Original: S EMI R ETRO : S EMI - TEMPLATE FRAMEWORK BOOSTS DEEP RETROSYNTHESIS PREDICTION
Revised: S EMI R ETRO : S EMI - TEMPLATE FRAMEWORK BOOSTS DEEP RETROSYNTHESIS PREDICTION

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Retrosynthesis brings scientiﬁc and societal beneﬁts by inferring possible reaction routes toward novel molecules.
Revised: Retrosynthesis brings scientiﬁc and societal beneﬁts by inferring possible reaction routes toward novel molecules.

Original: Recently, template-based (TB) and templatefree (TF) molecule graph learning methods have shown promising results to solve this problem.
Revised: Recently, template-based (TB) and templatefree (TF) molecule graph learning methods have shown promising results to solve this problem.

Original: 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.
Revised: 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.

Original: 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.
Revised: 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.

Original: Since many semitemplates are reduplicative, the template redundancy can be reduced while the essential chemical knowledge is still preserved to facilitate synthon completion.
Revised: Since many semitemplates are reduplicative, the template redundancy can be reduced while the essential chemical knowledge is still preserved to facilitate synthon completion.

Original: We call our method SemiRetro and introduce a directed relational graph attention (DRGAT) layer to extract expressive features for better center identiﬁcation.
Revised: We call our method SemiRetro and introduce a directed relational graph attention (DRGAT) layer to extract expressive features for better center identiﬁcation.

Original: Experimental results show that SemiRetro signiﬁcantly outperforms both existing
Revised: Experimental results show that SemiRetro signiﬁcantly outperforms both existing

Original: TB and TF methods.
Revised: TB and TF methods.

Original: In scalability, SemiRetro covers 96.9% data using 150 semitemplates, while previous template-based GLN requires 11,647 templates to cover 93.3% data.
Revised: In scalability, SemiRetro covers 96.9% data using 150 semitemplates, while previous template-based GLN requires 11,647 templates to cover 93.3% data.

Original: In top-1 accuracy, SemiRetro exceeds template-free G2G 3.4% (class known) and 6.4% (class unknown).
Revised: In top-1 accuracy, SemiRetro exceeds template-free G2G 3.4% (class known) and 6.4% (class unknown).

Original: Besides, SemiReto has better interpretability and training efﬁciency than existing methods.
Revised: Besides, SemiReto has better interpretability and training efﬁciency than existing methods.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: 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.
Revised: 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.

Original: 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.
Revised: 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.

Original: Fortunately, with the rapid accumulation of chemical data, machine learning is promising to solve this problem (Coley et al., 2018; Segler et al., 2018).
Revised: Fortunately, with the rapid accumulation of chemical data, machine learning is promising to solve this problem (Coley et al., 2018; Segler et al., 2018).

Original: In this paper, we focus on the single-step version 1 : predicting the reactants from a given product.
Revised: In this paper, we focus on the single-step version 1 : predicting the reactants from a given product.

Original: 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;
Revised: 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;

Original: Karpov et al., 2019) methods.
Revised: Karpov et al., 2019) methods.

Original: Generally, TB methods achieve high accuracy by leveraging reaction templates, which encode the molecular changes during the reaction.
Revised: Generally, TB methods achieve high accuracy by leveraging reaction templates, which encode the molecular changes during the reaction.

Original: However, the usage of templates brings some shortcomings, such as high computation cost and incomplete rule coverage, limiting the scalability.
Revised: However, the usage of templates brings some shortcomings, such as high computation cost and incomplete rule coverage, limiting the scalability.

Original: To improve the scalability, a class of chemically inspired TF methods (Shi et al., 2020;
Revised: To improve the scalability, a class of chemically inspired TF methods (Shi et al., 2020;

Original: Yan et al., 2020) (see Fig. 1) have achieved dramatical success, which decompose retrosynthesis into subproblems: i) center identiﬁcation and ii) synthon completion .
Revised: Yan et al., 2020) (see Fig. 1) have achieved dramatical success, which decompose retrosynthesis into subproblems: i) center identiﬁcation and ii) synthon completion .

Original: Center identiﬁcation increases the model scalability by breaking down the target molecule into virtual synthons without utilizing templates.
Revised: Center identiﬁcation increases the model scalability by breaking down the target molecule into virtual synthons without utilizing templates.

Original: 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.
Revised: 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.

Original: Although various TF methods have been proposed, the top- k retrosynthesis accuracy remains poor, especially when the reaction class is unknown.
Revised: Although various TF methods have been proposed, the top- k retrosynthesis accuracy remains poor, especially when the reaction class is unknown.

Original: Can we ﬁnd a more accurate way to predict potential reactants while keeping the scalability?
Revised: Can we ﬁnd a more accurate way to predict potential reactants while keeping the scalability?

Original: To address the aforementioned problem, we suggest combining the advantages of TB and TF approaches, and propose a novel framework namely SemiRetro.
Revised: To address the aforementioned problem, we suggest combining the advantages of TB and TF approaches, and propose a novel framework namely SemiRetro.

Original: Speciﬁcally, we break a full-template into several simpler semi-templates and embed them into the two-step TF framework.
Revised: Speciﬁcally, we break a full-template into several simpler semi-templates and embed them into the two-step TF framework.

Original: As many semi-templates are reduplicative, the template redundancy can be reduced while the essential chemical knowledge is still preserved to facilitate synthon completion.
Revised: As many semi-templates are reduplicative, the template redundancy can be reduced while the essential chemical knowledge is still preserved to facilitate synthon completion.

Original: Moreover, we introduce a directed relational graph attention (DRGAT) layer to extract more expressive molecular features to improve center identiﬁcation accuracy.
Revised: Moreover, we introduce a directed relational graph attention (DRGAT) layer to extract more expressive molecular features to improve center identiﬁcation accuracy.

Original: Finally, we combine the center identiﬁcation and synthon completion modules in a uniﬁed framework to accomplish retrosynthesis predictions.
Revised: Finally, we combine the center identiﬁcation and synthon completion modules in a uniﬁed framework to accomplish retrosynthesis predictions.

Original: 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.
Revised: 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.

Original: We show that SemiRetro signiﬁcantly outperforms these methods.
Revised: We show that SemiRetro signiﬁcantly outperforms these methods.

Original: 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.
Revised: 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.

Original: In top-1 accuracy,
Revised: In top-1 accuracy,

Original: SemiRetro exceeds template-free G2G 3.4% (class known) and 6.4% (class unknown).
Revised: SemiRetro exceeds template-free G2G 3.4% (class known) and 6.4% (class unknown).

Original: Owing to the semi-template, SemiReto is more interpretable than template-free G2G and RetroXpert in synthon completion.
Revised: Owing to the semi-template, SemiReto is more interpretable than template-free G2G and RetroXpert in synthon completion.

Original: Moreover, SemiRetro trains at least 6 times faster than G2G, RetroXpert, and GLN.
Revised: Moreover, SemiRetro trains at least 6 times faster than G2G, RetroXpert, and GLN.

Original: All these results show that the proposed SemiRetro boosts the scalability and accuracy of deep retrosynthesis prediction.
Revised: All these results show that the proposed SemiRetro boosts the scalability and accuracy of deep retrosynthesis prediction.

Original:  R ELATED WORK
Revised:  R ELATED WORK

Original: Template-based models TB methods infer reactants from the product through shared chemical transformation patterns, namely reaction templates.
Revised: Template-based models TB methods infer reactants from the product through shared chemical transformation patterns, namely reaction templates.

Original: These templates are either hand-crafted by human experts (Hartenfeller et al., 2011; Szymku ´ c et al., 2016) or automatically extracted by algorithms (Coley et al., 2017a; Law et al., 2009).
Revised: These templates are either hand-crafted by human experts (Hartenfeller et al., 2011; Szymku ´ c et al., 2016) or automatically extracted by algorithms (Coley et al., 2017a; Law et al., 2009).

Original: 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.
Revised: 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.

Original: To handle those challenges, (Coley et al., 2017b) suggests sharing the same templates among similar products.
Revised: To handle those challenges, (Coley et al., 2017b) suggests sharing the same templates among similar products.

Original: (Segler &
Revised: (Segler &

Original: Waller, 2017; Baylon et al., 2019) employ neural models for template selection with molecule ﬁngerprint as input.
Revised: Waller, 2017; Baylon et al., 2019) employ neural models for template selection with molecule ﬁngerprint as input.

Original: 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.
Revised: 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.

Original: TB methods are interpretable and accurate because they embed rich chemical knowledge into the algorithm.
Revised: TB methods are interpretable and accurate because they embed rich chemical knowledge into the algorithm.

Original: However, the vast space of templates and incomplete coverage severely limit their scalability.
Revised: However, the vast space of templates and incomplete coverage severely limit their scalability.

Original: Template-free models Instead of explicitly using templates
Revised: Template-free models Instead of explicitly using templates

Original: , TF approaches learn chemical transformations by the model.
Revised: , TF approaches learn chemical transformations by the model.

Original: (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.
Revised: (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.

Original: Despite the convenience of modeling,
Revised: Despite the convenience of modeling,

Original: SMILES cannot fully utilize the inherent chemical structures and may generate invalid SMILES strings.
Revised: SMILES cannot fully utilize the inherent chemical structures and may generate invalid SMILES strings.

Original: Therefore, (Zheng et al., 2019) propose a self-corrected transformer to ﬁx the syntax errors of candidate reactants.
Revised: Therefore, (Zheng et al., 2019) propose a self-corrected transformer to ﬁx the syntax errors of candidate reactants.

Original: Recently, G2G (Shi et al., 2020), RetroXpert (Yan et al., 2020) and
Revised: Recently, G2G (Shi et al., 2020), RetroXpert (Yan et al., 2020) and

Original: GraphRetro (Somnath et al., 2020) achieve state-of-the-art performance by decomposing the retrosynthesis into two sub-problems: i) center identiﬁcation and ii) synthon completion, as shown in
Revised: 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

Original: Fig.
Revised: Fig.

Original: 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.
Revised: 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.

Original: Synthon completion simpliﬁes the complexity of reactant generation by taking synthons as potential starting molecules.
Revised: Synthon completion simpliﬁes the complexity of reactant generation by taking synthons as potential starting molecules.

Original: For example, RetroXpert and G2G treat it as a SMILES or graph sequence translation problem from synthon to reactant.
Revised: For example, RetroXpert and G2G treat it as a SMILES or graph sequence translation problem from synthon to reactant.

Original: 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.
Revised: 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.

Original: Generally, these TF methods are more scalable but perform worse than TB GLN in top-1 accuracy.
Revised: Generally, these TF methods are more scalable but perform worse than TB GLN in top-1 accuracy.

Original: Challenges Although the two-step TF framework signiﬁcantly improves the algorithm’s scalability, the overall accuracy is relatively low.
Revised: Challenges Although the two-step TF framework signiﬁcantly improves the algorithm’s scalability, the overall accuracy is relatively low.

Original: A possible solution to this issue is to enhance submodules, i.e., center identiﬁcation and synthon completion.
Revised: A possible solution to this issue is to enhance submodules, i.e., center identiﬁcation and synthon completion.

Original: 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.
Revised: 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.

Original: How to develop a more suitable model that works well with unknown classes is the ﬁrst challenge.
Revised: How to develop a more suitable model that works well with unknown classes is the ﬁrst challenge.

Original: In addition, synthon completion is the major bottleneck affecting the overall accuracy.
Revised: In addition, synthon completion is the major bottleneck affecting the overall accuracy.

Original: 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
Revised: 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

Original: [Figure]
Revised: [Figure]

Original: and various residuals may agree with the same synthon (e
Revised: and various residuals may agree with the same synthon (e

Original: [Figure]
Revised: [Figure]

Original: g., F, CI, Br, and
Revised: g., F, CI, Br, and

Original: I have similar chemical properties).
Revised: I have similar chemical properties).

Original: For researchers, scalability, interpretability, and training efﬁciency are also important.
Revised: For researchers, scalability, interpretability, and training efﬁciency are also important.

Original: How to develop a more accurate, interpretable, and efﬁcient synthon completion model while maintaining the scalability is the second challenge
Revised: How to develop a more accurate, interpretable, and efﬁcient synthon completion model while maintaining the scalability is the second challenge

Original: [Figure]
Revised: [Figure]

Original:  D EFINITION AND O VERVIEW
Revised:  D EFINITION AND O VERVIEW

Original: Molecule representation There are two types of dominant representations, i.e., SMILES string (Weininger, 1988) and molecular graph.
Revised: Molecule representation There are two types of dominant representations, i.e., SMILES string (Weininger, 1988) and molecular graph.

Original: 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.
Revised: 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.

Original: Many NLP models can be directly applied to solve related problems in an end-to-end fashion.
Revised: Many NLP models can be directly applied to solve related problems in an end-to-end fashion.

Original: However, these models cannot guarantee the chemical correctness of the output molecules because they ignore structure information to some extent.
Revised: However, these models cannot guarantee the chemical correctness of the output molecules because they ignore structure information to some extent.

Original: Similar to recent breakthroughs (Dai et al., 2019; Shi et al., 2020; Yan et al., 2020; Somnath et al., 2020), we take the molecule as a labeled graph G
Revised: 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

Original: ( A, X, E ) , where A , X and E are adjacency matrix, atom features and bond features, seeing
Revised: ( A, X, E ) , where A , X and E are adjacency matrix, atom features and bond features, seeing

Original: Table.
Revised: Table.

Original: Under the graph framework, we can effectively apply chemical constraints to ensure the validity of output molecules.
Revised: Under the graph framework, we can effectively apply chemical constraints to ensure the validity of output molecules.

Original: Besides, graph-based methods are more controllable and interpretable than SMILES-based ones.
Revised: Besides, graph-based methods are more controllable and interpretable than SMILES-based ones.

Original: Problem deﬁnition Retrosynthesis aims to infer the set of reactants {R i } Ni =1 that can generate the product P .
Revised: Problem deﬁnition Retrosynthesis aims to infer the set of reactants {R i } Ni =1 that can generate the product P .

Original: Formally, that is to learn a mapping function F θ :
Revised: Formally, that is to learn a mapping function F θ :

Original: [Equation]
Revised: [Equation]

Original: 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.
Revised: 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.

Original: Overview As shown in Fig.
Revised: Overview As shown in Fig.

Original: [Table]
Revised: [Table]

Original: we adopt the two-step TF framework due to its scalability and effectiveness.
Revised: we adopt the two-step TF framework due to its scalability and effectiveness.

Original: 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
Revised: 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

Original: [Table]
Revised: [Table]

Original: which signiﬁcantly reduces the problem complexity
Revised: which signiﬁcantly reduces the problem complexity

Original: [Table]
Revised: [Table]

Original: [Table]
Revised: [Table]

Original:  M ETHODOLOGY 4. C ENTER IDENTIFICATION
Revised:  M ETHODOLOGY 4. C ENTER IDENTIFICATION

Original: Center identiﬁcation plays a vital role in the two-step retrosynthesis because errors caused by this step directly lead to the ﬁnal failures.
Revised: Center identiﬁcation plays a vital role in the two-step retrosynthesis because errors caused by this step directly lead to the ﬁnal failures.

Original: 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.
Revised: 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.

Original: How to obtain comprehensive and accurate center identiﬁcation results is still worth exploring
Revised: How to obtain comprehensive and accurate center identiﬁcation results is still worth exploring

Original: [Table]
Revised: [Table]

Original: Reaction centers We consider both atom centers and bond centers in the product molecule.
Revised: Reaction centers We consider both atom centers and bond centers in the product molecule.

Original: As shown in Fig.
Revised: As shown in Fig.

Original: 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).
Revised: 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).

Original: Both these atoms and bonds are called reaction centers.
Revised: Both these atoms and bonds are called reaction centers.

Original: [Figure]
Revised: [Figure]

Original: Directed relational GAT Commonly used graph neural networks (Defferrard et al., 2016; Kipf & Welling, 2016; Veliˇckovi´c et al., 2017) mainly focus on 0 and 1 edges, ignoring edge direction and multiple types, thus failing to capture expressive molecular features.
Revised: Directed relational GAT Commonly used graph neural networks (Defferrard et al., 2016; Kipf & Welling, 2016; Veliˇckovi´c et al., 2017) mainly focus on 0 and 1 edges, ignoring edge direction and multiple types, thus failing to capture expressive molecular features.

Original: As to molecules, different bonds represent different interatomic interactions, resulting in a multi-relational graph.
Revised: As to molecules, different bonds represent different interatomic interactions, resulting in a multi-relational graph.

Original: Meanwhile, atoms at the end of the same bond may gain or lose electrons differently, leading to directionality.
Revised: Meanwhile, atoms at the end of the same bond may gain or lose electrons differently, leading to directionality.

Original: Considering these factors, we propose a directed relational graph attention (DRGAT) layer based on the general information propagation framework Zheng et al.
Revised: Considering these factors, we propose a directed relational graph attention (DRGAT) layer based on the general information propagation framework Zheng et al.

Original: (2021), as shown in Fig.
Revised: (2021), as shown in Fig.

Original: 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.
Revised: 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.

Original: 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.
Revised: 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.

Original: [Figure]
Revised: [Figure]

Original: 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 reactant R i .
Revised: 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 reactant R i .

Original: We denote the atom center as c i ∈ { 0 , 1 } and bond center as c i,j ∈ { 0 , 1 } in the product P .
Revised: We denote the atom center as c i ∈ { 0 , 1 } and bond center as c i,j ∈ { 0 , 1 } in the product P .

Original: During the learning process, atoms features { h i } |P| i =1 are learned from the product
Revised: During the learning process, atoms features { h i } |P| i =1 are learned from the product

Original: P by applying stacked DRGAT, and the input bond features are { e i,j | a i,j = 1 } .
Revised: P by applying stacked DRGAT, and the input bond features are { e i,j | a i,j = 1 } .

Original: Then, we get the representations of atom i and bond ( i, j ) as
Revised: Then, we get the representations of atom i and bond ( i, j ) as

Original: [Equation]
Revised: [Equation]

Original: and
Revised: and

Original: [Equation]
Revised: [Equation]

Original: where we use average Readout operation, and || is vector concatenation operation.
Revised: where we use average Readout operation, and || is vector concatenation operation.

Original: Further, we predict the atom center probability p i and bond center probability p i,j via MLPs:
Revised: Further, we predict the atom center probability p i and bond center probability p i,j via MLPs:

Original: [Equation]
Revised: [Equation]

Original: ) and
Revised: ) and

Original: [Equation]
Revised: [Equation]

Original: Finally, center identiﬁcation can be reduced to a binary classiﬁcation, whose loss function is:
Revised: Finally, center identiﬁcation can be reduced to a binary classiﬁcation, whose loss function is:

Original: [Equation]
Revised: [Equation]

Original: In summary, we propose a directed relational graph attention (DRGAT) layer to learn expressive atom and bond features for accurate center identiﬁcation prediction.
Revised: In summary, we propose a directed relational graph attention (DRGAT) layer to learn expressive atom and bond features for accurate center identiﬁcation prediction.

Original: We consider both atom center and bond center to provide comprehensive results.
Revised: We consider both atom center and bond center to provide comprehensive results.

Original: In section.
Revised: In section.

Original: 5.2, we show that our method can achieve state-of-the-art accuracy, especially with reaction class unknown.
Revised: 5.2, we show that our method can achieve state-of-the-art accuracy, especially with reaction class unknown.

Original: 4. S YNTHON COMPLETION
Revised: 4. S YNTHON COMPLETION

Original: Synthon completion is the main bottleneck of two-step TF retrosynthesis, which is responsible for predicting and attaching residuals for each synthon.
Revised: Synthon completion is the main bottleneck of two-step TF retrosynthesis, which is responsible for predicting and attaching residuals for each synthon.

Original: 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.
Revised: 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.

Original: Because of these complexities, previous synthon completion approaches are usually inaccurate, unexplainable, and cumbersome.
Revised: Because of these complexities, previous synthon completion approaches are usually inaccurate, unexplainable, and cumbersome.

Original: Introducing the necessary chemical knowledge to improve interpretability and accuracy can be a promising solution.
Revised: Introducing the necessary chemical knowledge to improve interpretability and accuracy can be a promising solution.

Original: However, how to provide attractive scalability and training efﬁciency is a new challenge.
Revised: However, how to provide attractive scalability and training efﬁciency is a new challenge.

Original: Semi-templates The semi-template used in this paper is the local reaction pattern of each synthon, as shown in Fig.
Revised: Semi-templates The semi-template used in this paper is the local reaction pattern of each synthon, as shown in Fig.

Original: Different from GraphRetro (Somnath et al., 2020), our semi-template contains not only residuals but also synthon’s local structure.
Revised: Different from GraphRetro (Somnath et al., 2021), our semi-template contains not only residuals but also synthon’s local structure.

Original: Similar to the work of forward reaction prediction ( ? ), semi-template splits a binary reaction into two half reactions.
Revised: Similar to the work of forward reaction prediction (Segler & Waller, 2016), semi-template splits a binary reaction into two half reactions.

Original: Notably, we use dummy atom ∗ to represent possible synthon atoms that match the semi-template, signiﬁcantly reducing redundancy.
Revised: Notably, we use dummy atom ∗ to represent possible synthon atoms that match the semi-template, signiﬁcantly reducing redundancy.

Original: We extract semi-template from each synthon-reactant pair by removing reactant atoms that have exact matches in the synthon.
Revised: We extract semi-template from each synthon-reactant pair by removing reactant atoms that have exact matches in the synthon.

Original: There are two interesting observations: 1) Topsemi-templates cover 96.9% samples; 2) Reactants can be deterministically generated from semitemplates and synthons (introduced later).
Revised: 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).

Original: Based on these observations, synthon completion can be further simpliﬁed as a classiﬁcation problem.
Revised: Based on these observations, synthon completion can be further simpliﬁed as a classiﬁcation problem.

Original: In other words, we need to predict the semi-template type for each synthon, and the total number of classes is 150+1.
Revised: In other words, we need to predict the semitemplate type for each synthon, and the total number of classes is 150+1.

Original: The ﬁrst 150 classes are topsemi-templates, and the 151st class indicates uncovered classes.
Revised: The ﬁrst 150 classes are top-150 semi-templates, and the 151st class indicates uncovered classes.

Original: [Figure]
Revised: [Figure]

Original: Learning semi-templates For each synthon S j , denote its semi-template label as t j , 1 ≤ t j ≤ 151 .
Revised: Learning semi-templates For each synthon S j , denote its semi-template label as t j , 1 ≤ t j ≤ 151 .

Original: We use stacked DRGATs to extract atom features { h i }
Revised: We use stacked DRGATs to extract atom features { h i }

Original: |S j | i
Revised: |S j | i

Original: =1 from S j .
Revised: =1 from S j .

Original: In each synthon, atoms that exactly match the semi-template are called supporting atoms, seeing circled atoms in Fig.
Revised: In each synthon, atoms that exactly match the semi-template are called supporting atoms, seeing circled atoms in Fig.

Original: Given the supporting atom set { i 0 , i 1 , · · · } , the semi-template representation is:
Revised: Given the supporting atom set { i 0 , i 1 , · · · } , the semi-template representation is:

Original: [Equation]
Revised: [Equation]

Original: Based on this representation, we can get the predicted semi-template ˆ t j as:
Revised: Based on this representation, we can get the predicted semi-template ˆ t j as:

Original: [Equation]
Revised: [Equation]

Original: Denote 1 { c } ( · ) as the indicator function, the cross-entropy loss used for training is:
Revised: Denote 1 { c } ( · ) as the indicator function, the cross-entropy loss used for training is:

Original: [Equation]
Revised: [Equation]

Original: Applying semi-templates Once reaction centers, synthons, and corresponding semi-templates are known, we can deduce reactants with almost 100% accuracy.
Revised: Applying semi-templates Once reaction centers, synthons, and corresponding semi-templates are known, we can deduce reactants with almost 100% accuracy.

Original: This is not a theoretical claim; We provide a practical residual attachment algorithm in the appendix and open source code on GitHub.
Revised: This is not a theoretical claim; We provide a practical residual attachment algorithm in the appendix and open source code on GitHub.

Original: In summary, we suggest using the semi-templates to improve synthon completion performance.
Revised: In summary, we suggest using the semi-templates to improve synthon completion performance.

Original: Firstly, reducing this complex task to a classiﬁcation problem helps promote training efﬁciency and accuracy.
Revised: Firstly, reducing this complex task to a classiﬁcation problem helps promote training efﬁciency and accuracy.

Original: Secondly, the high coverage of semi-templates signiﬁcantly enhanced the scalability of
Revised: Secondly, the high coverage of semi-templates signiﬁcantly enhanced the scalability of

Original: TB methods.
Revised: TB methods.

Original: Thirdly, the deterministic residual attachment algorithm improves interpretability and accuracy.
Revised: Thirdly, the deterministic residual attachment algorithm improves interpretability and accuracy.

Original: In section.
Revised: In section.

Original: 5.3, we will show the effectiveness of the proposed method.
Revised: 5.3, we will show the effectiveness of the proposed method.

Original:  E XPERIMENTS
Revised:  E XPERIMENTS

Original: 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.
Revised: 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.

Original: The effectiveness of the proposed method is evaluated by systematic experiments, which focus on answering these questions:
Revised: The effectiveness of the proposed method is evaluated by systematic experiments, which focus on answering these questions:

Original: • Q1:
Revised: • Q1:

Original: For center identiﬁcation (CI), how much performance gain can be obtained from DRGAT?
Revised: For center identiﬁcation (CI), how much performance gain can be obtained from DRGAT?

Original: • Q2:
Revised: • Q2:

Original: For synthon completion (SC), can semi-templates reduce template redundancy and improve the synthon completion performance?
Revised: For synthon completion (SC), can semi-templates reduce template redundancy and improve the synthon completion performance?

Original: And why?
Revised: And why?

Original: • Q3:
Revised: • Q3:

Original: For retrosynthesis, how do we integrate CI and SC models into a uniﬁed retrosynthesis framework?
Revised: For retrosynthesis, how do we integrate CI and SC models into a uniﬁed retrosynthesis framework?

Original: Can SemiRetro outperform existing template-based and template-free methods?
Revised: Can SemiRetro outperform existing template-based and template-free methods?

Original: 5. B ASIC SETTING
Revised: 5. B ASIC SETTING

Original: Data We evaluate SemiRetro on the widely used benchmark dataset USPTO-50k (Schneider et al., 2016) to show its effectiveness.
Revised: Data We evaluate SemiRetro on the widely used benchmark dataset USPTO-50k (Schneider et al., 2016) to show its effectiveness.

Original: USPTO-50k contains 50k atom-mapped reactions with 10 reaction types.
Revised: USPTO-50k contains 50k atom-mapped reactions with 10 reaction types.

Original: Following (Dai et al., 2019; Shi et al., 2020; Yan et al., 2020), the training/validation/test splits is 8:1:1.
Revised: Following (Dai et al., 2019; Shi et al., 2020; Yan et al., 2020), the training/validation/test splits is 8:1:1.

Original: 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.
Revised: 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.

Original: In our graph-based methods, this shortcut will not be introduced by forbidding atom position encoding.
Revised: In our graph-based methods, this shortcut will not be introduced by forbidding atom position encoding.

Original: Baselines Template-based GLN (Dai et al., 2019), template-free G2G (Shi et al., 2020) and
Revised: Baselines Template-based GLN (Dai et al., 2019), template-free G2G (Shi et al., 2020) and

Original: 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.
Revised: 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.

Original: To show broad superiority, we also comapre SemiRetro with other baselines, incuding RetroSim (Coley et al., 2017b),
Revised: To show broad superiority, we also comapre SemiRetro with other baselines, incuding RetroSim (Coley et al., 2017b),

Original: NeuralSym (Segler & Waller, 2017), SCROP (Zheng et al., 2019), LV-Transformer (Chen et al., 2019), GraphRetro (Somnath et al., 2020), MEGAN (Sacha et al., 2021), and MHNreact (Seidl et al., 2021).
Revised: 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).

Original: As the retrosynthesis task is quite complex, subtle implementation differences or mistakes may cause critical performance ﬂuctuations.
Revised: As the retrosynthesis task is quite complex, subtle implementation differences or mistakes may cause critical performance ﬂuctuations.

Original: We prefer comparing SemiReto with opensource methods whose results are more reliable.
Revised: We prefer comparing SemiReto with opensource methods whose results are more reliable.

Original: 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.
Revised: 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.

Original: For simplicity, we leave the use of energy function in the future and concentrate on comparing original retrosynthesis models.
Revised: For simplicity, we leave the use of energy function in the future and concentrate on comparing original retrosynthesis models.

Original: Metrics This paper uses consistent metrics derived from previous literature for both submodule and overall performance. 1).
Revised: Metrics This paper uses consistent metrics derived from previous literature for both submodule and overall performance. 1).

Original: Center identiﬁcation : We report the accuracy of breaking input product into synthons. 2).
Revised: Center identiﬁcation : We report the accuracy of breaking input product into synthons. 2).

Original: Synthon completion : We present the accuracy of generating reactants from ground truth input synthons.
Revised: Synthon completion : We present the accuracy of generating reactants from ground truth input synthons.

Original: When a product has multiple synthons, the ﬁnal prediction is correct if and only if all reactants are correct.
Revised: When a product has multiple synthons, the ﬁnal prediction is correct if and only if all reactants are correct.

Original: Retrosynthesis : The metric is similar to that of synthon completion, except that the input synthons are also predicted by center identiﬁcation.
Revised: Retrosynthesis : The metric is similar to that of synthon completion, except that the input synthons are also predicted by center identiﬁcation.

Original: In other words, the retrosynthesis is correct if and only if both center identiﬁcation and synthon completion are correct.
Revised: In other words, the retrosynthesis is correct if and only if both center identiﬁcation and synthon completion are correct.

Original: Since there may be multiple valid routes for synthesizing a product, we report top- k accuracy.
Revised: Since there may be multiple valid routes for synthesizing a product, we report top- k accuracy.

Original: 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.
Revised: 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.

Original: 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.
Revised: 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.

Original: Besides, we use the opensource cheminformatics software RDkit (Landrum, 2016) to preprocess molecules and SMILES strings.
Revised: Besides, we use the opensource cheminformatics software RDkit (Landrum, 2016) to preprocess molecules and SMILES strings.

Original: The graph feature extractor consists of 6 stacked DRGAT, with the embedding size 256 for each layer.
Revised: The graph feature extractor consists of 6 stacked DRGAT, with the embedding size 256 for each layer.

Original: 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.
Revised: 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.

Original: The training costs of different methods can be found in the appendix.
Revised: The training costs of different methods can be found in the appendix.

Original: We run all experiments three times and report the means of their performance in default.
Revised: We run all experiments three times and report the means of their performance in default.

Original: To avoid the issue of information leakage, we ignore atom mapping numbers during the training and evaluation phases.
Revised: To avoid label leakage Yan et al. (2020); Somnath et al. (2021), we ignore atom mapping numbers during the training and evaluation phases.

Original: 5. C ENTER IDENTIFICATION ( Q1 )
Revised: 5. C ENTER IDENTIFICATION ( Q1 )

Original: 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.
Revised: 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.

Original: 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
Revised: 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

Original: (Schlichtkrull et al., 2018).
Revised: (Schlichtkrull et al., 2018).

Original: 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.
Revised: 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.

Original: Results and analysis 1)
Revised: Results and analysis 1)

Original: Highest accuracy : As shown in 2, the proposed SemiRetro outperforms baselines on all the cases with different k .
Revised: Highest accuracy : As shown in 2, the proposed SemiRetro outperforms baselines on all the cases with different k .

Original: For example, SemiRetro achieves the highest top-1, top2, top-3, top-5 accuracy on both reaction class known and unknown settings.
Revised: For example, SemiRetro achieves the highest top-1, top2, top-3, top-5 accuracy on both reaction class known and unknown settings.

Original: 2) Better potential :
Revised: 2) Better potential :

Original: 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.
Revised: 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.

Original: In particular, SemiRetro achieves nearly perfect top-5 accuracy on the setting of reaction class known (acc = 99.4%) and unknown (acc = 98.4%).
Revised: In particular, SemiRetro achieves nearly perfect top-5 accuracy on the setting of reaction class known (acc = 99.4%) and unknown (acc = 98.4%).

Original: 3) Better adaptability :
Revised: 3) Better adaptability :

Original: In a more general and complex case, where the reaction class is unknown, SemiRetro can signiﬁcantly exceed SOTA methods.
Revised: In a more general and complex case, where the reaction class is unknown, SemiRetro can signiﬁcantly exceed SOTA methods.

Original: For example, SemiRetro outperforms competitors by at least 10% on the top-3 and top5 accuracy.
Revised: For example, SemiRetro outperforms competitors by at least 10% on the top-3 and top5 accuracy.

Original: These results show that the center identiﬁcation performance has been dramatically improved using DRGAT, which is a good ﬁrst step towards accurate retrosynthesis prediction.
Revised: These results show that the center identiﬁcation performance has been dramatically improved using DRGAT, which is a good ﬁrst step towards accurate retrosynthesis prediction.

Original: [Table]
Revised: [Table]

Original: Improvement
Revised: Improvement

Original: [Table]
Revised: [Table]

Original: 5. S YNTHON COMPLETION ( Q2 )
Revised: 5. S YNTHON COMPLETION ( Q2 )

Original: 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.
Revised: 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.

Original: Firstly, we count the full-templates of GLN and semi-templates introduced in this paper.
Revised: Firstly, we count the full-templates of GLN and semi-templates introduced in this paper.

Original: We visualize the distribution and coverage of top- k templates for analyzing the redundancy.
Revised: We visualize the distribution and coverage of top- k templates for analyzing the redundancy.

Original: Secondly, we present the accuracy of synthon completion with ground truth synthon inputs.
Revised: Secondly, we present the accuracy of synthon completion with ground truth synthon inputs.

Original: The ﬁnal reactants are obtained by predicting the semi-templates and applying the residual attachment algorithm.
Revised: The ﬁnal reactants are obtained by predicting the semi-templates and applying the residual attachment algorithm.

Original: Thirdly, we compare the interpretability and training efﬁciency of different methods in short.
Revised: Thirdly, we compare the interpretability and training efﬁciency of different methods in short.

Original: 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.
Revised: 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.

Original: [Figure]
Revised: [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Table]
Revised: [Table]

Original: Results and analysis (1) Reduce redundancy : In Fig.
Revised: Results and analysis (1) Reduce redundancy : In Fig.

Original: 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.
Revised: 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.

Original: For example, the top-150 semi-templates cover the case of 96 .
Revised: For example, the top-150 semi-templates cover the case of 96 .

Original: 87% , while the full-templates only cover 39 .
Revised: 87% , while the full-templates only cover 39 .

Original: Using semi-templates can reduce 11,647 fulltemplates into 150 semi-templates and increase the cover rate from 93 .
Revised: Using semi-templates can reduce 11,647 fulltemplates into 150 semi-templates and increase the cover rate from 93 .

Original: 3% to 96 .
Revised: 3% to 96 .

Original: (2) Higher accuracy As shown in Table.
Revised: (2) Higher accuracy As shown in Table.

Original: 3, SemiRetro outperforms G2G in most cases.
Revised: 3, SemiRetro outperforms G2G in most cases.

Original: This improvement comes from two parts: Firstly, semi-templates reduce the difﬁculty of predicting residual structures;
Revised: This improvement comes from two parts: Firstly, semi-templates reduce the difﬁculty of predicting residual structures;

Original: Secondly, semi-templates eliminate the problem of residual attachment by using a deterministic algorithm.
Revised: Secondly, semi-templates eliminate the problem of residual attachment by using a deterministic algorithm.

Original: When the reaction class is unknown, the improvement is more dramatic.
Revised: When the reaction class is unknown, the improvement is more dramatic.

Original: (3) More interpretable and efﬁcient By using semi-templates, the residual attachment process is controllable and interpretable.
Revised: (3) More interpretable and efﬁcient By using semi-templates, the residual attachment process is controllable and interpretable.

Original: 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.
Revised: 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.

Original: 5. R ETROSYNTHESIS ( Q3 )
Revised: 5. R ETROSYNTHESIS ( Q3 )

Original: Objective and setting We explain how to combine center identiﬁcation and synthon completion to provide end-to-end retrosynthesis predictions.
Revised: Objective and setting We explain how to combine center identiﬁcation and synthon completion to provide end-to-end retrosynthesis predictions.

Original: We use a probability tree to search the top- k results, seeing Fig.
Revised: We use a probability tree to search the top- k results, seeing Fig.

Original: 6, where the probability product of two-step predictions is used to rank these results.
Revised: 6, where the probability product of two-step predictions is used to rank these results.

Original: [Figure]
Revised: [Figure]

Original: =
Revised: =

Original: [Figure]
Revised: [Figure]

Original: =
Revised: =

Original: [Figure]
Revised: [Figure]

Original: =
Revised: =

Original: [Figure]
Revised: [Figure]

Original: =
Revised: =

Original: [Figure] [Figure]
Revised: [Figure] [Figure]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: TB
Revised: TB

Original: RetroSim (Coley et al., 2017b)
Revised: RetroSim (Coley et al., 2017b)

Original: [Table]
Revised: [Table]

Original: NeuralSym (Segler & Waller, 2017)
Revised: NeuralSym (Segler & Waller, 2017)

Original: [Table] [Table]
Revised: [Table] [Table]

Original: TF
Revised: TF

Original: SCROP (Zheng et al., 2019)
Revised: SCROP (Zheng et al., 2019)

Original: [Table]
Revised: [Table]

Original: LV-Transformer (Chen et al., 2019) – – – –
Revised: LV-Transformer (Chen et al., 2019) – – – –

Original: [Table]
Revised: [Table]

Original: Shi
Revised: Shi

Original: [Table] [Table]
Revised: [Table] [Table]

Original: RetroXpert (Yan et al., 2020)
Revised: RetroXpert (Yan et al., 2020)

Original: [Table]
Revised: [Table]

Original: GraphRetro (Somnath et al., 2020)
Revised: GraphRetro (Somnath et al., 2021)

Original: [Table]
Revised: [Table]

Original: [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: MEGAN (Sacha et al., 2021)
Revised: MEGAN (Sacha et al., 2021)

Original: [Table]
Revised: [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: MHNreact (Seidl et al., 2021) –
Revised: MHNreact (Seidl et al., 2021) –

Original: [Table]
Revised: [Table]

Original: Results and analysis (1) Higher accuracy :
Revised: Results and analysis (1) Higher accuracy :

Original: SemiRetro achieves the highest accuracy in most settings, seeing Table.
Revised: SemiRetro achieves the highest accuracy in most settings, seeing Table.

Original: As to previous works with open source code, template-free G2G and
Revised: As to previous works with open source code, template-free G2G and

Original: RetroXpert are more scalable than template-based GLN while sacriﬁcing the top-1 accuracy.
Revised: RetroXpert are more scalable than template-based GLN while sacriﬁcing the top-1 accuracy.

Original: We use semi-template to reduce the template redundancy and improve the accuracy simultaneously.
Revised: We use semi-template to reduce the template redundancy and improve the accuracy simultaneously.

Original: 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.
Revised: 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.

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: We propose SemiRetro for retrosynthesis prediction, which achieves better accuracy and attractive scalability than previous methods.
Revised: We propose SemiRetro for retrosynthesis prediction, which achieves better accuracy and attractive scalability than previous methods.

Original: Speciﬁcally, the DRGAT achieves the highest center identiﬁcation accuracy.
Revised: Speciﬁcally, the DRGAT achieves the highest center identiﬁcation accuracy.

Original: The semi-template improves both the accuracy and scalability of synthon completion.
Revised: The semi-template improves both the accuracy and scalability of synthon completion.

Original: Moreover, SemiRetro has favorable interpretability and training efﬁciency.
Revised: Moreover, SemiRetro has favorable interpretability and training efﬁciency.

Original: We hope this work will promote the development of deep retrosynthesis prediction.
Revised: We hope this work will promote the development of deep retrosynthesis prediction.

Original: A A PPENDIX
Revised: A A PPENDIX

Original: Center identiﬁcation We show top-2 center identiﬁcation predictions in Fig.
Revised: Center identiﬁcation We show top-2 center identiﬁcation predictions in Fig.

Original: 7, where synthons are obtained from breaking edge centers for downstream synthon completion.
Revised: 7, where synthons are obtained from breaking edge centers for downstream synthon completion.

Original: We present the probability of each prediction where the total probability of top-2 predictions exceeds 98% , indicating strong inductive conﬁdence.
Revised: We present the probability of each prediction where the total probability of top-2 predictions exceeds 98% , indicating strong inductive conﬁdence.

Original: Since the top-2 predictions are accurate enough, seeing Table.
Revised: Since the top-2 predictions are accurate enough, seeing Table.

Original: 2, we use them for synthon completion.
Revised: 2, we use them for synthon completion.

Original: Synthon completion In Fig.
Revised: Synthon completion In Fig.

Original: 8, we present the process of predicting multiple reactants of the same product.
Revised: 8, we present the process of predicting multiple reactants of the same product.

Original: This process provides an end-to-end view of synthon completion, containing semitemplate prediction, top- k results search, and semi-template application.
Revised: This process provides an end-to-end view of synthon completion, containing semitemplate prediction, top- k results search, and semi-template application.

Original: By default, we choose the top-5 synthon completion results for each center identiﬁcation output as part of the ﬁnal topretrosynthesis results.
Revised: By default, we choose the top-5 synthon completion results for each center identiﬁcation output as part of the ﬁnal topretrosynthesis results.

Original: Platform The platform for our experiment is ubuntu 18.04, with a Intel® Xeon® Gold 6240R
Revised: Platform The platform for our experiment is ubuntu 18.04, with a Intel® Xeon® Gold 6240R

Original: Processor and 256GB memory.
Revised: Processor and 256GB memory.

Original: We use a single NVIDIA V100 to train models, where the CUDA version is 10.2.
Revised: We use a single NVIDIA V100 to train models, where the CUDA version is 10.2.

Original: [Table]
Revised: [Table]

Revised: 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 open- source code for center identiﬁcation and synthon completion.

Revised: In this paper, we use the same atom features of G2Gs.

Revised: We have also tried to combine all these atom features and use the same set of features in center identiﬁcation and synthon completion models.

Revised: The combined atom features do not make a signiﬁcant difference.

Revised: [Table]

Revised: [Table]

Revised: [Table]