RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning

Retrosynthesis , of which the goal is to find a set of reactants for synthesizing a target product , is an emerging research area of deep learning . While the existing approaches have shown promising results , they currently lack the ability to consider availability ( e.g. , stability or purchasability ) of the reactants or generalize to unseen reaction templates ( i.e. , chemical reaction rules ) . In this paper , we propose a new approach that mitigates the issues by reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules . To this end , we design an efficient reactant selection framework , named RETCL ( retrosynthesis via contrastive learning ) , for enumerating all of the candidate molecules based on selection scores computed by graph neural networks . For learning the score functions , we also propose a novel contrastive training scheme with hard negative mining . Extensive experiments demonstrate the benefits of the proposed selection-based approach . For example , when all 671k reactants in the USPTO database are given as candidates , our RETCL achieves top-1 exact match accuracy of 71.3 % for the USPTO-50k benchmark , while a recent transformer-based approach achieves 59.6 % . We also demonstrate that RETCL generalizes well to unseen templates in various settings in contrast to template-based approaches . The code will be released . 1 INTRODUCTION . Retrosynthesis ( Corey , 1991 ) , finding a synthetic route starting from commercially available reactants to synthesize a target product ( see Figure 1a ) , is at the center of focus for discovering new materials in both academia and industry . It plays an essential role in practical applications by finding a new synthetic path , which can be more cost-effective or avoid patent infringement . However , retrosynthesis is a challenging task that requires searching over a vast number of molecules and chemical reactions , which is intractable to enumerate . Nevertheless , due to its utter importance , researchers have developed computer-aided frameworks to automate the process of retrosynthesis for more than three decades ( Corey et al. , 1985 ) . The computer-aided approaches for retrosynthesis mainly fall into two categories depending on their reliance on the reaction templates , i.e. , sub-graph patterns describing how the chemical reaction occurs among reactants ( see Figure 1b ) . The template-based approaches ( Coley et al. , 2017b ; Segler & Waller , 2017 ; Dai et al. , 2019 ) first enumerate known reaction templates and then apply a wellmatched template into the target product to obtain reactants . Although they can provide chemically interpretable predictions , they limit the search space to known templates and can not discover novel synthetic routes . In contrast , template-free approaches ( Liu et al. , 2017 ; Karpov et al. , 2019 ; Zheng et al. , 2019 ; Shi et al. , 2020 ) generate the reactants from scratch to avoid relying on the reaction templates . However , they require to search the entire molecular space , and their predictions could be either unstable or commercially unavailable . We emphasize that retrosynthesis methods are often required to consider the availability of reactants and generalize to unseen templates in real-world scenarios . For example , when a predicted reactant is not available ( e.g. , not purchasable ) for a chemist or a laboratory , the synthetic path starting from the predicted reactant can not be instantly used in practice . Moreover , chemists often require retrosynthetic analysis based on unknown reaction rules . This is especially significant due to our incomplete knowledge of chemical reactions ; e.g. , 29 million reactions were regularly recorded between 2009 and 2019 in Reaxys1 ( Mutton & Ridley , 2019 ) . Contribution . In this paper , we propose a new selection-based approach , which allows considering the commercial availability of reactants . To this end , we reformulate the task of retrosynthesis as a problem where reactants are selected from a candidate set of available molecules . This approach has two benefits over the existing ones : ( a ) it guarantees the commercial availability of the selected reactants , which allows chemists proceeding to practical procedures such as lab-scale experiments or optimization of reaction conditions ; ( b ) it can generalize to unseen reaction templates and find novel synthetic routes . For the selection-based retrosynthesis , we propose an efficient selection framework , named RETCL ( retrosynthesis via contrastive learning ) . To this end , we design two effective selection scores in synthetic and retrosynthetic manners . To be specific , we use the cosine similarity between molecular embeddings of the product and the reactants computed by graph neural networks . For training the score functions , we also propose a novel contrastive learning scheme ( Sohn , 2016 ; He et al. , 2019 ; Chen et al. , 2020b ) with hard negative mining ( Harwood et al. , 2017 ) to overcome a scalability issue while handling a large-scale candidate set . To demonstrate the effectiveness of our RETCL , we conduct various experiments based on the USPTO database ( Lowe , 2012 ) containing 1.8M chemical reactions in the US patent literature . Thanks to our prior knowledge on the candidate reactants , our method achieves 71.3 % test accuracy and significantly outperforms the baselines without such prior knowledge . Furthermore , our algorithm demonstrates its superiority even when enhancing the baselines with candidate reactants , e.g. , our algorithm improves upon the existing template-free approach ( Chen et al. , 2019 ) by 11.7 % . We also evaluate the generalization ability of RETCL by testing USPTO-50k-trained models on the USPTOfull dataset ; we obtain 39.9 % test accuracy while the state-of-the-art template-based approach ( Dai et al. , 2019 ) achieves 26.7 % . Finally , we demonstrate how our RETCL can improve multi-step retrosynthetic analysis where intermediate reactants are not in our candidate set . We believe our scheme has the potential to improve further in the future , by utilizing ( a ) additional chemical knowledge such as atom-mapping or leaving groups ( Shi et al. , 2020 ; Somnath et al. , 2020 ) ; ( b ) various constrastive learning techniques in other domains , e.g. , computer vision ( He et al. , 2019 ; Chen et al. , 2020b ; Hénaff et al. , 2019 ; Tian et al. , 2019 ) , audio processing ( Oord et al. , 2018 ) , and reinforcement learning ( Srinivas et al. , 2020 ) . 2 SELECTION-BASED RETROSYNTHESIS VIA CONTRASTIVE LEARNING . 2.1 OVERVIEW OF RETCL . In this section , we propose a selection framework for retrosynthesis via contrastive learning , coined RETCL . Our framework is based on solving the retrosynthesis task as a selection problem over a candidate set of commercially available reactants given the target product . Especially , we design a selection procedure based on molecular embeddings computed by graph neural networks and train the networks via contrastive learning . To this end , we define a chemical reactionR → P as a synthetic process of converting a reactant-set R = { R1 , . . . , Rn } , i.e. , a set of reactant molecules , to a product molecule P ( see Figure 1a ) . We 1A chemical database , https : //www.reaxys.com aim to solve the problem of retrosynthesis by finding the reactant-setR from a candidate set C which can be synthesized to the target product P . Especially , we consider the case when the candidate set C consists of commercially available molecules . Throughout this paper , we say that the synthetic direction ( fromR to P ) is forward and the retrosynthetic direction ( from P toR ) is backward . Note that our framework stands out from the existing works in terms of the candidate set C. To be specific , ( a ) template-free approaches ( Lin et al. , 2019 ; Karpov et al. , 2019 ; Shi et al. , 2020 ) choose C as the whole space of ( possibly unavailable ) molecules ; and ( b ) template-based approaches ( Coley et al. , 2017b ; Segler & Waller , 2017 ; Dai et al. , 2019 ) choose C as possible reactants extracted from the known reaction templates . In comparison , our framework neither requires ( a ) search over the entire space of molecules , or ( b ) domain knowledge to extract the reaction templates . We now briefly outline the RETCL framework . Our framework first searches the most likely reactantsetsR1 , . . . , RT ⊂ C in a sequential manner based on a backward selection score ψ ( R|P , Rgiven ) , and then ranks the reactant-sets using ψ ( R|P , Rgiven ) and another forward score φ ( P |R ) . For learning the score functions , we propose a novel contrastive learning scheme with hard negative mining for improving the selection qualities . We next provide detailed descriptions of the search procedure and the training scheme in Section 2.2 and 2.3 , respectively . 2.2 SEARCH PROCEDURE WITH GRAPH NEURAL NETWORKS . We first introduce the search procedure of RETCL in detail . To find a reactant-setR = { R1 , . . . , Rn } , we select each elementRi sequentially from the candidate set C based on the backward ( retrosynthetic ) selection score ψ ( R|P , Rgiven ) . It represents a selection score of a reactant R given a target product P and a set of previously selected reactantsRgiven ⊂ C. Note that the score function is also capable of selecting a special reactant Rhalt to stop updating the reactant-set . Using beam search , we choose top T likely reactant-setsR1 , . . . , RT . Furthermore , we rank the chosen reactant-setsR1 , . . . , RT based on the backward selection score ψ ( R|P , Rgiven ) and the forward ( synthetic ) score φ ( P |R ) . The latter represents the synthesizability ofR for P . Note that ψ ( R|P , Rgiven ) and φ ( P |R ) correspond to backward and forward directions of a chemical reaction R → P , respectively ( see Section 2.1 and Figure 1a ) . Using both score functions , we define an overall score on a chemical reactionR → P as follows : score ( P , R ) = 1 n+ 2 ( max π∈Π n+1∑ i=1 ψ ( Rπ ( i ) |P , { Rπ ( 1 ) , . . . , Rπ ( i−1 ) } ) + φ ( P |R ) ) , ( 1 ) whereRn+1 = Rhalt and Π is the space of permutations defined on the integers 1 , . . . , n+1 satisfying π ( n+1 ) = n+1 . Based on score ( P , R ) , we decide the rankings of R1 , . . . , RT for synthesizing the target product P . We note that the maxπ∈Π operator and the 1n+2 term make the overall score ( equation 1 ) be independent of order and number of reactants , respectively . Figure 2 illustrates this search procedure of our framework . Score design . We next elaborate our design choices for the score functions ψ and φ . We first observe that the molecular graph of the product P can be decomposed into subgraphs from each reactant of the reactant-set R , as illustrated in Figure 1a . Moreover , when selecting reactants sequentially , the structural information of the previously selected reactants Rgiven should be ignored to avoid duplicated selections . From these observations , we design the scores ψθ ( R|P , Rgiven ) and φ ( P |R ) as follows : ψ ( R|P , Rgiven ) = CosSim ( fθ ( P ) − ∑ S∈Rgiven gθ ( S ) , hθ ( R ) ) , φ ( P |R ) = CosSim ( ∑ R∈R gθ ( R ) , hθ ( P ) ) , where CosSim is the cosine similarity and fθ , gθ , hθ are embedding functions from a molecule to a fixed-sized vector with parameters θ . Note that one could think that fθ and gθ are query functions for a product and a reactant , respectively , while hθ is a key function for a molecule . Such a query-key separation allows the search procedure to be processed as an efficient matrix-vector multiplication . This computational efficiency is important in our selection-based setting because the number of candidates is often very large , e.g. , |C| ≈ 6× 105 for the USPTO dataset . To parameterize the embedding functions fθ , gθ and hθ , we use the recently proposed graph neural network ( GNN ) architecture , structure2vec ( Dai et al. , 2016 ; 2019 ) . The implementation details of the architecture is described in Section 3.1 . Incorporating reaction types . A human expert could have some prior information about a reaction type , e.g. , carbon-carbon bond formation , for the target product P . To utilize this prior knowledge , we add trainable embedding bias vectors u ( t ) and v ( t ) for each reaction type t into the query embeddings of ψ and φ , respectively . For example , φ ( P |R ) becomes CosSim ( ∑ R∈R gθ ( R ) + v ( t ) , hθ ( P ) ) . The bias vectors are initialized by zero at beginning of training .

Summary: This submission describes an approach to single-step retrosynthesis based on contrastive learning that selects reactants that can be used to synthesize a target product in a single step. The stated contributions are (1) an approach to retrosynthesis that is constrained to only select “available” starting materials; and (2) a novel contrastive learning scheme with hard negative mining. The use of a contrastive loss to learn an embedding of reactants and their products that exhibit the property that the sum of reactant vectors has a high cosine similarity to the product vector is clever. This is a nice way to give structure to a continuous vector space. The strategy of hard negative mining is also clever and identifies the examples that would intuitively be the most informative.