PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation
Authors: Binghong Chen
Chengtao Li
Hanjun Dai
Rampi Ramprasad
Le Song
Venue:  (2021)
Keywords: ML for Chemistry
Polymer Retrosynthesis
Few-show Learning
Domain Adaptation

Abstract: Polymers appear everywhere in our daily lives -- fabrics, plastics, rubbers, etc. --  and we could hardly live without them. To make polymers, chemists develop processes that combine smaller building blocks~(monomers) to form long chains or complex networks~(polymers). These processes are called polymerizations and will usually take lots of human efforts to develop. Although machine learning models for small molecules have generated lots of promising results, the prediction problem for polymerization is new and suffers from the scarcity of polymerization datasets available in the field. Furthermore, the problem is made even more challenging by the large size of the polymers and the additional recursive constraints, which are not present in the small molecule problem. In this paper, we make an initial step towards this challenge and propose a learning-based search framework that can automatically identify a sequence of reactions that lead to the polymerization of a target polymer with minimal polymerization data involved. Our method transfers models trained on small molecule datasets for retrosynthesis to check the validity of polymerization reaction. Furthermore, our method also incorporates a template prior learned on a limited amount of polymer data into the framework to adapt the model from small molecule to the polymer domain. We demonstrate that our method is able to propose high-quality polymerization plans for a dataset of 52 real-world polymers, of which a significant portion successfully recovers the currently-in-used polymerization processes in the real world.

Under review as a conference paper at ICLR 2021
POLYRETRO: FEW-SHOT POLYMER RETROSYNTHESIS VIA DOMAIN ADAPTATION
Anonymous authors Paper under double-blind review
ABSTRACT
Polymers appear everywhere in our daily lives fabrics, plastics, rubbers, etc. - and we could hardly live without them. To make polymers, chemists develop processes that combine smaller building blocks (monomers) to form long chains or complex networks (polymers). These processes are called polymerizations and will usually take lots of human efforts to develop. Although machine learning models for small molecules have generated lots of promising results, the prediction problem for polymerization is new and suffers from the scarcity of polymerization datasets available the field. Furthermore, the problem made even more challenging by the large size of the polymers and the additional recursive constraints, which are not present the small molecule problem. In this paper, we make an initial step towards this challenge and propose a learning-basse automatically identify a sequence of reactions that lead to the polymerization of a target polymer with minimal polymerization data involved. Our method transfers models trained on small molecule datasets for retrosynthesis to check the validity of polymerization reaction. Furthermore, prior learned on a limited amount of polymer data into the framework to adapt the model from small molecule to the polymer domain. We demonstrate that our method is able to propose high-quality polymerization plans for dataset of 52 real-worls polymers, of which more than 50% successfully recovers the currently-in-use polymerization processes in the real world.
search framework that
method also incorporates template
INTRODUCTION
Human beings are living in a world of chemical products, among which a category of chemicals, called polymers, is playing essential role. Ranging from fabrics to plastics to rubbers, polymers are appearing in every corner of our daily lives. Polymers with different properties are desired when used in different cireumstances, and chemists have been spending tremendous effort to design and synthesize new polymers in the pursuit of ones with better properties. To make polymers, chemists develop processes that combine small building blocks, which we call monomers, form longer chains or complex networks. Such processes are called polymerization and will take a significant amount of human effort to develop.
Since the rise of deep learning (LeCun et al. 2015). applying these models to science problems like biology and chemistry ones have gradually gathered attentions. Specifically, the applications of Al methods in the retrosynthetic design of chemical compounds have become very popular recently (Segler et al., 2018, Coley et al.)). While most work focuses on synthesizing drug- like small molecules, the study of polymer retrosynthesis is still at its infancy. The reasons are multi- fold, but one of the most important ones being the lack of available polymerization datasets, which poses difficulties for cxisting learning- based methods to learn meaningful pattern for polymerization reactions. Moreover, polymers usually have chain or network structure with repeat units, which is very different from small molecules. This additional constraints also introduces difficulties in the formulation and modeling of polymer dessign/eetoosynhessi..
In this paper, focus specifically on the polymer retrosynthesis problem. While there has been series of work focusing on small molecule retrosynthesis (Corey & Wipke 1969 Gasteiger et al.
1992 Coley al. 2017 Liu al. 2017 Segler & Waller 2017 SSeglr al. 2018 Coley al.

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able to propose Under review as a conference paper at ICLR 2021
Karpov et al. 2019 Baylon et al., Schreck et al.; Dai al. 2019 Chen et al., 2020), the problem of polymerization is very different and challenging in the machine learning sense that
1. To predict synthesis routes for polymer repeat units, additional structural constraints such as recursive constraint should be imposed to guarantee a potentially valid polymerization procedure. Such constraints do not exist in existing formulation for molecule retrosynthesis, thus most methods could not be directly applied.
Polymerization data for training very limited. Compared with retrosynthesis models built for small molecules where accessible training data is at least tens of thousands, the size of polymerization data is tiny, and our case it even less than 100. This size is meaningless for most existing models to learn any synthesis patterns.
In this paper, we formulate the problem of polymer retrosynthesis as a constrained optimization and present Pol yRetro, a novel learning-based search framework to tackle the problem of poly- mer retrosynthesis. With beam search and rejection sampling. PolyRetro monomer candidates with high polymerization probability while satisfying monomer synthesizability constraints. Our method is based on reaction templates collected from small molecule reactions, which capture the local structural properties for small molecule reactions. We leverage an one-step retrosynthesis model trained on a small molecule reaction dataset and adapt it to the polymer domain by incorporating template prior learned tiny-sized polymerization data. To verify whether the proposed monomers are synthesizable, we employ Retro (Chen et al.) 2020), a multi-step retrosyn- thesis model to predict their synthesis routes. We demonstrate Po re through experiments that it is able to predict monomers accurately given target polymer repeat units. To our knowledge, we are the first formulate, model and tackle the polymer retrosynthesis machine learning problem.
The approach we developed is general in the sense that it can also be applied to other machine learning problem such as theorem proving and program synthesis, where the results we want obtain involves recursion. For instance, the analogue problem in theorem proving is deriving proof for a theorem which contains recursive relations; the analogue problem in program synthesis generating programs containing loops and recursive calls. We choose to focus our application in polymer synthesis because its importance and high societal impact. See Appendix E for more concrete discussions on the generality of PolyRetro.
Our contributions are summarized below:
We formulate the problem of polymer retrosynthesis constrained optimization problem. To our knowledge, this is the first machine learning formulation that takes constraints in polymer retrosynthesis into consideration.
We propose PolyRetro, learning- based search framework that tackles the problem of polymer retrosynthesis. To our knowledge, this is also the first bearning-based method in this problem setting.
Rely is able recover 53% of ground truth monomers for real-world polymer dataset using limited training data, significantly outperforming all existing algorithms.
2 RELATED WORKS
Computer-aided retrosynthetic planning for chemical molecules was first formalized by E. J. Corey (Corey & Wipke 1969) and have been deployed over the past years. The task of retrosynthetic design is to identify a series of reactions that leads to the synthesis of target molecule. This is one of the most fundamental problems in organic chemistry.
Recently, many machine learning methods has been proposed to the easier but also important subproblem, where one is given target molecule and the task is to predict the direct predicates (Coley et al.)) Methods to tackle such 'one-step version' of retrosynthesis could be roughly divided into two categories, template-based and template-fres ones. template of chemical reaction essentially how bonds and atom change during the reaction, and could be applied reversely to get reactants from products. Thus there have been a series of methods trying to predict the reaction templates given product molecules to get the corresponding reactants (Coley et al.) 2017; (Seglrr & Waller 2017; [aylon et al.) Dai et al. 2019). While powerful, these methods are not applicable in the case where training data comes without templates. To resolve this, there have been attempts to
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sequence-to-ssequeee model to directly predict SMILES representation of reactants (Liu et al. Karpov et al. 2019). Such methods could be straight- forwardly applied to any reaction data, but may need large number of reactions for training to find meaningful reaction patterns without help of reaction templates.
On the other hand, there has been works trying to directly solve multi-step retrosynthesis predic- tion (Segler et al. 2018) Schreek et al.) Kishimoto et al.] 2019 Chen et al., (020). This multi-step procedure is usually decomposed into two modules, one retrosynthesis module which could propose possible direct predicates given product molecules, and a planning algorithm to search for best synthetsis route with recursive application of one-step module.
While there has been such rich literature in the retrosynthesis domain, paucity of work has tackle the specific problem polymer retrosynthesis. We focus on polymer retrosynthesis and bring knowledge from general retrosynthesis literature to help tackle the problem.
3 BACKGROUND
In this section, focus providing the background knowledge about molecule retrosynthesis as well as defining notations. This serves as the building block in our polymer retrosynthesis modeling.
Given molecule E . where M indicates the space of molecules, the molecule retrosynthesis problem focuses on finding set of reactants S C M that can be used to synthesize m. Before introducing the approaches for retrosynthesis, we first cover the background reaction templates.
3.1 REACTION TEMPLATE
A reaction template T' := o" pattern of that is matched with target molecule m, into r! that appears in i-th reactant 8; E S. The set of templates T can be extract from existing chemical reactions in the literature. Although applying templates involves with expensive subgraph matching between o" and m, where itself is an NP-hard problem, such approach provides tractable way of finding candidate S with chemical rules.
+7 is graph rewriting rule that rewrites subgraph
3.2 LEARNING-BASHR MOLECULE RETROSYNTHESIS
The molecule retrosynthesis problem has raised increasing interest in the machine learning (ML) community, due to its importance in chemistry and the difficulty structured prediction setting. We here mainly focus the MI. approaches for such problem, some of these provide probabilistic interpretations that will be needed in our optimization framework. Depends on the number of reaction steps needed synthesize m, such problem can be categorized into one-step and multi-step retrosynthesis.
One-step molecule retrosynthesis: One-step setting requires that == S m can be realized in one chemical reaction. It focuses on modeling p(S'm) with or without reaction templates. As the template based one guarantees the satisfaction of human defined rules. we use the model proposed in NeuralSym (Segler & Waller 2017) for one-step prediction. Specifically in this model:
p(Smm)
E(TTmm) (SubgMatch(o", m))
TET
where SubgMatch(()) operator checks the subgraph matching between T and m.
Multi-step molecule retrosynthesis The multi-step extension allows using multiple reactions Rm (R. J==1 synthesize m, with the restriction that the reactants set C I C M where is the set of starting molecules. This is essentially planning problem that search through the reaction space using one-step models as expansion proposals. In our paper, we use the Retro*(Chen et al. 2020) which is the ofate-of-fie off-the-art approach that provably optimizes the synthesizability of R...
[R.i

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OH
n H2O
HD
Terephthalic Acid
Ethylene Glycol
Polyethylene Terephthalate (PET)
Condensation polymerization for synthesizing PET. We use shaded color regions to highlight the monomers (light blue), polymer (light pink), repeat unit (dark pink), end-groups (purple), and by-product (light yellow). Unit polymer is polymer with unit length (n 1).
Figure 1:
4 MODELING POLYMER RETROSYNTHESIS
polymer a large molecule composed of many repeat units. Directly using molecule retrosynthesis techniques for polymers not feasible, (i) the (potentially infinite) large molecule is feasible for existing molecule retrosynthesis approaches; (ii) the polymer retrosynthesis has nontrivial recursive and stability contraints, which cannot be easily addressed in existing approaches.
In the following content, we first state these constraints in Section 4.1
then we formally present our
modeling strategy for such problem Section 4.2
4.1 CONSTRAINTS FOR POLYMERS AND POLYMERIZATIONS
In chain structured polymer, each repeat unit in the polymer has two open bonds which link with neighboring units in a chain-like structure. At the end the chain are the end-groups. They are functional groups closely related the polymerization process, where the polymer is synthesized from the monomers.
In this work focus condensation polymerization. in which large molecules join together and lose small molecules such as H2O/HCI (Figure[] [ This type of polymerization needs to satisfy the recursive constraint (unit polymerization) placed the unit polymer.
Definition (Recursive constraint) Given the repeat unit with structure -A-, where we use "-' represent an open bond, which connects to the neighboring repeat unit in long chain, the unit polymer u should be b-A-c, where b- and induced unit polymerization reaction should be positive:
urd-groups, and the probability of the following
UnitPolymerivatioonf:: u) b-A-c + b-A-c - b-A-A-c b-c
In this reaction, unit polymers join together and lose b-c the byproduct. practice, hope that the end-groups b- and -c tend to react with each other with high probability, which makes the polymerization reaction continue happen. Therefore the unit polymer itself is not stable. However the monomers, which are the precursors of the unit polymer, should satisfy the stability constraint.
Definition 2 (Stability constraint) Given the unit polymer u with structure b-A-c, the monomers m should be (b-B-b, c-C-c], where b-B-b and c-C-o are symmetric, and u can be synthesized from m with the following reaction:
UnitPolymarSynttlessst m) b-B-b c-C-c b-A-c b-c
(3)
Both of the above constraints come from chemical insights which guide polymer synthesis.
4.2 POLYMER RETROSYNTHESIS MODELING
Section 4.1 has defined the structural constraints for polymer retrosynthesis, which are symbolic with rule definitions. In real application, polymerization also requires the efficiency to make the entire pipeline cost efficient. With both the rule constraints and efficiency requirement, we formu- late the polymer retrosynthesis as a constrained optimization problem. We first define molecule synthesizability below:
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Poyymer induction
Monomer
MonomerProposee Proposal
Synthesizability Check
-A-
A
b
GrCrt
by-B,r
Accept
By
by
b
Reject
by-B,r
by.8urrb2
Accept
by.Bartba
Reject
D,
Rank: P|b,A-G| -A-)
Rank: P(b P(b,B,,b, 5-G ((A-s) bpA-s)
Rank: B(b,B, & b -A.)
Figure 2: PolyRetro beam search framework. Given the repeat unit r, we first generate unit polymer candidates by polymer induction. We then rank the candidates using p(u/r) and sample monomers (m1, m2) from the top-n unit polymers. Next we re-rank the unit polymer and monomer pair (u, m1. m2) using the joint p(u m.. my(r) p(ulr) -p(m,, mylu and perform the synthesizability check on (m,, m2) using Retro*, multi-step retrosynthesis planner. Finally the top-k results which pass the check are returned.
Definition (Molecule synthesizability) A molecule m is h-synthesizable from a set of starting molecules I and only if there exists a series of reactions R. whose initial reaciants are in I, and the joint probability of reactions in R. satisfy a given lower bound li, iee. p(R.)= > h.
Practically we rely on the molecule retrosynthesis algorithm presented in 3.2 to obtain both R. and p(R). Using the key concepts defined above, we state our optimization formulation below:
Definition 4 (Polymer Retrosynthetic Optimization) Given a target polymer represented as re- peat unit we want to identify a unit polymer and monomers ma, m2 which maximize the polymerization probability with the recursive, stability, and molecule synthesizability constraints.
p(u, my m2]r) p(ulr) pfmm,mal
max M, m2
(4)
S. I.
pl p(Rm1,m22 > h. where >>
The model p(u, m1, "M1,/)/r) m2(r) (r)) is decomposed into p(u(r) that incorporates the recursive constraint, and p(m1, m2/1) that incorporates the stability constraint. The formulation can interpreted using a mathematical induction analogy. Synthesizing the target polymer can be understood as proving the feasibility of the polymer. To "prove" the target polymer, we need to first work on the base case, i..,, find monomer which is synthesizable, and then prove the induction step, i.e. maximize the polymerization probability.
Although p(m1, m2/u can be characterized using one-step molecule retrosynthesis model, directly optimizing the above problem is nontrivial, as (i) the recursive constraint model p(u /r) which predicts unit polymer from repeat unit not available and there is not enough data in the literature to p that is not possible to characterize the gradient, convexity, etc.. In the next section, will present Po PolyRet ro to effectively solve the constrained optimization by overcoming these difficulties.
estimate such model; and (ii) the constraint of p(Rm1.m2)) > goes through a planning algorithm
POLYRETRO FOR POLYMER RETROSYNTHESIS
To tackle the challenging constrained optimization problem defined in Section 4.2 we propose PolyRetro (Figure 2 learning-basee search framework with minimal polymerization data involved. We first introduce the overall procedures of the algorithm, and then cover the details.
Our main idea is based on the rejection sampling framework for solving Eq ( which treat the molecule synthesizabilityy constraint black-box rejection criteria. Although asymptotically can sample target solution (u, my m2) from Eq (4). good proposal algorithm needed to keep the rejection rate low and mix fast. Since the joint probability of (u, mı, m2, decomposes into two

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where i(T) is an empirical estimation that of template prior from the limited polymer dataset, and (0,1) is a factor tha balances the two Bullback-Leible divergences. The optimal solution is Under review as a conference paper at ICLR 2021
terms, approximate the max operator with beam seareh in two steps:
top top-Ky,m ,,,,,,,mP(u, m1, m2(r) top-ku,m Kum wtpppmm.1......mmmmm m2 ( [u top-n {p(u))
(5)
We present the modeling and inference of these two steps in the next two sections. In Section 5.1 we show how to generate top-n candidate unit polymers that satisfy with recursive constraint using domain adaptation with novel polymer induction technique. Then in Section 5.2] we show the generative procedure of monomers with stability constraints. We conclude the approach with the molecule synthesizabilityy check in Section 5.3
5.1 UNIT POLYMER PROPOSAL WITH DOMAIN ADAPTED POLYMER INDUCTION
In the first step of the beam search, we want to quickly generate the feasible candidate unit polymers from the repeat unit Proposing candidates that satisfy recursive constraint is hard. This involves predicting the end-groups from the repeat units. Also note that there will not be enough data to directly learn the model p(u(r).
observe that, is relatively easy to obtain the one-step retrosynthesis model (one-step model for short) pm)mm) in Eq (T for molecule m and there enough small molecule chemical reactions to train such one-step model. Based on this, we propose to perform domain adaptation using polymer induction technique (Figure 3). explained below:
Polymer induction: the basic idea to leverage one- step molecule retrosynthesis model to help predict the end groups unit polymer. As the model p(S m) only accepts molecules rather than repeat units, circumvent this issue with the following procedure:
Double Repeat Unit
Apply reaction template
1. Link two repeat units head-to-tail to form double repeat unit, and add hydrogen as end-groups, i.e. H-A-A-H:
2. Loop through all the reaction templates:
(a) Apply reaction template to the double repeat unit;
Reconstructaandidaee
(b) If the result is in the form of H-A-A-H - H-
A-c b-A-H, add b-A-c to the candidate list.
Unit P Polymer
The above procedure is based on the induction principle: the base case solves (i.e., we find the unit polymer with form b-A-c), then units b-A~A-C where the bond between two As (de- noted symbol) belongs the reaction center. Such induction thus provides the guidance for the template based retrosynthesis: template T of of r/ should have the subgraph matched at the location that covers the bond denoted as ~. Using template based graph rewriting with rf and r'2" we can obtain the end groups b and c.
synthesize the double
Figure 3: Polymer induction for generating unit polymer candidates. For each of the reaction template, repeat unit, if it can break the bond connect- ing two repeat units into two end-groups. These two and-groups are then being put back to the repeat unit reconstruct unit polymer candidates.
apply it to the double
Modeling p(u/r) with domain adaptation: the polymer induction step gives us a list of unit polymer and corresponding template candidates {(T,, b--A-c;)} that satisfy the recursive constraint, which is actually the support of p(ulr) (i.e., samples with non-zero probability). Given unit polymer and template pair (u b-A-c, 7), we formulate the corresponding joint probability as the optimal solution of following optimization:
min A DRr((p(S [u, u} in = = b-A +(1 X) DK (P(T)) Tlr) p(u-h-A..
matching one-step model
matching larget prior
(6)
'Beam search has similar performance other sampling schemes when A is large, as show in Appendix

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p(u = b-A-c, T|r) = Ap(fu, u) (b-A-A-c) (1 A)p(T). This objective performs the domain adaptation from molecule synthesis into polymer synthesis with the following benefits:
With the polymer induction technique, adapt one-step model for molecules to polymer domain. By interpolating between one-step model and the prior Iearned on target domain, we can achieve a balance between enormous foreign domain knowledge and limited target-domain priors.
There could be multiple design choices for P(T). Due to the limited data in target (polymer) domain, we use kernel density estimation with atom- and bond-counting features for simplicity.
5.2 MONOMER PROPOSAL UNDER STABILITY CONSTRAINT
Using domain adapted polymer induction in the above section, we can obtain top-n unit polymers {by-A-cs 1 and their scores. The second step of the beam search seeks for monomers given the unit polymers. As both of them are proper molecules, the one-step model can be directly used here:
p(m1, m2lu) p(S {m1, m2) Im u)II [Both my and mg are symmetric]
(7)
Adding together with the scores of each unit polymer u. we can approximately get list of feasible unit polymer and monomers with the highest joint probability.
5.3 SYNTHESIIZABIITY AS FILTERING CRITERIA
Once we have the top-n list of monomers (mi, mi ) obtained in previous two beam-search steps, we can perform rejection step using any off the shelf multi-step retrosynthesis planner to check the synthesizability p p(mm),ma) for each (m, m2) E {mi, my = and return the top-k them which satisfies the recursive, stability and synthesizablity constraints. This finishes PolyRetro ro algorithm.
EXPERIMENTS
Experiment Settings. To evaluate the performance of PolyRetro, we collect a dataset of 52 condensation polymer<"] and their corresponding synthesis recipes. For each polymer, the task is to predict the ground truth monomers and unit polymer given the repeat unit. We split the data into folds and repeat the same experimental setup times to compute the mean and standard deviation of the performance metric. Because we are targeting few-shot learning setting and considering we do not have enough data for testing, in each experiment, we only use one fold of data for training and hold out the other four folds for evaluation. We use top-k recovery rate, i.e. whether the target is in the top-k prediction, on both unit polymers and monomers to measure the performance.
PolyRetro Implementation. The one-step retrosynthesis model used in Pol colyRetr is trained on the publicly available reaction dataset extracted from United States Patent Office (USPTO). We use a multilayer perceptron model with one hidden layer of size 512, which takes 2048 dimensional molecular fingerprint (Rogers Hahn 2010) as input, and predicts a probability distribution over 230k reactions templates extracted from the same dataset. Reactants can be obtained by applying the reaction templates to the product using RDKit (rdk). In the monomer retrosynthesis step, Retro" (Chen ett al. 2020) employs the same model for synthesis route search given list of commercially available building blocks from eMolecule B which consists of 231M commercially available molecules.
We implement the template prior in Pol yRet ro using nonparametric Gaussian kernel density estimator with bandwidth 1. Its performance is not sensitive to the handwidth parameter. The features are the number of atoms and edges on both side of the template. We set À to 0.999 in Eq (6).
Baselines/A Aittitees//blaitio blation Studies. We compare ro ro with Transformer Vaswani et al., 2017)
model which directly predicts the target sequence (unit polymers/monomerrs) from the repeat unit. We also conduct an ablation study by replacing the unit polymer proposal step in PolyRetro with (a) sampling randomly from the support of p(u(r) (Random Proposal), and (b) using only the one-step retrosynthesis model trained on USPTO as p( (u)r) (PolyRet
Since learning a sequence-
to- oe-seuence model from only a few data points is nearly impossible, we give all Transformer-based models a large advantage by allowing the use of test data for validation for early stopping in training.
'It's hard to collect data this domain. We conduct human evaluations on an additional 100 polymers which have no ground truth recipes. More details are discussed in Appendix D http://dwwnloadd.coo wmm.cculls.comm/ff//2/000 1-01/
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of unit palymers recovered top-k prediction
top-k prediction
Recuraive
70
70
Stability
60
Syntheszabillty
50
50
PolyRetro
PotyRetro
Polytetro-USPTO
40
PolyRetro-UsPTO
Random Proposal
Random Proposal Transtormer
30
Transtormer
20
10
10
0
10
20
50
10
20 20 40 5.0
Top-k
Top-k
Figure 4: Percentages of targets recovered by algorithms in top-k prediction. Left: unit polymers prediction using p(u[r); Right: monomer prediction using p(ulr) p(m1, m2 /u)... We also show PolyRetro's performance upper bounds after addressing the labeled constraints on the right.
Results. The top-k recovery rate result on unit polymers and monomers is summarized in Figure 4 PolyRetro achieves over 50% top-1, 71% top-30 recovery rate in unit polymer prediction, and over 50% top-5 recovery rate in monomer prediction. The performance is significantly better than the sequence-t--eeqeence baseline, which is not able to learn anything, achieving 0% top-50 recovery rate in both results, not to mention that the baseline model can see the test set during training. This is within our expectation due the lack of training data. With more data we should see an increase the number. However, end-to-end approaches still suffer from the inherent limitation of failing to address the structural constraints. When the baselines are given even more advantages by knowing structural priors in advance, they still cannot perform as well as PolyRetro, as shown in Appendix [
Domain Adaptation. Knowledge transfer from small molecule reactions to unit polymerization is clearly supported by the result, as Po 1 yettro and Pol yRel ro-USPTO outperform the random proposal baseline by large margin. With such strong performance of Poly yeleettr recovering the ground truth monomers, could also be of realistic significance. Domain adaptation by incorporating template prior is also help- ful for improving the performance. With the template prior learned on one fold of data, P PolyRet ro able to have a 5% gain in performance compared with Pol wot.tt--tssSst. unit polymers. The main reason that is because of the limitation on unseen templates for one-step retrosynthesis, which we discuss below.
have reason believe the other monomers generated by our algorithm
when predicting ro is not improving the monomer prediction further
Limitations. We display the performance upper bound of Pol yRet.o in the monomer prediction result in Figure From top to bottom, the three horizontal lines correspond to the upper bounds after addressing the recursive constraint, the recursive and stability constraints, and all three constraints. These upper bounds are resulted from the following limitations of the algorithm.
Constraint Formulation. Our constraint formulations in Definition D and describe the most common pattern for polymerization but unable to cover all special cases. The algorithm can be improved by incorporating more chemical knowledge.
Unseen Reaction Templates. Since the templates for one-step retrosynthesis model are extracted from the small molecule reactions, there could be new reaction templates which only exist in polymerization. The limitation can be alleviated by extracting templates and training the one-step retrosynthesis model directly using polymerization data.
Monomer Refrosynthesis. For rare cases, Retro* cannot find synthesis path for the ground truth monomer in limited time. This can be resolved by allowing more time for Retro* search and using larger building block molecule set.
7 CONCLUSION
Polymer synthesis is an important yet challenging task with high societal impact. this work, we propose a novel constrained optimization formulation for the problem, together with learning-based search framework to tackle the problem. Our framework is able to handle the structural constraints that appeared in the polymerization process, and to deal with small data regimes by adapting the model trained on small molecule reaction dataset to polymer domain. Our method significantly outperforms the baselines in terms of the ground truth monomers recovered rate in top-k prediction.
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John S Schreck, Connor W Coley, and Kyle JM Bishop. Learning retrosynthetic planning through simulated experience. ACS Central Science.
Marwin HS Segler and Mark P Waller. Neural-symbolic machine learning for retrosynthesis and reaction prediction. Chemistry-A European Journal, 23(25):5966-5977 2017.
Marwin HS Segler, Mike Preuss, and Mark Waller. Planning chemical syntheses with deep neural networks and symbolic ai. Nature, 555(7698):604 2018.

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Under review as a conference paper at ICLR 2021
Andrew Trask, Felix Hill, Scott E Reed, Jack Rae, Chris Dyer, and Phil Blunsom. Neural arithmetic logic units. In Advances in Neural Information Processing Systems, pp.
2018.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, pp. 5998-6008, 2017.
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Under review as a conference paper at ICLR 2021
A POLYMERIZATION EXAMPLES
More examples of condensation polymerization are presented below. The recursive and stability constraints are strictly imposed here.
(2n-1) H2O
Adipic Acid
Hexameehylenediamine
Nylon 6,6
Figure 5: Condensation polymerization for synthesizing nylon-6,6 from hexamethylencdiimine and adipic acid.
HAA
NHg
+ (2n-1) HCI
0
p-P henylenediamine
Terephthaloyl Chloride
Kevlar
Figure 6: Condensation polymerization for synthesizing Kevlar from p-Phenylenediamine and terephthaloyl chloride.
B ALGORITHM SUMMARY
To attack the constrained optimization problem defined in Eq (4 PolyRetro (Algorithm[] I adopts a rejection sampling approach using a two-step beam search as the proposal distribution. The first step (line 1-2) of beam search proposes valid unit polymer candidates given the target repeat unit using domain adaptation with polymer induction. Then in the second step (line 3-5), monomer candidates proposed given the unit polymers. Finally rejection sampling (line 6-9) is performed to filter out infeasible monomers.
Algorithm 1: PolyRetro(r)
Compute p(u/r) via domain adaptation using polymer induction (Eq nt top-n, p(ulr)
for i f- to n do
Compute p(m1, m2 (u:) via one-step model with constraints (Eq
((m (b.5) _
(i.5) (
)=
= top ttpptm,, my ing p(m1, mglu:)
for i +- 1 to do
for j - to do
(1d) (1r)) Rmi,m² Retro (m .112
accept(bd) p m2) h
(((l) mi (2) .m2 R (0)
Rmm,m2) ) top- top p(ulr) p(m1, mmlu, (w. m
return ((((), m m2 RR,Rl (()
(6)

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Under review as a conference paper at ICLR 2021
C
TEMPLATE PRIOR ESTIMATION
To estimate the template prior p(T) in Eq
(6), we use kernel density estimation with Gaussian kemel,
1
x x
) x - x
Pkde (x)
exp
))
n n(2T)d//2p) h/
h
i=1
where x (na(o" nb(l annarr (r) mbb(r (rr na(r22), nb(r22)) R is the feature
vector for the i-th template T; == Ti + T + r2 Here we use na(s) and nb((s) to denote the number of atoms and bonds in subgraph s.
D MORE EXPERIMENTS
% monomers recovered in top-k prediction
Recursive
Stability...
60
Synthesizailily
recovered
40
PolyRetro
Random proposal
seq-seq-retro
mlp retro
20
30
50
Top-k
Figure 7: Top-k monomer recovery with added baselines. The dash-dotted lines represent baselines with additional advantage.
Additional Baselines. We introduce two baselines, seq2seq-retro and mip-retro, to monomer recovery experiments. Both based existing one-step retrosynthesis methods. Since it is infeasible to directly apply one-step models to the repeat unit r, which is an incomplete molecule, we give additional advantage both baselines by completing r using one of the most common end-groups pairs, and perform one-step retrosynthesis on the completed molecule H-r-H to obtain the monomer candidates. Details of two baselines (originally one-step models) are: 1) seq2seq-retro, seq2seq model pretrained on USPTO, fune-tuned on the training split; 2) mlp-retro: mlp model trained on USPTO. It is template-bass based and cannot be fine-tuned.
Although given additional advantage, both baselines are out out-performed by PolyRetro (Figure The mep-retro baseline not performing well, as it is based on reaction templates which cannot modify the fixed moo-gsoup.. The seq2seq baseline is doing well for large k. but unlike Po FolyRetro and mlp-retro, fails to provide chemical explanations.
beam search
softmax sampling
nucleus sampling
2 0.346 = 0.035
0.284 + 0.051 0.538 + 0.028
0.038 + 0.011
10
0.519 0.028
0.519 + 0.023
Table 1: Percentages of monomers recovered under different sampling schemes.
Ablation Study Sampling Schemes. We explore other sampling schemes including softmax sampling and nucleus sampling. Both strategies lead to similar performance as the beam search we used in PolyRetro when k is large enough.
Evaluation on Additional Polymers. We collect an additional 100 polymers for evaluating monomer prediction, and the original 52 polymers for training. As do not have the synthesis recipes for
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Under review as a conference paper at ICLR 2021
these polymers, the evaluation can be seen the process of proposing monomers for synthesizing a newly found polymer. We conduct a double-blind A/B testing similar to (Segler et (018)) by asking experienced chemists to compare the top-1 monomers proposed by different algorithms. We found that seq2seq-retro does not apply in this scenario: for all 100 polymers whose repeat units are much larger than the in the training set, seq2seq-retro predicts the same monomer output. This is probably due to seq2seq models do not generalize well to longer and unseen sequences. The A/B testing result between PolyRes ro and mlp-retro is: 1) Po PolyRetro gives the more sensible solution for 82/100 polymers and 2) all solutions for the rest 18/100 polymers have apparent flaws. Additionally. the chemists found that in most of the failed cases, the monomers do not have more than one functional groups which allow them to react recursively and form a chain.
E GENERALITY OF POLYRETRO
Pol colyRetr can be adapted to learn to solve combinatorial search problems where the search targets have additional recursive constraints. As an example, we will show how to apply searching symbolic programs for integer addition tasks (Kaiser & Sutskever 2015 Trask et al. 2018 Cai et al.) 2017). Integer addition of arbitrary length is an elementary skill to human. However, is proven to be very difficult for standard neural networks, because needs simultaneously learning of single digit operations and the concept of recursion. This can be formulated using PolyRet if we view the search policy for program for single digit operation the monomer retrosynthesis model, and the recursion as the polymerization prediction step. In the few-shot learning case, domain adaptation would be useful transfer knowledge across domains, such as integer addition in other bases, the current one. Both the recursion and few-shot learning challenges can be handled by Po yRet ro. Other examples include:
learning sorting program (Cai et al. 2017 Li al. 2020), where we need to design sorting algorithm with recursive structures that solve the sorting of sub-sequences, and adapt to domains with different "comparison function" needed for sorting;
learning shift-reduce algorithm for parsing (Dyer et al. (0166), where we need to search over shift/reduce polices in the recursive parsing algorithm, and adapt to domains with different grammars (e.g., different domain specific languages).
13


Reviews:
[{"review_id": "mrWiPp8KdqZ", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper focuses on polymer retro synthesis problem. This is a novel problem and is very challenging because of the very small amounts of training data available (<100). They use reaction templates collected from small molecule reactions and formulate polymer retrosynthesis as a constrained optimization problem. The authors claim that this is the first learning method that takes constraints in polymer retrosynthesis problem.\n\nThe paper is well written and easy to follow. Overall I am positive about the paper as it proposes a new important tasks and provides a method that works well to tackle the task. The usage of reaction templates from small molecules as well as the ability to deal with with small amounts of training data is potentially useful and can serve as a model for few shot modeling in this domain for other tasks as well.\n\nAs for weaknesses,  I think the the baseline transformer performance of 0% top-50 recovery can be improved (and hence not a representation of true baseline). For example, can unsupervised pretraining be used in some way to improve its performance? Or can the small molecule reaction prediction data be used (aka in Molecule Transformer [1]) to seed the transformer model? \n\n[1] Schwaller et al: Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "kmfIdi7vV9L", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "# summary #\nThis paper is concerning the retrosynthesis of polymerizations from monomer candidates. \n \nA polymer is a very long chain of unit polymers. Therefore conventional retrosynthesis models do not suit well for polymerization since they focus on applications on small molecules. \nThe paper defines a problem of predicting a unit polymer and monomers, given a polymer repeat unit (Eq. 5). \nA unit polymer can be decomposed into a polymer repeat unit and end-groups. \nThus the first component of the polyretro is a probabilistic proposal of the repeat-unit + end-groups using a one-step reaction model based on template reactions (Eq.6). \nThe second component of the polyretro is to choose combinations of monomers to synthesize the polymer unit (Eq.7). \nExperimental validations reveal that the proposed polyretro algorithm can achieve 71% tope-30 recovery accuracy in unit polymer prediction, and 50% top-5 recovery accuracy for monomer prediction. \n\n# comments #\nI have been working on the retrosynthesis for smaller molecules and enjoys the paper reading. \nI think this polymer challenge is quite new, and polymer synthesis is important for real applications. \n\nOverall, the paper is well written and readability is satisfactory. \nProblem definition of Eq. 5 seems straightforward but at the same time reasonable. \nTechnical sections (Sec 4. and 5) are easy to read: finds little difficulties to understand the main idea. \n\nI cannot identify the technical novelty of the proposed polyretro method. \nIn Sec 3.2, \"we use Retro*\", but which parts of the algorithm are borrowed from Retro* and which parts are not? \n\nSince this is a new challenge, it is difficult to assess the significance of the target recovery rates in experiments. \nThe Transformer baseline completely fails to recover targets in the experiments. This result itself is important to confirm that the typical retrosynthesis model is incapable. \nHowever, such a ``weak baseline is not much informative to assess the significance of the proposed method. \nThe paper can be further empowered if the paper is equipped with additional experiments on a baseline method that actually works (not zero recovery scores). It can be a non-ML approach or a commercial software with block-box algorithms. \n\nMinor issue:\nIn Fig. 1, the light pink of the polymer is invisible depending on the display/printer environments. \n\n# evaluations #\n(++) First attempt of retrosynthesis for polymer reactions, polymers are important in practical applications\n\n(+) Readability is satisfactory in general. \n\n(-) Technical novelty is unclear for me\n\n(-) Significance of the target recovery scores is unclear to me", "extra_scores": {}, "extra_text": {}}, {"review_id": "ZEA6s9titLI", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "##########################################################################\n \nSummary: \n\nThis paper proposes a method for the retrosynthesis prediction of polymers. A challenge in this problem is the lack of synthetic data for polymers. The method attempts to leverage models for small molecule retrosynthesis predictions (where there is more abundant data), as well as domain specific constraints derived from the chemistry of a particular class of polymerization reactions. The method is shown to outperform some baselines that are commonly used in small molecule retrosynthesis.\n\n##########################################################################\n\nReasons for score: \n\nOverall, I vote for acceptance. I think the paper proposes a novel approach for polymer retrosynthesis that performs better than some of the baseline methods. However, I have some concerns, especially about the overall problem formulation of polymer retrosynthesis.\n\n##########################################################################\n\nStrengths:\n\n*Paper is written clearly\n\n*Interesting multistep method and applied constraints to convert a polymer repeat unit to monomers\n\n*Some useful ablation studies\n\nWeaknesses:\n\n*Some concerns about the relevance of the overall problem formulation\n\n*The method focuses on a particular type of polymerization reaction called condensation polymerization. Not sure how generalizable this method is to other common industrially relevant polymerization reactions, such as chain-growth type reactions \n\n*The evaluation dataset is very small (52 examples)\n\n##########################################################################\n\nQuestions and other comments:\n\n*I wonder how practically relevant the overall polymer retrosynthesis problem is, at least in the way that it is currently presented. In small molecule synthesis (eg in medicinal chemistry or natural product synthesis) there is a well-defined target molecule containing the properties that we want, so it is useful to perform retrosynthesis on the target molecular structure to obtain the step by step synthesis procedure that describes how to create the target molecule from precursor starting materials. However, in polymer synthesis, there is a much less defined target polymer structure (we have a distribution of different polymer structures), and the synthetic procedure required to create the target polymer in condensation polymerization is typically a single step mixing of the monomer building blocks. In my opinion, the problem of converting the unit polymer to monomers and subsequently to precursor starting materials makes sense (and this is the aspect of the work that is similar to typical small molecule retrosynthesis because the unit polymer and monomers are small molecules). But the polymer induction part of the modeling, where we start with a given polymer repeat unit and convert it to the unit polymer, makes much less sense because in a real use case why wouldn’t you just perform the analysis (eg designing a new polymer or performing retrosynthetic analysis) directly on the unit polymer or monomer building blocks, with the knowledge that the target polymer structure is essentially just a repeated form of the unit polymer structure.\n\n*How important is the polymer induction step to find the possible end groups? My intuition is that there are not that many possible end groups for condensation polymerization. It would be interesting to see the distribution of the average number of unit polymer candidates for each of the 52 examples.\n\n*The additional seq2seq baseline for monomer proposal in Figure 7, where the repeat unit is converted to the unit polymer using a very simple heuristic (by adding the most common end group pairs) shows pretty competitive performance compared to the proposed PolyRetro model, and I think this simple heuristic is actually a very reasonable addition to the baselines. \n\n*The PolyRetro model seems to have very marginal improvements over the PolyRetro-USPTO model, which seems to suggest a simple one step retrosynthesis model is already pretty good for the polymer induction step? \n\n*Any thoughts about how to obtain more data for future developments in this area?\n\n\n", "extra_scores": {}, "extra_text": {}}, {"review_id": "YX4OyPWmnO", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "Summary\n- This paper aims at solving an important yet challenging task, the polymer retrosynthesis problem.\n- This paper formulates the condensation polymerization as a constrained optimization with prior structural knowledge.\n- This paper proposes a framework to solve the problem and demonstrates its effectiveness in a few-shot benchmark.\n\n---\n\nStrengths\n- Although I'm not an expert in chemistry, I already know that retrosynthesis is a very important problem in academia and industry. Thus, polymer retrosynthesis also seems to be practically important.\n- This paper well explains how to apply a single-step retrosynthesis model (trained in a reaction benchmark) into the polymer retrosynthesis problem. All sub-steps (except domain adaptation) are reasonable and easy to understand.\n\n---\n\nConcern (or Weakness) \\#1: Domain adaptation\n- The domain adaptation is emphasized throughout this paper. However, I think this adaptation is hard to provide a meaningful gain in general, (a) fundamentally in the few-shot setting and (b) practically as shown in Figure 4 (right). Details of (a) and (b) are described below.\n  - (a) In the few-shot setting, the prior empirical distribution $\\hat{p}(T)$ is often meaningless. This is because the reaction templates are very diverse, e.g., 40k reactions in USPTO-50k has 10k different templates, so the distribution is often very sparse, especially in the few-shot setting. In this case, optimizing Equation (6) cannot improve $p(u|r)$. In my opinion, the authors' choice, using atom- and bond-counting features, might alleviate this issue, however, I'm not sure what is the (chemical) meaning of this simplified distribution, and it can generalize to other polymer benchmarks.\n  - (b) As illustrated in Figure 4 (right), the adaptation does not provide any gain in the monomer prediction even though it depends on $p(u|r)$ as described in the caption of Figure 4. It means that the stability constraint will filter out some wrong predictions in $p(u|r)$ from PolyRetro-USPTO. It is then hard to say that the domain adaptation is helpful for polymer retrosynthetic optimization (Definition 4).\n- Since tempalte-based models are used and $u$ is determined when $T$ is given, the PolyRetro model with domain adaptation can be written as $p_\\text{PolyRetro}(T|r)=\\lambda p_\\text{USPTO}(T|b-A-A-c) + (1-\\lambda)\\hat{p}(T)$ where $p_\\text{USPTO}(T|b-A-A-c)$ is the pre-trained single-step model. Am I correct? The notations are somewhat confused because all density functions are denoted by $p$.\n- Hyperparameter settings:\n  - How to decide $\\lambda$ without the validation set? I think one fold should be used for validation.\n  - What $h$ is used for experiments? Is $h$ in the $\\hat{p}(T)$ the same as used in Definition 4?\n\nConcern (or Weakness) \\#2: Methodology Novelty\n- All sub-steps (except domain adaptation) are just filtering processes using a pre-trained single-step model. While the authors well-formulate the polymer retrosynthesis problem and well-explain the sub-steps, they seem somewhat straightforward. I am willing to give credit for this work because this is the first one (to my knowledge), but I am concerned about novelty in terms of methodology.\n\nConcern (or Weakness) \\#3: No qualitative study\n- In retrosynthesis, top-k accuracy cannot reflect practical scenarios since multiple solutions can exist. Thus many papers often provide their success/failure cases and allow readers to evaluate them. In my opinion, such studies are critical in this field because it can provide additional insight into a chemist even though a prediction is wrong.\n\nConcern (or Weakness) \\#4: Baselines\n- What is the support of $p(u|r)$? I'm curious about how RandomProposal works.\n- I cannot be sure that RandomProposal and Transformer are good baselines. As I mentioned in the second concern, designing Poly Retro-USPTO is somewhat straightforward given the constraints of the proposed problem. Thus I think PolyRetro-USPTO is a more proper and stronger baseline.\n- In the Appendix, there are stronger baselines than RandomProposal and Transformer. Why are they in the Appendix? I think they should be presented in the main paper.\n- I want to know I correctly understand how mlp-retro and seq2seq-retro work. As far as I understand, they follow three sub-steps: (a) Given $r$, apply a pre-trained single-step retrosynthesis model (template-based mlp or template-free seq2seq) into $H-r-H$; (b) If the model obtains $H-B-b+c-C-H\\rightarrow H-r-H$; (c) Then, the final monomers are $b-B-b$ and $c-C-c$ and the unit polymer is $b-B-C-c$. If I'm right, I wonder why mlp-retro is failed (because it is similar to PolyRetro-USPTO), and seq2seq-retro is not working for additional polymers as described in Appendix (because it just uses a pre-trained model). If I'm wrong, that can be a stronger baseline than RandomProposal/Transformer.\n- Is Transformer trained using only one training fold from scratch?\n\n---\n\nQuestions\n- What is $\\mathcal{R}_{m1,m2}$? I think its definition should be provided.\n- Are the end-groups ($b$, $c$ in papers' definitions) of polymers often unknown? Or, are they not important? I wonder why only the repeat unit $r$ is given in the polymer retrosynthesis problem.\n- Why the monomers should be symmetric? I think $b-B-b' + c'-C-c \\rightarrow b-A-c + b'+c'$ can be another available candidate under the stability constraint (Definition 2).\n\n---\n\nConclusion: While this paper focuses on an important problem, I have several concerns mentioned above, so  I think this paper is on the borderline.\n", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a novel problem of polymer retrosynthesis, and a method to solve it. The authors formally define the polymer retrosynthesis optimization problem as a constrained problem to identify the monomers and the unit polymer, with the recursive and stability constraints. Further, since the main challenge with polymer retrosynthesis is the extremely scarce training data, the authors propose a domain adaptation technique that can utilize a single-step retrosynthesis model trained on a large amount of data. The authors also use Retro* [Chen et al. 20] for synthesizability check of the monomers. The proposed method, PolyRetro, is validated against few naive baselines for top-k recovery performance, and is shown to outperform them. 

All reviewers found the problem of polymer retrosynthesis tackled to be important as well as novel, and the paper to be very well-written. However, all reviewers had a common concern on the limited technical novelty and meaningless baselines that makes it difficult to evaluate the significance of the results. Some of the reviewers were also concerned with the insignificant performance gain with the proposed domain adaptation technique (PolyRetro vs. PolyRetro-USPTO in Figure 4), and its limited applicability to a condensation polymerization. The authors provided new results with more baselines, which fine-tune the single-step retrosynthesis model (MLP, seq-to-seq) trained on USPTO.

The below is the summary of pros and cons:

**Pros**
- The tackled polymer retrosynthesis problem is novel and practically important. 
- The proposed problem formulation and constraints are interesting and make sense.
- The paper is well-written and easy to follow even for non-domain experts.

**Cons**
- The proposed solution with recursive and stability constraints is rather straightforward, as well as the use of Retro* for screening out the monomers. 
- The domain adaptation technique, which is advertised as an important contribution to combat extreme data scarcity, is both straightforward and yields small performance gain.
- The baselines in the original version of the paper are simply meaningless strawmans, and the new baseline (seq2seq-retro) in Section D of the Appendix seems quite strong, making it difficult to validate the effectiveness of the proposed method.

The paper received split reviews, with three leaning toward acceptance and one leaning toward rejection. After the interactive discussion period with the authors, the reviewers had an in-depth discussion, where all reviewers agreed that the technical novelty or contribution to the general machine learning field, or general applicability to polymer synthesis is limited. The reviewers did not reach a consensus, which makes the paper a borderline case, and after the discussion with the program chairs, we decided to reject the paper due to the unresolved concerns.   

I believe that the proposed problem-specific solution is adequate, although it has little technical novelties, since this is an application paper. However what is more problematic is the inconclusive experimental validation results due to lack of meaningful baselines. I suggest the authors to compare against seq2seq retro + Retro* in order to properly validate the effectiveness of the proposed method. Also, results in Figure 7, or the polymerization examples in Section A of the appendix should be incorporated into the main paper. I also suggest that the authors drop domain adaptation from the title since it constitutes a small part of the method and thus is misleading. 
Decision: Reject