and approach are a small variation of previously existing work. Therefore this reviewer cannot justify to recommend  this work for publication in a top ML conference.", "strength_and_weaknesses": "### strengths\n- clear description\n- interesting memory model\n\n### weaknesses:\n- the paper has several incorrect definitions\n- the paper yet proposes another benchmark for retrosynthesis even though there are already several existing ones\n- the presented model is not properly compared\n- the baselines in the paper are inconsistent with previous work\n- somewhat limited ML model novelty\n- some proposed reactions in the case study are chemically incorrect", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "2: The contributions are only marginally significant or novel.", "empirical_novelty_and_significance": "1: The contributions are neither significant nor novel."}, "extra_text": {}}, {"review_id": "0SZY5OA9wTK", "reviewer": "Reviewer_f3iV", "summary": "", "questions": "", "limitations": "", "rating": 3, "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": "", "summary_of_the_paper": "This paper is concerned about retrosynthetic planning, where the task is, given a target molecule, to find a reaction tree to synthesize the target molecule from a set of starting compounds. The proposed method called Metro is based on single-step retrosynthesis transformer, but is equipped with a memory module so that it can make use of the retrosynthetic route as context information for the purpose of predicting the reactants. Another contribution is yet another preprocessing of the USPTO dataset, resulting in a new benchmark. The resultant dataset consists of reaction trees extracted from the dataset, and its test dataset is much larger than that in previous benchmark tasks.\n\nThe authors conducted empirical studies using the proposed benchmark. In terms of top-$k$ accuracy scores, the proposed method achieves more than 10 points improvements over the transformer model (= Metro - memory module). The authors also confirm that the accuracy degrades as the depth of the reaction tree increases.", "summary_of_the_review": "I am not positive about accepting this paper, mainly because I am still not motivated to utilize context information. I understand that while it can bias the retrosynthetic process and can prune the search space, its advantage over the existing methods has not been clarified because of the selection of baseline methods. If there is any misunderstanding, I would appreciate it if the authors could point them out.", "strength_and_weaknesses": "## Strength\n- The experimental results indicate that the proposed method achieves better accuracy scores than baseline methods, including a transformer-based method. The result suggests that the proposed memory module contributes to the performance improvement, validating their approach.\n- The authors propose a novel benchmark task considering the limitations of the existing one. In addition, since the details are well described in the paper, it will be not difficult to reproduce the task.\n\n## Weakness\n- __Why context information:__ Although the authors discuss why the context information helps in Section 5.3, I am not very convinced of it. The authors claim that the context information can prune the reaction search space, but as far as I understand, many of the existing methods utilize search algorithms for pruning. In fact, all of the methods compared in the experiments use the same DFS algorithm, which is not very fair for the existing methods.\n- __Baseline methods do not cover the existing methods introduced in the introduction:__ Since many of the existing methods combine ML and planning algorithms, it is necessary to compare the proposed method with other methods utilizing a wide variety of search algorithms.\n- __Presentation needs improvement:__ I had a bit difficulty in understanding the proposed algorithm, because the paper does not provide the whole algorithm (Algorithm 1 was helpful to understand it). In particular, although Figure 2 provides the overview of the proposed method, at first, it is difficult to understand why it has three inputs, A, B, and D. I would appreciate it if the authors could provide the whole algorithm first, and dive into the details after that. Similarly, although the authors pointed out three limitations in the third paragraph of the introduction, they came out suddenly, and I was not very motivated from them.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "1: The main claims of the paper are incorrect or not at all supported by theory or empirical results.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject