Year: 2021

Abstract:
Retrosynthesis brings scientific and societal benefits by inferring possible reaction routes toward novel molecules. Recently, template-based (TB) and templatefree (TF) molecule graph learning methods have shown promising results to solve this problem. TB methods are more accurate using pre-encoded reaction templates, and TF methods are more scalable by decomposing retrosynthesis into subproblems, i.e., center identification and synthon completion. To combine both advantages of TB and TF, we suggest breaking a full-template into several semitemplates and embedding them into the two-step TF framework. Since many semitemplates are reduplicative, the template redundancy can be reduced while the essential chemical knowledge is still preserved to facilitate synthon completion. We call our method SemiRetro and introduce a directed relational graph attention (DRGAT) layer to extract expressive features for better center identification. Experimental results show that SemiRetro significantly outperforms both existing TB and TF methods. In scalability, SemiRetro covers 96.9% data using 150 semitemplates, while previous template-based GLN requires 11,647 templates to cover 93.3% data. In top-1 accuracy, SemiRetro exceeds template-free G2G 3.4% (class known) and 6.4% (class unknown). Besides, SemiReto has better interpretability and training efficiency than existing methods.

heading: 1 INTRODUCTION
text: Retrosynthesis prediction (Corey & Wipke, 1969; Corey, 1991) plays a crucial role in synthesis planning and drug discovery, which aims to infer possible reactants for synthesizing a target molecule. This problem is quite challenging due to the vast search space, multiple theoretically correct synthetic paths, and incomplete understanding of the reaction mechanism, thus requiring considerable expertise and experience. Fortunately, with the rapid accumulation of chemical data, machine learning is promising to solve this problem (Coley et al., 2018; Segler et al., 2018). In this paper, we focus on the single-step version 1: predicting the reactants from a given product.
Common deep-learning-based retrosynthesis works can be divided into template-based (TB) (Coley et al., 2017b; Segler & Waller, 2017; Dai et al., 2019) and template-free (TF) (Liu et al., 2017; Karpov et al., 2019) methods. Generally, TB methods achieve high accuracy by leveraging reaction templates, which encode the molecular changes during the reaction. However, the usage of templates brings some shortcomings, such as high computation cost and incomplete rule coverage, limiting the scalability. To improve the scalability, a class of chemically inspired TF methods (Shi et al., 2020; Yan et al., 2020) (see Fig. 1) have achieved dramatical success, which decompose retrosynthesis into subproblems: i) center identification and ii) synthon completion. Center identification increases the model scalability by breaking down the target molecule into virtual synthons without utilizing templates. Synthon completion simplifies reactant generation by taking synthons as potential starting molecules, i.e., predicting residual molecules and attaching them to synthons to get reactants. Although various TF methods have been proposed, the top-k retrosynthesis accuracy remains poor, especially when the reaction class is unknown. Can we find a more accurate way to predict potential reactants while keeping the scalability?
To address the aforementioned problem, we suggest combining the advantages of TB and TF approaches, and propose a novel framework namely SemiRetro. Specifically, we break a full-template
1In single-step retrosynthesis, the synthesis route length is 1, i.e., only one reaction needs to be inferred.
into several simpler semi-templates and embed them into the two-step TF framework. As many semi-templates are reduplicative, the template redundancy can be reduced while the essential chemical knowledge is still preserved to facilitate synthon completion. Moreover, we introduce a directed relational graph attention (DRGAT) layer to extract more expressive molecular features to improve center identification accuracy. Finally, we combine the center identification and synthon completion modules in a unified framework to accomplish retrosynthesis predictions.
We evaluate the effectiveness of SemiRetro on the benchmark data set USPTO-50k, and compare it with recent state-of-the-art TB and TF methods. We show that SemiRetro significantly outperforms these methods. In scalability, SemiRetro covers 96.9% of data using 150 semi-templates, while previous template-based GLN requires 11,647 templates to cover 93.3% of data. In top-1 accuracy, SemiRetro exceeds template-free G2G 3.4% (class known) and 6.4% (class unknown). Owing to the semi-template, SemiReto is more interpretable than template-free G2G and RetroXpert in synthon completion. Moreover, SemiRetro trains at least 6 times faster than G2G, RetroXpert, and GLN. All these results show that the proposed SemiRetro boosts the scalability and accuracy of deep retrosynthesis prediction.

heading: 2 RELATED WORK
text: Template-based models TB methods infer reactants from the product through shared chemical transformation patterns, namely reaction templates. These templates are either hand-crafted by human experts (Hartenfeller et al., 2011; Szymkuć et al., 2016) or automatically extracted by algorithms (Coley et al., 2017a; Law et al., 2009). For a product molecule, due to the vast search space, multiple qualified templates, and non-unique matching sites for each template, it is challenging to select and apply the proper template to generate chemically feasible reactants. To handle those challenges, (Coley et al., 2017b) suggests sharing the same templates among similar products. (Segler & Waller, 2017; Baylon et al., 2019) employ neural models for template selection with molecule fingerprint as input. The state-of-the-art GLN (Dai et al., 2019) learns the joint distribution of templates and products by decomposing templates into pre-reaction and post-reaction parts and introducing logic variables to apply structure constraints. TB methods are interpretable and accurate because they embed rich chemical knowledge into the algorithm. However, the vast space of templates and incomplete coverage severely limit their scalability.
Template-free models Instead of explicitly using templates, TF approaches learn chemical transformations by the model. (Liu et al., 2017; Karpov et al., 2019) solve the retrosynthesis problem with seq2seq models, e.g. Transformer (Vaswani et al., 2017), LSTM (Hochreiter & Schmidhuber, 1997), based on the SMILES representation of molecules. Despite the convenience of modeling, SMILES cannot fully utilize the inherent chemical structures and may generate invalid SMILES strings. Therefore, (Zheng et al., 2019) propose a self-corrected transformer to fix the syntax errors of candidate reactants. Recently, G2G (Shi et al., 2020), RetroXpert (Yan et al., 2020) and GraphRetro (Somnath et al., 2021) achieve state-of-the-art performance by decomposing the retrosynthesis into two sub-problems: i) center identification and ii) synthon completion, as shown in Fig. 1. Center identification increases the model scalability by breaking down the target molecule into virtual synthons without utilizing templates, among which G2G reports the highest accuracy. Synthon completion simplifies the complexity of reactant generation by taking synthons as potential starting molecules. For example, RetroXpert and G2G treat it as a SMILES or graph sequence translation problem from synthon to reactant. GraphRetro completes synthons by predicting pre-defined leaving groups, but it does not provide end-to-end open-source algorithms for attaching leaving groups and model construction. Generally, these TF methods are more scalable but perform worse than TB GLN in top-1 accuracy.
Challenges Although the two-step TF framework significantly improves the algorithm’s scalability, the overall accuracy is relatively low. A possible solution to this issue is to enhance submodules, i.e., center identification and synthon completion. 1) To the best of our knowledge, current GNN models only work well for center identification when the reaction class is known; Otherwise, the model performance degrades rapidly. How to develop a more suitable model that works well with unknown classes is the first challenge. 2) In addition, synthon completion is the major bottleneck affecting the overall accuracy. Specifically, predicting and attaching residuals for each synthon are
difficult because the residual structures could be complex, attaching residuals into synthons may violate chemical rules, and various residuals may agree with the same synthon (e.g., F, CI, Br, and I have similar chemical properties). For researchers, scalability, interpretability, and training efficiency are also important. How to develop a more accurate, interpretable, and efficient synthon completion model while maintaining the scalability is the second challenge.

heading: 3 DEFINITION AND OVERVIEW
text: Molecule representation There are two types of dominant representations, i.e., SMILES string (Weininger, 1988) and molecular graph. SMILES is commonly used in early works (Liu et al., 2017; Schwaller et al., 2018; Zheng et al., 2019; Schwaller et al., 2019; Tetko et al., 2020) due to its simpleness. Many NLP models can be directly applied to solve related problems in an end-to-end fashion. However, these models cannot guarantee the chemical correctness of the output molecules because they ignore structure information to some extent. Similar to recent breakthroughs (Dai et al., 2019; Shi et al., 2020; Yan et al., 2020; Somnath et al., 2021), we take the molecule as a labeled graph G(A,X,E), where A, X and E are adjacency matrix, atom features and bond features, seeing Table. 1. Under the graph framework, we can effectively apply chemical constraints to ensure the validity of output molecules. Besides, graph-based methods are more controllable and interpretable than SMILES-based ones.
Problem definition Retrosynthesis aims to infer the set of reactants {Ri}Ni=1 that can generate the product P . Formally, that is to learn a mapping function Fθ:
Fθ : P 7→ {Ri}N1i=1. (1)
Considering the unknown by-products, the law of conservation of atoms no longer holds here, which makes the problem quite challenging because the algorithm needs to generate new atoms and bonds to get potential reactants automatically.
Overview As shown in Fig. 1, we adopt the two-step TF framework due to its scalability and effectiveness. Our method is distinguished from previous works in two folds: 1) We propose a relational graph attention (DRGAT) layer to improve the center identification performance; 2) We use semitemplates to facilitate synthon completion, which significantly reduces the problem complexity.

heading: 4 METHODOLOGY

heading: 4.1 CENTER IDENTIFICATION
text: Center identification plays a vital role in the two-step retrosynthesis because errors caused by this step directly lead to the final failures. Previous works have limitations, e.g., RetroXpert (Yan et al., 2020) provides incomplete prediction without considering atom centers, and G2G (Shi et al., 2020) performs poorly when the reaction class is unknown. How to obtain comprehensive and accurate center identification results is still worth exploring.
Reaction centers We consider both atom centers and bond centers in the product molecule. As shown in Fig. 2, from the product to its corresponding reactants, either some atoms add residuals by dehydrogenation without breaking the product structure (case 1), or some bonds are broken to allow new residues to attach (case 2). Both these atoms and bonds are called reaction centers.
Directed relational GAT Commonly used graph neural networks (Defferrard et al., 2016; Kipf & Welling, 2016; Veličković et al., 2017) mainly focus on 0 and 1 edges, ignoring edge direction and multiple types, thus failing to capture expressive molecular features. As to molecules, different bonds represent different interatomic interactions, resulting in a multi-relational graph. Meanwhile, atoms at the end of the same bond may gain or lose electrons differently, leading to directionality. Considering these factors, we propose a directed relational graph attention (DRGAT) layer based on the general information propagation framework Zheng et al. (2021), as shown in Fig. 3. During message passing, DRGAT extracts source and destination node’s features via independent MLPs to consider the bond direction and use the multi-head edge controlled attention mechanism to consider the multi-relational properties. We add shortcut connections from the input to the output in each layer and concatenate hidden representations of all layers to form the final node representation.
Labeling and learning reaction centers We use the same labeling algorithm as G2G to identify ground truth reaction centers, where the core idea is comparing each pair of atoms in the product P with that in a reactantRi. We denote the atom center as ci ∈ {0, 1} and bond center as ci,j ∈ {0, 1} in the product P . During the learning process, atoms features {hi}|P|i=1 are learned from the product P by applying stacked DRGAT, and the input bond features are {ei,j |ai,j = 1}. Then, we get the representations of atom i and bond (i, j) as
ĥi = hi||Readout({hs}|P|s=1)︸ ︷︷ ︸ atom representation and ĥi,j = eij ||hi||hj ||Readout({hs}|P|s=1)︸ ︷︷ ︸ bond representation , (2)
where we use average Readout operation, and || is vector concatenation operation. Further, we predict the atom center probability pi and bond center probability pi,j via MLPs:
pi = MLP6(ĥi) and pi,j = MLP7(ĥi,j). (3)
Finally, center identification can be reduced to a binary classification, whose loss function is: L1 = ∑ P ( ∑ i
ci log pi + (1− ci) log pi︸ ︷︷ ︸ atom center
+ ∑ i,j
ci,j log pi,j + (1− ci,j) log pi,j︸ ︷︷ ︸ bond center ). (4)
In summary, we propose a directed relational graph attention (DRGAT) layer to learn expressive atom and bond features for accurate center identification prediction. We consider both atom center and bond center to provide comprehensive results. In section. 5.2, we show that our method can achieve state-of-the-art accuracy, especially with reaction class unknown.

heading: 4.2 SYNTHON COMPLETION
text: Synthon completion is the main bottleneck of two-step TF retrosynthesis, which is responsible for predicting and attaching residuals for each synthon. This task is challenging because the residual structures could be complex to predict, attaching residuals into synthons may violate chemical rules, and various residuals may agree with the same synthon. Because of these complexities, previous synthon completion approaches are usually inaccurate, unexplainable, and cumbersome. Introducing the necessary chemical knowledge to improve interpretability and accuracy can be a promising solution. However, how to provide attractive scalability and training efficiency is a new challenge.
Semi-templates The semi-template used in this paper is the local reaction pattern of each synthon, as shown in Fig. 4. Different from GraphRetro (Somnath et al., 2021), our semi-template contains not only residuals but also synthon’s local structure. Similar to the work of forward reaction prediction (Segler & Waller, 2016), semi-template splits a binary reaction into two half reactions. Notably, we use dummy atom ∗ to represent possible synthon atoms that match the semi-template, significantly reducing redundancy. We extract semi-template from each synthon-reactant pair by removing reactant atoms that have exact matches in the synthon. There are two interesting observations: 1) Top-150 semi-templates cover 96.9% samples; 2) Reactants can be deterministically generated from semi-templates and synthons (introduced later). Based on these observations, synthon completion can be further simplified as a classification problem. In other words, we need to predict the semitemplate type for each synthon, and the total number of classes is 150+1. The first 150 classes are top-150 semi-templates, and the 151st class indicates uncovered classes.
Learning semi-templates For each synthon Sj , denote its semi-template label as tj , 1 ≤ tj ≤ 151. We use stacked DRGATs to extract atom features {hi} |Sj | i=1 from Sj . In each synthon, atoms that exactly match the semi-template are called supporting atoms, seeing circled atoms in Fig. 4. Given the supporting atom set {i0, i1, · · · }, the semi-template representation is:
ĥj = Readout({hi} |Sj | i=1)||Readout({hk|k ∈ {i0, i1, · · · }}). (5)
Based on this representation, we can get the predicted semi-template t̂j as:
t̂j = argmax 1≤c≤151 p̃j,c; p̃j = Softmax(MLP8(ĥj)). (6)
Denote 1{c}(·) as the indicator function, the cross-entropy loss used for training is:
L2 = − ∑
j∈{1,2,··· ,|S|} ∑ 1≤c≤151 1{c}(tj) log(p̃j,c). (7)
Applying semi-templates Once reaction centers, synthons, and corresponding semi-templates are known, we can deduce reactants with almost 100% accuracy. This is not a theoretical claim; We provide a practical residual attachment algorithm in the appendix and open source code on GitHub.
In summary, we suggest using the semi-templates to improve synthon completion performance. Firstly, reducing this complex task to a classification problem helps promote training efficiency and accuracy. Secondly, the high coverage of semi-templates significantly enhanced the scalability of TB methods. Thirdly, the deterministic residual attachment algorithm improves interpretability and accuracy. In section. 5.3, we will show the effectiveness of the proposed method.

heading: 5 EXPERIMENTS
text: As mentioned earlier, the main contributions of this paper are proposing a DRGAT layer for central identification and suggesting to use semi-template for synthon completion. The effectiveness of the proposed method is evaluated by systematic experiments, which focus on answering these questions:
• Q1: For center identification (CI), how much performance gain can be obtained from DRGAT? • Q2: For synthon completion (SC), can semi-templates reduce template redundancy and improve
the synthon completion performance? And why? • Q3: For retrosynthesis, how do we integrate CI and SC models into a unified retrosynthesis frame-
work? Can SemiRetro outperform existing template-based and template-free methods?

heading: 5.1 BASIC SETTING
text: Data We evaluate SemiRetro on the widely used benchmark dataset USPTO-50k (Schneider et al., 2016) to show its effectiveness. USPTO-50k contains 50k atom-mapped reactions with 10 reaction types. Following (Dai et al., 2019; Shi et al., 2020; Yan et al., 2020), the training/validation/test splits is 8:1:1. As mentioned in previous works, the USPTO-50k dataset contains a shortcut in that the product atom with atom-mapping ”1” is part of the reaction center in 75% of the cases. In our graph-based methods, this shortcut will not be introduced by forbidding atom position encoding.
Baselines Template-based GLN (Dai et al., 2019), template-free G2G (Shi et al., 2020) and RetroXpert (Yan et al., 2020) are primary baselines, which not only achieve state-of-the-art performance, but also provide open-source PyTorch code that allows us to verify their effectiveness. To show broad superiority, we also comapre SemiRetro with other baselines, incuding RetroSim (Coley et al., 2017b), NeuralSym (Segler & Waller, 2017), SCROP (Zheng et al., 2019), LV-Transformer (Chen et al., 2019), GraphRetro (Somnath et al., 2021), MEGAN (Sacha et al., 2021), and MHNreact (Seidl et al., 2021). As the retrosynthesis task is quite complex, subtle implementation differences or mistakes may cause critical performance fluctuations. We prefer comparing SemiReto with opensource methods whose results are more reliable. Energy-based approaches (Sun et al., 2020) are ignored because they are more like plug-and-play training strategies and result filters, focusing on enhancing existing models. For simplicity, we leave the use of energy function in the future and concentrate on comparing original retrosynthesis models.
Metrics This paper uses consistent metrics derived from previous literature for both submodule and overall performance. 1). Center identification: We report the accuracy of breaking input product into synthons. 2). Synthon completion: We present the accuracy of generating reactants from ground truth input synthons. When a product has multiple synthons, the final prediction is correct if and only if all reactants are correct. 3). Retrosynthesis: The metric is similar to that of synthon completion, except that the input synthons are also predicted by center identification. In other words, the retrosynthesis is correct if and only if both center identification and synthon completion are correct. Since there may be multiple valid routes for synthesizing a product, we report top-k accuracy.
Implementation details Thanks to the elegant implementation of G2G (Shi et al., 2020), we can develop our SemiRetro in a unified PyTorch framework (Paszke et al., 2019), namely TorchDrug. We use the same data preprocessing algorithm and code framework as G2G in center identification, ensuring the only difference is the GNN used for feature extraction. Besides, we use the opensource cheminformatics software RDkit (Landrum, 2016) to preprocess molecules and SMILES strings. The graph feature extractor consists of 6 stacked DRGAT, with the embedding size 256 for each layer. We train the proposed model for 30 and 100 epochs in center identification and synthon completion with batch size 64 on a single NVIDIA V100 GPU. The training costs of different methods can be found in the appendix. We run all experiments three times and report the means of their performance in default. To avoid the label leakage Yan et al. (2020); Somnath et al. (2021), we ignore atom mapping numbers during the training and evaluation phases.
5.2 CENTER IDENTIFICATION (Q1)
Objective and setting This section studies how much center identification performance gain can be obtained from the proposed DRGAT, especially when the reaction class is unknown. The data preprocessing, training, and evaluating process is similar to G2G. The primary difference between our SemiRetro and G2G is the graph feature extractor, where we use DRGAT while G2G uses RGCN
(Schlichtkrull et al., 2018). We trained our model up to 30 epochs, which occupied about 2940 MB of GPU memory, where the batch size is 64, and the learning rate is 1e-5.
Results and analysis 1) Highest accuracy: As shown in 2, the proposed SemiRetro outperforms baselines on all the cases with different k. For example, SemiRetro achieves the highest top-1, top2, top-3, top-5 accuracy on both reaction class known and unknown settings. 2) Better potential: Since the possible synthesis routes toward a product may be multiple, the top-k accuracy (k > 1) is important, and the performance gain of SemiRetro rises as k increases, indicating the better potential. In particular, SemiRetro achieves nearly perfect top-5 accuracy on the setting of reaction class known (acc = 99.4%) and unknown (acc = 98.4%). 3) Better adaptability: In a more general and complex case, where the reaction class is unknown, SemiRetro can significantly exceed SOTA methods. For example, SemiRetro outperforms competitors by at least 10% on the top-3 and top5 accuracy. These results show that the center identification performance has been dramatically improved using DRGAT, which is a good first step towards accurate retrosynthesis prediction.
5.3 SYNTHON COMPLETION (Q2)
Objective and setting This section reveals the effectiveness of using semi-template in three folds: 1) reducing the template redundancy, 2) improving the accuracy, and 3) promoting interpretability and training efficiency. Firstly, we count the full-templates of GLN and semi-templates introduced in this paper. We visualize the distribution and coverage of top-k templates for analyzing the redundancy. Secondly, we present the accuracy of synthon completion with ground truth synthon inputs. The final reactants are obtained by predicting the semi-templates and applying the residual attachment algorithm. Thirdly, we compare the interpretability and training efficiency of different methods in short. We trained our model up to 100 epochs, which occupied about 2320 MB of GPU memory, where the batch size is 64, and the learning rate is 1e-4.
Results and analysis (1) Reduce redundancy: In Fig. 5, we show the distribution and coverage of top-k full-templates and semi-templates, where the former distribution is sharper than the latter, indicating a higher top-k coverage. For example, the top-150 semi-templates cover the case of 96.87%, while the full-templates only cover 39.64%. Using semi-templates can reduce 11,647 fulltemplates into 150 semi-templates and increase the cover rate from 93.3% to 96.87%. (2) Higher accuracy As shown in Table. 3, SemiRetro outperforms G2G in most cases. This improvement comes from two parts: Firstly, semi-templates reduce the difficulty of predicting residual structures; Secondly, semi-templates eliminate the problem of residual attachment by using a deterministic algorithm. When the reaction class is unknown, the improvement is more dramatic. (3) More interpretable and efficient By using semi-templates, the residual attachment process is controllable and interpretable. In addition, our model can be trained at least 8 times faster than previous synthon completion models such as GLN, G2G, and RetroXpert, seeing the appendix for details.
5.4 RETROSYNTHESIS (Q3) Objective and setting We explain how to combine center identification and synthon completion to provide end-to-end retrosynthesis predictions. We use a probability tree to search the top-k results, seeing Fig. 6, where the probability product of two-step predictions is used to rank these results.
Results and analysis (1) Higher accuracy: SemiRetro achieves the highest accuracy in most settings, seeing Table. 4. As to previous works with open source code, template-free G2G and RetroXpert are more scalable than template-based GLN while sacrificing the top-1 accuracy. We use semi-template to reduce the template redundancy and improve the accuracy simultaneously. (2) Better adaptability We observe the performance gain of SemiRetro increases when the reaction class is unknown, suggesting our method can work better in more general and complex cases.

heading: 6 CONCLUSION
text: We propose SemiRetro for retrosynthesis prediction, which achieves better accuracy and attractive scalability than previous methods. Specifically, the DRGAT achieves the highest center identification accuracy. The semi-template improves both the accuracy and scalability of synthon completion. Moreover, SemiRetro has favorable interpretability and training efficiency. We hope this work will promote the development of deep retrosynthesis prediction.

heading: A APPENDIX
text: Center identification We show top-2 center identification predictions in Fig. 7, where synthons are obtained from breaking edge centers for downstream synthon completion. We present the probability of each prediction where the total probability of top-2 predictions exceeds 98%, indicating strong inductive confidence. Since the top-2 predictions are accurate enough, seeing Table. 2, we use them for synthon completion.
Synthon completion In Fig. 8, we present the process of predicting multiple reactants of the same product. This process provides an end-to-end view of synthon completion, containing semitemplate prediction, top-k results search, and semi-template application. By default, we choose the top-5 synthon completion results for each center identification output as part of the final top-10 retrosynthesis results.
Platform The platform for our experiment is ubuntu 18.04, with a Intel® Xeon® Gold 6240R Processor and 256GB memory. We use a single NVIDIA V100 to train models, where the CUDA version is 10.2.
Important details We follow the setting of G2Gs, which provides open-source code on https://github.com/DeepGraphLearning/torchdrug/. G2Gs use different atom features in their opensource code for center identification and synthon completion. In this paper, we use the same atom features of G2Gs. We have also tried to combine all these atom features and use the same set of features in center identification and synthon completion models. The combined atom features do not make a significant difference.

Review summaries:
* This paper is interested in the problem of one-step retrosynthesis, i.e., predicting the reactants that formed a given product. * For this task, the authors propose a new model (SemiRetro) that combines ideas from previous template-based (TB) and template-free (TF) methods.  * SemiRetro consists of two parts:   	i. First, DRGAT (a new GNN architecture based on edge attention) predicts the reaction center, and breaks down the product molecule into synthons.   	ii. Second, "semi-templates" (chosen from a fixed set by a classification model) is used to complete the synthons and assemble the reactants. * The authors show: 	1. DRGAT works much better than previous methods for identifying reaction centers (specifically the R-GCN used in Shi et al. (2020)'s G2G) -- see Table 2. 	2. That the use of semi-templates allows SemiRetro to cover a wider range of reactions than previous template-based methods while also using less unique templates -- see Figure 5. 	3. SemiRetro outperforms many previous TB and TF methods in terms of top-k accuracy, particularly in the reaction class unknown case -- see Table 4. 

This paper studies a retrosynthesis problem of chemical reactions.  This paper proposes a new variant of retrosynthesis models, with mainly two new components: DRGAT for powerful embedding computation and the semi-templates for accurate synthon completions.  Especially, the propose semi-template takes the local (neighboring) bonding structure into consideration. This significantly reduces the redundancy of the templates, allowing connecting the template-based retrosynthesis (less scalable but powerful) and the template-free retrosynthesis (scalable but less powerful).  The experimental validations show the efficacy of the new components for each subtask, and the final retrosynthesis performances exceeds the existing retrosynthesis models. 

This paper presents a novel template-based retrosynthesis prediction method. The authors reduce template redundancy and obtain 150 abstract semi-templates, which can cover 96.9% of data samples. Besides, the authors propose directed relational graph attention to find the reaction center. Experiments show the proposed method can achieve superior retrosynthesis performance.