Original: Human-in-the-loop for a Disconnection Aware Retrosynthesis
Revised: Human-in-the-loop for a Disconnection Aware Retrosynthesis

Original: Anonymous Author(s)
Revised: Anonymous (s)

Original: AfﬁliationAddress
Revised: AfﬁliationAddress

Original: Abstract
Revised: Abstract

Original: Retrosynthesis is an approach commonly undertaken when considering the manufacture of novel molecules.
Revised: Retrosynthesis is an approach commonly undertaken when considering the manufacture of novel molecules.

Original: During this process, a target molecule is broken downand analyzed by considering the bonds to be changed as well as the functionalgroup interconversion.
Revised: During this process, a target molecule is broken downand analyzed by considering the bonds to be changed as well as the functionalgroup interconversion.

Original: In modern computer-assisted synthesis planning tools, thepredictions of these changes are typically carried out automatically.
Revised: In modern computer-assisted synthesis planning tools, thepredictions of these changes are typically carried out automatically.

Original: However theremay be some beneﬁt to the decision being guided by to those executing the process:typically, chemists have a clear idea where the retrosynthetic change should happen,but not how such a transformation is to be realized.
Revised: However theremay be some beneﬁt to the decision being guided by to those executing the process:typically, chemists have a clear idea where the retrosynthetic change should happen,but not how such a transformation is to be realized.

Original: Using a data-driven model, theretrosynthesis task can be further explored by giving chemists the option to explorespeciﬁc disconnections.
Revised: Using a data-driven model, theretrosynthesis task can be further explored by giving chemists the option to explorespeciﬁc disconnections.

Original: In this work, we design an approach to provide this optionto those carrying out retrosynthetic analysis by adapting a transformer-based modelfor single-step retrosynthesis.
Revised: In this work, we design an approach to provide this optionto those carrying out retrosynthetic analysis by adapting a transformer-based modelfor single-step retrosynthesis.

Original: The model takes as input a product SMILES string,in which the atoms where the transformation should occur are tagged accordingly.
Revised: The model takes as input a product SMILES string,in which the atoms where the transformation should occur are tagged accordingly.

Original: This model predicts precursors corresponding to a disconnection occurring in thecorrect location in 88.9% of the test set reactions, where 14.1% are identical to theground truth and 74.7% are other valid sets of precursors.
Revised: This model predicts precursors corresponding to a disconnection occurring in thecorrect location in 88.9% of the test set reactions. The assessment with a forwardprediction model shows that 76% of the predictions are chemically correct, with14.1% perfectly matching the ground truth.

Original:  Introduction
Revised:  Introduction

Original: Society relies on the production of novel molecules and materials.
Revised: Society relies on the production of novel molecules and materials.

Original: The most common method fordeveloping novel compound synthesis involves breaking existing atom-to-atom bonds and formingnew ones.
Revised: The most common method fordeveloping novel compound synthesis involves breaking existing atom-to-atom bonds and formingnew ones.

Original: Retrosynthetic analysis (retrosynthesis) is the practice of analyzing a target moleculeinto commercially available precursors by way of potential disconnections (bond breakages) andfunctional group interconversion [1].
Revised: Retrosynthetic analysis (retrosynthesis) is the practice of analyzing a target moleculeinto commercially available precursors by way of potential disconnections (bond breakages) andfunctional group interconversion [1].

Original: E. J. Corey, whose primary focus was in studying the structureof organic compounds, was the ﬁrst one to pioneer this concept [2, 3].
Revised: E. J. Corey, whose primary focus was in studying the structureof organic compounds, was the ﬁrst one to pioneer this concept [2, 3].

Original: However, despite 60 years ofsynthetic strategy development, the search space for all potential disconnections is so large that evenprofessionals ﬁnd it difﬁcult to explore.
Revised: However, despite 60 years ofsynthetic strategy development, the search space for all potential disconnections is so large that evenprofessionals ﬁnd it difﬁcult to explore.

Original: The fact that compounds can have multiple retrosyntheticroutes and that each route is affected by elements such as reaction conditions, availability of reactants,and reaction yields (to mention a few) makes the entire process even more challenging [4].
Revised: The fact that compounds can have multiple retrosyntheticroutes and that each route is affected by elements such as reaction conditions, availability of reactants,and reaction yields (to mention a few) makes the entire process even more challenging [4].

Original: Several deep-learning-based approaches to single-step retrosynthesis treat the prediction of possibledisconnections as a translation task, relying on the use of the Transformer architecture [5] andthe simpliﬁed molecular-input line-entry system (SMILES) [6, 7] notation [8–11].
Revised: Several deep-learning-based approaches to single-step retrosynthesis treat the prediction of possibledisconnections as a translation task, relying on the use of the Transformer architecture [5] andthe simpliﬁed molecular-input line-entry system (SMILES) [6, 7] notation [8–11].

Original: Given a targetmolecule, these approaches suggest the best set of precursors (i.e. reactants, and possibly otherreagents) as the translation’s outcome, with the possibility to generate multiple such sets.
Revised: Given a targetmolecule, these approaches suggest the best set of precursors (i.e. reactants, and possibly otherreagents) as the translation’s outcome, with the possibility to generate multiple such sets.

Original: One possible downside of such models is that they provide chemists little control over the disconnections they want to investigate for a given target molecule.
Revised: One possible downside of such models is that they provide chemists little control over the disconnections they want to investigate for a given target molecule.

Original: The recommended precursors arenot guaranteed to be consistent with the chemist’s desired disconnections because the single-stepretrosynthetic models suggest the precursors thought to be optimal based on the training dataset.
Revised: The recommended precursors are not guaranteed to be consistent with the chemist’s desired disconnections because the single-step retrosynthetic models suggest the precursors thought to be optimal based on the training dataset.

Original: In this work, we extend transformer-based models for single-step retrosynthesis in order to enhance thecontrol by chemists when determining a retrosynthetic route by exploring user-deﬁned disconnections.
Revised: Inthis work, we extend transformer-based models for single-step retrosynthesis in order to enhance thecontrol by chemists when determining a retrosynthetic route by exploring user-deﬁned disconnections.

Original: We developed a ’human-in-the-loop’ component that combines expert knowledge and experience withthe power of deep learning.
Revised: We developed a ’human-in-the-loop’ component that combines expert knowledge and experience withthe power of deep learning.

Original: We can therefore use human knowledge and decision-making strategiesthat statistical and machine learning algorithms can’t yet encode due to a lack of relevant trainingdata to provide an enhanced experience in retrosynthetic problems.
Revised: We can therefore use human knowledge and decision-making strategiesthat statistical and machine learning algorithms can’t yet encode due to a lack of relevant trainingdata to provide an enhanced experience in retrosynthetic problems.

Original:  Method
Revised:  Method

Original: We base our model on the approach by Schwaller et al.
Revised: We base our model on the approach by Schwaller et al.

Original: This model converts a tokenized
Revised: This model converts a tokenized

Original: SMILES string representing a product into a tokenized SMILES string representing a set of precursors(including solvent, reagents, and catalysts).
Revised: SMILES string representing a product into a tokenized SMILES string representing a set of precursors(including solvent, reagents, and catalysts).

Original: In order to encode the desired disconnection into theinput SMILES string, we exploit the atom mapping notation of SMILES: all the atoms involved inthe transformation are attributed an atom mapping number of 1 .
Revised: In order to encode the desired disconnection into theinput SMILES string, we exploit the atom mapping notation of SMILES: all the atoms involved inthe transformation are attributed an atom mapping number of 1 .

Original: As a simple example, in order toproduce the target molecule butanol, with SMILES string CCCCO , from a transformation involvingthe alcohol bond, the input SMILES string (before tokenization) would be CCC[CH2:1][OH:1] .
Revised: As a simple example, in order toproduce the target molecule butanol, with SMILES string CCCCO , from a transformation involvingthe alcohol bond, the input SMILES string (before tokenization) would be CCC[CH2:1][OH:1] .

Original: The dataset to train this model can be generated starting from any set of reaction SMILES.
Revised: The dataset to train this model can be generated starting from any set of reaction SMILES.

Original: All thereactions are ﬁrst atom-mapped with RXNMapper [12].
Revised: All thereactions are ﬁrst atom-mapped with RXNMapper [12].

Original: This enables the identiﬁcation of productatoms with different neighboring atoms or bonds than the precursors.
Revised: This enables the identiﬁcation of productatoms with different neighboring atoms or bonds than the precursors.

Original: These atoms are then assignedan atom map number of 1 , while the atom mapping information is removed from all the other productatoms.
Revised: These atoms are then assignedan atom map number of 1 , while the atom mapping information is removed from all the other productatoms.

Original: The atom mapping information is also removed from all the precursors.
Revised: The atom mapping information is also removed from all the precursors.

Original: For this study, the dataset was generated from Pistachio [13], resulting in training, validation, and testsets of sizes 2,269,560, 10,000, and 126,435, respectively.
Revised: For this study, the dataset was generated from Pistachio [13], resulting in training, validation, and testsets of sizes 2,269,560, 10,000, and 126,435, respectively.

Original: The model was implemented using the
Revised: The model was implemented using the

Original: OpenNMT-py library [14, 15].
Revised: OpenNMT-py library [14, 15].

Original: As in [11], the hyperparameters used during training were: layers = 4,heads = 8, rnn_size = 384, word_vec_size = 384, max_generator_batches = 32, accum_count = 4,label_smoothing = 0. The model was trained for 260,000 steps.
Revised: As in [11], the hyperparameters used during training were

Original: Figure 1 illustrates a chemical reaction from the dataset, with the atom mapping obtained from
Revised: Figure 1 illustrates a chemical reaction from the dataset, with the atom mapping obtained from

Original: RXNMapper.
Revised: RXNMapper.

Original: From the atom mapping, it can be seen that the atoms involved in the chemicaltransformation are the ones with mappings 2, 3, and 5.
Revised: From the atom mapping, it can be seen that the atoms involved in the chemicaltransformation are the ones with mappings 2, 3, and 5.

Original: Accordingly, those are the atoms that endup being marked during the dataset generation.
Revised: Accordingly, those are the atoms that endup being marked during the dataset generation.

Original: Figure 2 shows the chemical equation after preprocessing, where the atom mapping information has been removed, and the target molecule containsan indication of what atoms are involved in the chemical transformation.
Revised: Figure 2 shows the chemical equation after preprocessing, where the atom mapping information has been removed, and the target molecule containsan indication of what atoms are involved in the chemical transformation.

Original:  Results
Revised:  Results

Original: In order to analyze the results produced by the model, we determined how many predictions wereidentical to the ground truth after having canonicalized with RDKit [16] and sorted the suggestedprecursors.
Revised: In order to analyze the results produced by the model, we determined how many predictions wereidentical to the ground truth after having canonicalized with RDKit [16] and sorted the suggestedprecursors.

Original: It was found that 14.1% of the reactions produced by the model were identical tothe ground truth.
Revised: It was found that 14.1% of the reactions produced by the model were identical tothe ground truth.

Original: This value is in line with the accuracy of transformer-based models suggestingprecursors including reagents, solvents and catalysts.
Revised: This value is in line with the accuracy of transformer-based models suggestingprecursors including reagents, solvents and catalysts.

Original: However, because there are typically severalways to synthesize the same molecule [11], this statistic, which corresponds to the top-1 accuracy ofthe single-step retrosynthesis model, isn’t very useful.
Revised: However, because there are typically severalways to synthesize the same molecule [11], this statistic, which corresponds to the top-1 accuracy ofthe single-step retrosynthesis model, isn’t very useful.

Original: More signiﬁcantly, it is necessary to assess how many predictions involved chemically correctdisconnections between the given pair of atoms (disconnection accuracy).
Revised: More signiﬁcantly, it is necessary to assess how many predictions involved chemically correctdisconnections between the given pair of atoms (disconnection accuracy).

Original: To do this, the predictedprecursors were combined with the target molecules to produce reaction SMILES that were atommapped with RXNMapper [12].
Revised: To do this, the predictedprecursors were combined with the target molecules to produce reaction SMILES that were atommapped with RXNMapper [12].

Original: This made it possible to determine if the atoms involved in thepredicted transformation are the same as were speciﬁed in the model input.
Revised: This made it possible to determine if the atoms involved in thepredicted transformation are the same as were speciﬁed in the model input.

Original: It was found that 88.9%of the reactions had disconnections between the correct atoms.
Revised: It was found that 88.9%of the reactions had disconnections between the correct atoms.

Original: We also evaluated the chemicalcorrectness of the predictions by determining the round-trip accuracy on the predictions, which isobtained by comparing the product predicted by a forward reaction prediction model with the initialtarget molecule.
Revised: We also evaluated the chemicalcorrectness of the predictions by determining the round-trip accuracy on the predictions, which isobtained by comparing the product predicted by a forward reaction prediction model with the initialtarget molecule.

Revised: This analysis shows that 76% of the predicted disconnections between the given setof atoms are chemically valid disconnections.

Original: The results produced by the model were also analyzed with respect to the number oftagged atoms in the target molecules, which is illustrated in Table 1.
Revised: The results produced by the model were also analyzedwith respect to the number of tagged atoms in the target molecules, which is illustrated in Table 1.

Original: [Table]
Revised: [Table]

Original: >3. Total 100. The reactions with zero tagged atoms correspond to reactions where the reaction product is presentalready in the precursors, sometimes with a different stereochemistry (which was ignored during thegeneration of the dataset).
Revised: The reactions with zero tagged atoms correspond to reactions where the reaction product is presentalready in the precursors, sometimes with a different stereochemistry (which was ignored during thegeneration of the dataset).

Original: Table 1 shows that the disconnection accuracy decreases with the numberof tagged atoms.
Revised: Table 1 shows that the disconnection accuracy decreases with the numberof tagged atoms.

Original: For single atoms tags, the bond accuracy produced was 99.1%.
Revised: For single atoms tags, the bond accuracy produced was 99.1%.

Original: From this it can beconcluded that the simpler changes in a reaction, the more precise the prediction was.
Revised: From this it can beconcluded that the simpler changes in a reaction, the more precise the prediction was.

Original: An example ofthis could be a reaction where there is only one or two tagged atoms in the input.
Revised: An example ofthis could be a reaction where there is only one or two tagged atoms in the input.

Original: This is consistentwith the difﬁculty of this task for a chemist: it is easy to suggest reactions with one or two transformedatoms, while it is much harder to suggest precursors corresponding to ﬁve or more transformed atoms.
Revised: This is consistentwith the difﬁculty of this task for a chemist: it is easy to suggest reactions with one or two transformedatoms, while it is much harder to suggest precursors corresponding to ﬁve or more transformed atoms.

Original: The chemical correctness of the predictions is highest for transformations involving one or two atoms.
Revised: The chemical correctness of the predictions is highest for transformations involving one or two atoms.

Original: When more atoms are involved in the transformation, this value decreases rapidly, which is consistentwith the difﬁculty of suggesting precursors for complex reactions transforming many atoms.
Revised: When more atoms are involved in the transformation, this value decreases rapidly, which is consistentwith the difﬁculty of suggesting precursors for complex reactions transforming many atoms.

Original: Figure 3 shows an example with six tagged atoms, where the prediction is different from the groundtruth.
Revised: Figure 3 shows an example with six tagged atoms, where the prediction is different from the groundtruth.

Original: In this case, the predicted precursors are actually a subset of the ground truth precursors.
Revised: In this case, the predicted precursors are actually a subset of the ground truth precursors.

Original:  Conclusion
Revised:  Conclusion

Original: In this work, we developed a ‘human-in-the-loop’ scheme in a Transformer-based model for singlestep retrosynthesis that allows experts to specify where the disconnections should occur.
Revised: In this work, we developed a ‘human-in-the-loop’ scheme in a Transformer-based model for singlestep retrosynthesis that allows experts to specify where the disconnections should occur.

Original: This workgives those performing retrosynthetic analysis the decision of where bonds should be broken.
Revised: This workgives those performing retrosynthetic analysis the decision of where bonds should be broken.

Original: Weprocessed the input training data in such a way that the atoms involved in the chemical transformationduring the reaction are marked correspondingly.
Revised: We processed the input training data in such a way that the atoms involved in the chemical transformationduring the reaction are marked correspondingly.

Original: By using this approach, we were able to train amodel that was able to predict 14.1% of reactions identically to the data inputted.
Revised: By using this approach, we were able to train amodel that was able to predict 14.1% of reactions identically to the data inputted.

Original: 88.9% of thepredictions made by the model provided disconnections between the speciﬁed atoms, and in 76.0%of these cases, the predictions were chemically correct.
Revised: 88.9% of thepredictions made by the model provided disconnections between the speciﬁed atoms, and in 76.0%of these cases, the predictions were chemically correct.

Original: This result was signiﬁcant as there are oftenmultiple precursors that could be used to synthesize the same target molecule and provides a new wayto exploit human knowledge and decision-making strategies through a ‘human-in-the-loop’ scheme.
Revised: This result was signiﬁcant as there are oftenmultiple precursors that could be used to synthesize the same target molecule and provides a new wayto exploit human knowledge and decision-making strategies through a ‘human-in-the-loop’ scheme.

Original: Combining additional elements of artiﬁcial intelligence in the current development may furtherincrease the accuracy and ability to predict the disconnections in the desired location, improving theoverall performance .
Revised: Combining additional elements of artiﬁcial intelligence in the current development may furtherincrease the accuracy and ability to predict the disconnections in the desired location, improving theoverall performance .

Original: In fact, the current model requires chemists to specify exactly all the atomsinvolved in the change.
Revised: In fact, the current model requires chemists to specify exactly all the atomsinvolved in the change.

Original: In the future, we plan to train a variant of the model that allows only subsetsof transformed atoms to be tagged.
Revised: In the future, we plan to train a variant of the model that allows only subsetsof transformed atoms to be tagged.

Original: This will be achieved by generating a new data set where target
Revised: This will be achieved by generating a new data set where target

Original: SMILES are represented multiple times, with different numbers of tagged atoms.
Revised: SMILES are represented multiple times, with different numbers of tagged atoms.