Molecular Graph Enhanced Transformer for Retrosynthesis Prediction
Authors: Kelong Mao
Peilin Zhao
Tingyang Xu
Yu Rong
Xi Xiao
Junzhou Huang
Venue:  (2020)

Abstract: With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simpliﬁed Molecular-Input Line-Entry System (SMILES) string, the process of synthesis is analogized to a process of language translation from reactants to products. However, the MT models that applied on SMILES data usually ignore the information of natural atomic connections and the topology of molecules. In this paper, we propose a Graph Enhanced Transformer (GET) framework, which adopts both the sequential and graphical information of molecules. Four different GET designs are proposed, which fuse the SMILES representations with atom embedding learned from our improved Graph Neural Network (GNN). Empirical results show that our model signiﬁcantly outperforms the Transformer model in test accuracy.

Under review as a conference paper at ICLR 2020
MOLECULAR GRAPH ENHANCED TRANSFORMER FOR RETROSYNTHESIE PREDICTION
Anonymous authors Paper under double-blind review
ABSTRACT
With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction formu- lated as Machine Translation (MT) task. Namely, since each molecule can be represented as Simplified Molecular-Input Line-Entry System (SMILES) string, the process of synthesis is analogized to a process of language translation from reactants to products. However, the MT models that applied on SMILES data usually ignore the information of natural atomic connections and the topol- ogy of molecules. In this paper, we propose a Graph Enhanced Transformer (GET) framework, which adopts both the sequential and graphical information of molecules. Four different GET designs are proposed, which fuse the SMILES representations with atom embedding learned from our improved Graph Neural Network (GNN). Empirical results show that our model significantly outperforms the Transformer model in test accuracy,
INTRODUCTION
Retrosynthesis prediction aims predict set of suitable reactants that synthesize the desired molecule via a series of reactions. It pushes forward an immense influence in agriculture, medical treatment, drug discovery and so on. However, the retrosynthesis prediction challenging since there are massive possible synthetic routes available and it is often difficult to navigate the direc- tion of retrosynthesis process. Indeed, each bond in the target molecule may represent a possible retrosynthetic disconnection, leading to a vast space of possible starting materials. Besides, the dif- ference between two synthetic routes may be subtle, which usually depends on the global structures. Actually, planning a proper retrosynthetic route for complex molecule is also a tough work even for the professional chemists.
One of the prevailing methods is to deem the retrosynthesis prediction task. This analogy comprehensible since every molecule has a unique text representation named SMILES (Weininger) 1988). In this case, given target molecule written in SMILES notation, the retrosynthesis prediction just to predict a string of SMILES which represents the reactants. Based on this idea, Liu al. (2017) first applied the L.STM with attention mechanism in retrosyn- thesis prediction and achieved comparable performance compared with previous traditional meth- ods. Whereafter, many works (Karpov et al.) 2019 Zheng al. 2019 Lin et al. 2019 Lee al.) 2019) tried to employ Transformer (Vaswani et al. 2017). which is a more powerful Sequence-to- Sequence(Seq2Seq) model, to improve prediction accuracy in retrosynthesis. However, these meth- ods just utilize the sequential representations of the molecule, while ignoring the natural topological connections between atoms within the molecule. These atomic connections can provide more flexi- ble and accurate chemical information, which is critical in many related chemical tasks like molecu- lar representation (Duvenaud et al. 2015) Gilmer et al. 2017) and chemical reaction prediction (Jin) al. 2017 Do et al. (019).. We believe that the absence of this molecular graph information hin- ders the further improvement of the present methods for retrosynthesis. How to effectively make use of this natural graphical information of the molecular structure, therefore, becomes vital problem.
machine translation
To tackle this problem, we propose Graph Enhanced Gransformer(GET) framework that can enjoy the advantage of both graph-level representations and sequence-level representations. Specifically, to solve the retrosynthesis problem, we design an improved Graph Neural Network(GNN) called Graph Attention with Edge and Skip-connection (GAES) learn each atom's 's representation, and

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try four strategies to incorporate it with the original SMILES representation in the encoder. The main contributions of this paper are as follows:
We propose new framework called GET that fuses graphical representations with sequen- tial representations of the target molecule solve retrosynthesis prediction task.
We design a powerful GNN called GAES that learns high-quality representations of atom nodes a self-attention manner with bond features, and it is less affected by the side-effect of stacking more layers.
GET is evaluated USPTO-50K, common benchmark dataset for retrosynthesis. Experimental results show that our model achieves new records for top-1 prediction accuracy in the state-of-the-ar methods and outperforms Seq2Seq-based methods in all tested top-n accuracy, demonstrating the effectiveness of fusing the molecular graph information with the SMILES sequence information.
2 RELATED WORK
Prior work on retrosynthesis can be mainly summarized into two categories: template- based meth- ods and template-fres free methods.
Template-based Methods The majority of computer-aided retrosynthetic methods in the early period were relied on encoding reaction templates or generalized subgraph matching rules. LHASA (Corey Wipke 1969) was the first software for retrosynthetic analysis. Recently, one of the most well-known retrosynthesis analysis tool is Synthia (Szymkué et al. 2016) that inte-
grated about 70,000 hand-encoded reaction rules collected by manual. Based on the 60,000 reaction templates derived from 12 million single-step reaction examples, Scchreck et al. (2019) introduced Reinforcement Learning (RL) into this area by treating retrosynthesis as a game whose goal is to identify policies that make (near) optimal reaction choices during each step of retrosynthetic plan- ning. (Segler et al. (2018) extracted two sets of transformation rules and combined Monte Carlo tree search with symbolic AI discover possible retrosynthetic routes. Besides manual extracted rules, some works (Law et al. 2009; [Segler & Waller 2017; Coley et al.] (017)) tried to collect reaction templates automatically and perform retrosynthesis based on these automated templates. Although template-bel basele methods work well in many cases, they still face serious drawback that they generally cannot achieve accurate prediction accuracy outside their known templates.
Template-free Methods Our model just belongs to this category. Emerging template-free methods are to treat retrosynthesis as a machine translation task as introduced in section [ Since these methods do not need any reaction templates and prior chemistry knowledge, they are attracting more and more attention from academia. Moreover, without the constraint of fixed templates, they have the potential of discovering novel synthetic routes. The most related work to ours are Zheng et al. (2019) Lin et al. (2019): Karpov et al. 2019); Lee et al. (2019) that apply Transformer in retrosynthesis prediction.
3 BACKGROUND
In this section, we first introduce how GNN is used in learning the molecule (or atoms) representa- tion, and then describe how the Transformer model is previously applied in retrosynthesis prediction.
3.1 GNN FOR MOLECULE REPRESENTATION LEARNING
GNN has been widely used in learning the representation of the molecule and its atoms. Naturally, molecules can be represented as graph structure with atoms as nodes and bonds as edges. Suppose that molecular graph G has initial node representations h, and edge representations evw. a typical one-layer GNN can learn new and more powerful node representations from G by the following message passing process described in Gilmer al. (2017):
my
Σ M (hw, Rw: Evw),
(1)
WEN(u)
h
new
U (h,, (h.,m.y.
(2)
where (v) denotes the neighbors node in graph G, M is the message function that is respon- sible for collecting information from neighbors, and U is the update function for fusing collected
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information my with old node representation h, to obtain the new node representation hgmw. Fur- ther, we can stack several these GNN layers to capture higher-order neighbors' information.
Then a readout function R can be used to integrate all node representations into whole graph representation g:
g R E G))-
The h2 and g. which represent the atoms and the whole molccule, are often trained in an end-to-end way for specific chemical task, such chemical properties prediction, reaction prediction and molecule optimization.
3.2 TRANSFORMER FOR RETROSYNTHESIS PREDICTION
Transformer (Vaswani et 2017 is Seq2Seq model that has shown excellent performance in machine translation task. Also, it has been applied in chemical reaction prediction and retrosynthe- sis prediction before. Given an input SMILES that represents the target molecule and specified reaction type (optional), retrosynthesis prediction is to predict the output SMILES which represents the possible reactants that can synthesis the target molecule the specified reaction type. Thus, ret- rosynthesis prediction can be deemed as machine translation task whose source language is target molecule SMILES and the target language is reactants SMILES.
In this view, Transformer can be applied to retrosynthesis prediction the same as to machine translation. Since Transformer is a mature model that has been widely used in natural language processing (NLP), just give simple introduction here. Specifically, Transformer follows an encoder-decoder structure and is composed of several combinations of multi-head attention layers and position-wise feed forward layers. The encoder consists of stack of N 6 identical layers. Each layer includes two main components: (multi-head) self-attention layer and feed-forward net- work. Given input vectors (Pl,..,,Po) Pn., , p E Rd, the /-th output 8, of the self-attention layer is calculated by:
W pm Vt = W.P., 8: softmax < qt. km
= 4-WPPM km
(4)
Vdk
where de the dimension q and k. Wg. Wm, W, are weight matrices. One such operation called one head, and can concatenate several heads change to multi-head self attention.
The feed-forward network is composed of two linear transformations with ReLU activation:
FFN(x) max (0, æ Wi + bi) W₂ + b2,
(5)
where Wi, W₂ are weight matrices and b,, b2 are biases.
Similarly, the decoder also mainly composed of multi-attention layers and position-ve -wise feed for- ward layers. It will generate the output SMILES step by step. At step f, it utilizes the encoder's ss out- put (p'...., ....,,'.) and all previous steps' output (z,,..., xi/1) 1) to generate the next SMILES character rt This process repeated until generating specific termination character, i.e., x =< EOS >.
4 GRAPH ENHANCED TRANSFORMER
In this section, we provide the details about our Graph Enhanced Transformer (GET) framework for retrosynthesis prediction. Figure [ shows an overview of GET. On the whole, GET is accord with typical encoder-decoder structure, of which the integral encoder is composed of the graph encoder and transformer encoder for learning the representation in graph-level and sequence-level respectively. Given target molecule's SMILES, it will first pass through the two encoders somehow to get the hidden representation of each character, and then the decoder will utilize these hidden representations to generate the output SMILES.
4.1 GRAPH ENCODER
We design a new powerful GNN called Graph Attention with Edge and Skip-connection (GAES) as the graph encoder, which learn the representation of each atom in molecule. This graphical
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N+
-
[N-I+N+)=N--
N#Cclccocel
Hidden Representation
Transformer Encoder
Decoder
Graph Encoder
<RX 4>clccol-c220 (22o
Integral Encoder
Overview of GET. The input SMILES will be processed by the two-sub encoders (graph encoder and transformer encoder) somehow to be transformed its hidden representation. Then, each step, the decoder will utilize the hidden representation and all outputs of the previous steps to generate the present step's SMILES character.
Figure 1:
representation reflects the connection of atoms within a molecule and may play a significant role in further alleviating the long-term dependency problem to avoid generating chemically invalid output. We use LDKid(La 2016) to transform SMILES into the molecular graph, whose nodes are atoms and edges chemical bonds. The input representation of the atom node is 21-dimensional vector that contains some chemical information about the atom, and the detail can be found in Table (nearly consistent with Gilmer al. (2017)). The input representation of the edge is 4-dimensional one-hot vector that encodes the bond types including single, double, triple and aromatic.
Since the SMILES sequence (æ11 .in) has been transformed into a graph G with the input repre- sentations (h1,- ...., hx) for nodes and {esj] } for edges that exist between node i and node our GNN will produce new representation h/ for each node by the following message passing operations:
exp (LeakyReLU (aT Wh,llWh,lless
Oaj Σ KEN, exp (LeakyReL.U (a" [Wh,] [he]]
(6)
h, = Σ aig Wiy,h,
JEN:
where E (22F++E) is weight vector for attention mechanism and W FF is weight matrix for transforming the node features, so F' is the input dimension of nodes, F² is the output dimension of nodes and E is the dimension of edges. N, is the set of first-order neighbors of node i (including itself). a an activation function, e.g., RcLU.
In practice. perform K multi-head attention (Veličković et al. 2017) to enrich the model capacity and to stabilize the learning process. Each attention head has its own parameters and we average their outputs get better representation:
K
1
h
=
ogy whh,
(8)
K
k=1jEN,
The above operations can be seen as GAT (Veličković 2017 extended include edge features.
Then, to mitigate the accuracy reduction issue (Kipl & Welling 2016) caused by stacked graph convolution layers, we adopt the gated kiip-connettion mechanism (Ryu et al. 2018) to get the final representation:
z; = (Ugmod(Uh, U2h, + b),
(9)
h 2, h: 2,)= h
(10)
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Table
Input representation of atom nodes
Atom Feature
Description
Atom type
C, N, O, S, P, P. F, Sn, CI, Br, Se, Si (one-hot) Numbers of protons (integer)
Atom number
Acceptor
Accepts electrons (binary)
Donor
Donates electrons (binary)
Aromatic
In an aromatic system (binary) sp. sp2, sp3 (one-hot null) (integer)
Hybridization
Number of Hydrogens
where U1, U2 and b are trainable parameters.
Note that the above operations are just in one layer of our GAES, and we can stack several layers to capture the information about higher-order atom neighbors that to obtain more comprehensive representations.
4.2 TRANSFORMER ENCODER
The transformer encoder is the same as described in section 3 which can capture the sequential representations of molecules (or atoms) represented by SMILES. The original SMILES (æ1, (I1,..., Fy,) is changed to sequence of vectors (P...... Pn), p E IR after passing the embedding layer. And it by the transformer encoder.
will be further updated to vectors
4.3 REPRESENTATION FUSION
Intuitively, graphical representations reflect the intrinsic structural features of molecules and should be beneficial generate chemical-vale and more accurate SMILES output. To this end, propose four fusion strategies to fuse these graphical and sequential embeddings.
Integral Encoder
HR
HR
HR
HR
Transformer Encoder
Graph Epcoder
Transformer Encoder
GR
Lincer
Linear
Linear
Graph Encoder
SR
SR
Transformer Encoder
GR
Transformer Encoder
ISR
GR
ISR
Embedding Laper
Graph Encoder
Embedding Layer
Embedding Layer
Graph Encoder
Embedding Layer
JAR
GET-LTI
GET-LT2
GET-CT
GET-LG
Inpot Sequanca Representation
Graph-ivel Regenerttation
JAR
ImputAtom Bepeesenbatin
Sequente level Represetatioo
Regensentaiion
Consatezate Operation
Figure 2: Illustration of four fusion strategies in the encoder of GET. The integral encoder is com- posed two sub-encoders: graph encoder and transformer encoder. The embedding layer as described in 4.2 The hidden representation of the target molecule can be obtained in four ways (GET-LTI, GET-LT2, GET-CT and GET-LG).

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shown in Figure GET--GG), the SMILES sequence first pass through the transformer encoder, then it is concatenated with natural atom features (h.,..,, h,)) to be the input representation of the graph encoder. Those non-atomic characters are added into the molecular graph as standalone nodes Under review as a conference paper at ICLR 2020
4.3.1 GRAPH LINK TRANSFORMER
As shown in Figure (GET-LTI), we concatenate the atom representations with embeddings of SMILES and perform linear transformation by weight matrix M. Then the new representations are sent to the transformer encoder to produce the output of the integral encoder. For non-atomia characters in SMILES, the corresponding atom representations are set vector. Formally,
[p:|[/]] (V), vn) (pi,... p(,) MMausforreerBuoodreMM.. Mpn),
P.
(11)
(12)
where h if x; is non-atomic character, || encoder's output.
is the concatenation operation. (v1, Un)) is integral
Considering that there may exist inconsistency between encoder and decoder since the initial atom features inputted to the graph encoder cannot be utilized by the decoder when making inference, try another way by replacing the natural atom features (hi,..., h,, with (PI...,,y, Pn) as the input representations of the graph encoder:
(h1, .... h/,) GraphEEccode((p,.....
(13)
In this way, the graph encoder can enhance the original sequential representations (Pi...., Pn) with molccule structure information directly, but the natural atom features have to "sacrificed', Then send the output of the graph encoder to the transformer encoder to get the final output:
(UI,..,, Up)) (p/, ........,)) = TransformerFnoooder(h h(,))
(14)
We name the first scheme GET-LTI and the second scheme GET-LT2.
4.3.2 GRAPH CONCATENATE WITH TRANSFORMER
As shown Figure 2 (GET-CT), we concatenate the outputs of the graph encoder and transform encoder, and also perform linear transformation to get the output of the integral encoder:
p: = P'11AA] (U1.... Un) (MP MP.))
(15)
(16)
The notations are consistent with 4.3.1
4.3.3 TRANSFORMER LINK GRAPH
which do not connect with any other node, and their "atom feature vectors" are just zero vectors. Finally, the graph encoder's output will be the integral encoder's output:
(Vi,..,, v,)) (h/,-.. h(,)) GraphEEcoder((p// (hi), [p,, ((mn))
(17)
4.4 DECODER
The decoder is the same as vanilla Transformer's (Vaswani et al. 2017) decoder which has been introduced in section 3 At step the encoder's output (U1,..., V,,) and all previous steps'
output (x4, .... x/-1) are used by the decoder to generate the next SMILES character z until of =< FOS >.

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5 EXPERIMENTS
In this section, we evaluate our model for retrosynthesis prediction on a common benchmark dataset USPTO-50K which derived from USPTO granted patents that includes 50,033 reactions classified into 10 reaction types. A reaction is described as pair of sequences which consist of SMILES nota- tions for target molecule (with reaction type) and reactants. For example, an heterocycle formation reaction is described as: ("<RX 4>c (cccc((c22n(((n]]]
"N#Colccoce [NN-J- where "<RX 4>" represents heterocycle formation reaction, cco(=22nm[[H]]] is SMILES of the target molecule, "N#Cel ccccel" and "[N-]=[N+] are SMILES of two reactants sepa- rated by ""
5.1 SETTINGS
Dataset Split Many previous works (Liu et al. 2017 Coley etal. 2017 Zheng al. 2019 Karpov et al.) (019) follow specific split strategy with 40,029, 5,004 and 5,004 reactions for training, validation and testing, and we keep the same.
Implementation For the graph encoder, it is implemented based on DGL (Wang al. 2018). We stack 3 identical layers in our GNN. The input and output dimension nodes are to 21 and 256 respectively. The number of multi-head is set 2; For the transformer encoder and the decoder, we implement them using OpenNMT (Klein et al. 2017), and the parameter settings are presented the code; Besides, the final dimension of the integral encoder's output v is set to 256.
5.2 RESULT
We compare our model with the vanilla Transformer (Vaswani et al. 2017), Rule-based Expert System mentioned in Liu et al. (2017). Similarity (Coley et al. 2017) and L.TM+ (Liu et al. 2017). Note that the results of vanilla Transformer are based on our own experiments since the results reported by previous works (Zheng al. 2019 Lin al. 2019; Karpov et al., 20119] Lee it al. 2019) are different from each other. The retrosynthesis prediction accuracy across all classes provided in Table 2 Moreover, we also test the performance of GET-LT1 when removing the reaction type from the original dataset, and the result is shown in Table 3
Table 2: Comparison of top-n accuracies across all classes
top-n accuracy (%)
Model
3
5
10
Rule-based Expert System
35.4
52.3
59.1
65.1
Lttenionnntion
37.4
52.4
57.0
61.7
Similarity
52.9
73.8
81.2
88.1
Transformer (baseline)
54.3
68.4
72.0
74.4
GET-CT (our)
55.9
70.1
73.2
76.3
GET-LG (our)
54.9
69.7
72.2
74.6
GET-LT2 (our)
56.2
69.4
72.5
74.7
GET-LTI (our)
57.4
71.3*
74.8*
77.4*
Table 3: Comparison of top-n accuracies across all classes without reaction type
top-n accuracy (%)
Model
3
5
10
Similarity
37.3
54.7
63.3
74.1
Transformer (baseline)
42.3
57.5
61.0
65.7
GET-LTI (our)
44.9
58.8
62.4
65.9
Results show that our models outperform all previous methods in top-1 accuracy, and our best model GET-LTI achieves the new state-of-the-art among all Seq2Seq-based methods, i.e.

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LSTM+AAttentin, vanilla Transformer and models of GET. Compared with vanilla Transformer, GET-LTI can improve the prediction accuracy by 3.1%, %, 2.9%, 2.8% and 3.0% in top-1, top-3, top-5 and top-10 accuracy. Other variants also have varying degrees of performance improvement over vanilla Transformer. And our model can retain this comprehensive superiority after removing the reaction type, demonstrating that molecule structure information can help Transformer to predict more accurate reactants, Besides, note that Similarity (Coley et al. 2017)) is template-based model which predicts 100 candidates and just chooses the top-10 10 as the final result, while other template- free models, i.e., LSTM+Attention, Transformer and GET, are trained to accurately predict the top-1 output and only generate 10 candidates using beam search. Therefore, is not surprising that Simi- larity achieves very high accuracy. Nevertheless, even in this unfair situation, our models can surpass Similarity up to 4.5% in top-1 accuracy.
In addition, (LSTM+Attention vanilla Transformer and GET-LTI) for each reaction class in Table E Results show that our approach can improve vanilla Transformer on 9 of 10 reaction classes by margin of 1.3% to 17.4%, indicating the better generalization ability and comprehensiveness of our model.
we present the detailed top-10 accuracy of three Seq2Seq-based models
Table 4: Comparison of the top-10 10 accuracy for each reaction class
top- 10 accuracy (%)
Model
2
4
6
8
10
LSTM+Attention
57.5
74.6
46.1
27.8
80.0
62.8
67.8
69.1
47.3
56.5
Transformer (baseline)
73.5
81.9
62.7
52.2
86.1
71.5
80.0
83,9
65.2
73.9
GET-LTI (our)
76.6
84.2
66.1
65.6
89.2
75.7
81.3
81.5
71.7
91.3
Furthermore, the rate of producing grammatically invalid SMILES for different beam sizes are shown in Table (with reaction type). As can be seen, after fusing molecule structure information, the model is more inclined to generate chemical-valid SMILES compared with vanilla Transformer, since the graphical representations, which directly capture the topological connection of atoms, able to break the limitation of the SMILES sequence and can give the model additional guidance to produce chemical-valid compound.
Table 5: The rate of producing grammatically invalid SMILES for different beam sizes
invalid SMILES' rate (%) when beam size k
Model
5
LStM+Attentin
12.2
15.3
18.4
22.0
Transformer (baseline)
3.5
14.3
20.3
30.2
GET-LTI (our)
2.2
13.4
19.5
29.3
CONCLUSION AND FUTURE WORK
We propose Graph Enhanced TrTT an effective framework that successfully combines the graphical and sequential representations of the molecule to improve the retrosynthesis predic- tion performance. Experiments indicate that our model outperforms state-of-the thettherrt Seq2Seq- methods on USPTO-50K dataset, and shows promising ability in reducing invalid SMILES rate.
In the future, we plan to explore how to utilize the molecular graph information in the decoder. 2) research how to let the decoder generate reactants in the form of graph directly.
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Reviews:
[{"review_id": "rygfa6a_Fr", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "\nThis paper focuses on the retrosynthesis prediction problem which to my understanding is the factorization of a target molecule into simpler structures. Previously, retrosynthesis prediction has been tackled as a language translation problem by using a Seq2Seq algorithm called \"Transformer\". This sequential approach was possible because molecules can be expressed as strings using the following format: Simplified Molecular-Input Line-Entry System (SMILES).\n\nDespite its good performance, language translation methods ignore the graphical structure of molecules. This paper proposes to add a graph neural network in front of the Seq2Seq/Transformer network to exploit the graphical structure, hence the name “Graph Enhanced Transformer (GET)”. The Graph Neural Network considered in this work also uses an attention mechanism similarly to Graph Attention Networks.\n\nThe main contribution is the addition of a Graph Neural Network to a Seq2Seq model for retrosynthesis prediction which provides state of the art results.\n\nFrom a machine learning / representation learning perspective, I do not consider the paper is innovative enough. Even so, the paper shows strong results for retrosynthesis prediction, I guess it would be a good fit in a chemistry conference.\n\n\nThings to improve:\n\nThere are plenty of works related to graph Autoencoders and Graph generative models that are not mentioned in the related work. It would be great to dedicate a section for them. Here some examples: \n\n- Li, Y., Vinyals, O., Dyer, C., Pascanu, R., & Battaglia, P. (2018). Learning deep generative models of graphs.\n\n-Simonovsky, M., & Komodakis, N. (2018, October). Graphvae: Towards generation of small graphs using variational autoencoders.\n\n-De Cao, N., & Kipf, T. (2018). MolGAN: An implicit generative model for small molecular graphs.\n\n-You, J., Ying, R., Ren, X., Hamilton, W. L., & Leskovec, J. (2018). Graphrnn: Generating realistic graphs with deep auto-regressive models.\n\nAll experiments are compared to author’s re-implementations of other works. It would be interesting to directly report other work accuracies.\n", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "HygJspHTFB", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "~The authors propose an enhancement to the transformer architecture that takes molecule graph structure into account.~\n\nI applaud the authors work on making more physically plausible machine learning constraints. However, I feel like this work is incremental and does not vastly improve SoA.\n\nFor all tables, what are the error bars on the accuracy?\n\nThere are multiple possible SMILES strings for any unique molecule (which includes the canonical SMILES string.) Does bootstrapping your Transformer model with multiple non-canonical SMILES for the same input molecules improve performance?\n\nMany seq2seq molecules allow an attention mechanism on the input sequence while decoding, and that seems like this would be useful for this data. What would the impact of this be?\n\nDo you have any explanation why GET-LT1 is your top performing model?\n\nDoes your model generalize to unseen molecules or reactions better than previous methods?\n\nIn Table 5, why does the % invalid SMILES go up with more beams? I would figure that larger beam sizes would result in more valid generated molecules?\n\nIn Table 4, please report the number of parameters for each model.\n\nIn Table 2, what are the astrices (*) in the GET-LT1 row?\n\nIn Table 3, you should bold the Similarity model for the top-5 and top-10 accuracy.\n\nIn equation 9, do you mean “sigmoid”?\n\nIn equation 8, should the k superscript be a subscript?\n\nHow would this model perform with SELFIES representation of small molecules, which are more robust representation [Krenn et al., 2019]?\n", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "H1gIxC_m9H", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 1, "confidence": null, "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": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposed Graph Enhanced Transformer(GET) to combine the graphical and sequential representations of the molecule to improve the retrosynthesis prediction performance. Experiments indicated that the proposed model outperforms state-of-the-art Seq2Seq-based methods on USPTO-50K dataset, and showed ability in reducing invalid SMILES rate.\n\nTwo main comments: \n\n1. This paper provide no novelty with respect to deep learning method. It is just a combination of sequence transformer and graph neural network (using RDKit(Landrum, 2016) to transform a SMILES into the molecular graph). The decoder is the same as vanilla Transformer to generate SMILE string output. \n\n2. The writing can be improved. For instance, in the caption of Figure 1 - \"somehow to be transformed\" ...  plus a few other places have wording issues like this.  \n\n \n\n\n", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}]

Author rebuttal:

Meta-review: Several approaches can be used to feed structured data to a neural network, such as convolutions or recurrent network. This paper proposes to combine both roads, by presenting molecular structures to the network using both their graph structured and a serialized representation (SMILES), that are processed by a framework combining the strenth of Graph Neural Network and the sequential transformer architecture.

The technical quality of the paper seems good, with R1 commenting on the performance relative to SOTA seq2seq based methods and R3 commenting on the benefits of using more plausible constraints. The problem of using data with complex structure is highly relevant for ICLR.

However, the novelty was deemed on the low side. As a very competitive conference, this is one of the key aspects necessary for successful ICLR papers. All reviewers agree that the novelty is too low for the current (high) bar of ICLR. 
Decision: Reject