PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation

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 more than 50 % successfully recovers the currently-in-used polymerization processes in the real world . 1 INTRODUCTION . Human beings are living in a world of chemical products , among which a category of chemicals , called polymers , is playing an 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 circumstances , 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 , to 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 AI 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 multifold , but one of the most important ones being the lack of available polymerization datasets , which poses difficulties for existing learning-based methods to learn meaningful pattern for polymerization reactions . Moreover , polymers usually have a 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 design/retrosynthesis . In this paper , we focus specifically on the polymer retrosynthesis problem . While there has been a series of work focusing on small molecule retrosynthesis ( Corey & Wipke , 1969 ; Gasteiger et al. , 1992 ; Coley et al. , 2017 ; Liu et al. , 2017 ; Segler & Waller , 2017 ; Segler et al. , 2018 ; Coley et al . ; Karpov et al. , 2019 ; Baylon et al . ; Schreck et al . ; Dai et 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 . 2 . Polymerization data for training is 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 in our case it is 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 PolyRetro , a novel learning-based search framework to tackle the problem of polymer retrosynthesis . With beam search and rejection sampling , PolyRetro is able to propose 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 a template prior learned on tiny-sized polymerization data . To verify whether the proposed monomers are synthesizable , we employ Retro * ( Chen et al. , 2020 ) , a multi-step retrosynthesis model to predict their synthesis routes . We demonstrate PolyRetro through experiments that it is able to predict monomers accurately given target polymer repeat units . To our knowledge , we are the first to 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 to 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 is 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 as a constrained optimization problem . To our knowledge , this is the first machine learning formulation that takes constraints in polymer retrosynthesis into consideration . • We propose PolyRetro , a learning-based search framework that tackles the problem of polymer retrosynthesis . To our knowledge , this is also the first learning-based method in this problem setting . • PolyRetro is able to recover 53 % of ground truth monomers for a 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-free ones . A template of chemical reaction is 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 ; Segler & Waller , 2017 ; Baylon 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 use sequence-to-ssequence model to directly predict SMILES 1 representation of reactants ( Liu et al. , 2017 ; Karpov et al. , 2019 ) . Such methods could be straight-forwardly applied to any reaction data , but may need a 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 prediction ( Segler et al. , 2018 ; Schreck et al . ; Kishimoto et al. , 2019 ; Chen et al. , 2020 ) . This multi-step procedure is usually decomposed into two modules , a one-step 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 , a paucity of work has tackle the specific problem of polymer retrosynthesis . We focus on polymer retrosynthesis and bring knowledge from general retrosynthesis literature to help tackle the problem . 3 BACKGROUND . In this section , we focus on providing the background knowledge about molecule retrosynthesis as well as defining notations . This serves as the building block in our polymer retrosynthesis modeling . Given a molecule m ∈M whereM indicates the space of molecules , the molecule retrosynthesis problem focuses on finding a set of reactants S ⊂ M that can be used to synthesize m. Before introducing the approaches for retrosynthesis , we first cover the background on reaction templates . 3.1 REACTION TEMPLATE . A reaction template T : = oT → rT1 +rT2 + . . .+rT|T | is a graph rewriting rule 2 that rewrites subgraph pattern oT that is matched with target molecule m , into rTi that appears in i-th reactant si ∈ 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 oT and m , where itself is an NP-hard problem , such approach provides a tractable way of finding candidate set S with chemical rules . 3.2 LEARNING-BASED 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 in structured prediction setting . We here mainly focus on the ML approaches for such problem , as some of these provide probabilistic interpretations that will be needed in our optimization framework . Depends on the number of reaction steps needed to synthesize m , such problem can be categorized into one-step and multi-step retrosynthesis . One-step molecule retrosynthesis : One-step setting requires that R : = 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 ( S|m ) ∝ ∑ T∈T p ( T |m ) I [ SubgMatch ( oT , m ) ] ( 1 ) where SubgMatch ( · ) operator checks the subgraph matching between oT and m. Multi-step molecule retrosynthesis The multi-step extension allows using multiple reactionsRm = { Rmi } |Rm| i=1 to synthesize m , with the restriction that the reactants set S ⊂ I ⊂M where I is the set of starting molecules . This is essentially a 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 state-of-the-art approach that provably optimizes the synthesizability ofRm . 1https : //www.daylight.com/dayhtml/doc/theory/theory.smiles.html . 2https : //www.daylight.com/dayhtml/doc/theory/theory.smarts.html

Summary: 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.