Learning to Make Generalizable and Diverse Predictions for Retrosynthesis

1 INTRODUCTION . This paper proposes a novel approach for one-step retrosynthesis . This task is crucial for material and drug manufacturing ( Corey & Wipke , 1969 ; Corey , 1991 ) and aims to predict which reactants are needed to generate a given target molecule as the main product . For instance , Figure 1 demonstrates that the input molecule “ [ N- ] = [ N+ ] =NCc1ccc ( SCCl ) cc1 ” , expressed here as a SMILES string ( Weininger , 1988 ) , can be generated using reactants “ CSc1ccc ( CN= [ N+ ] = [ N- ] ) cc1 ” and “ ClCCl ” . For decades , this task has been solved using template-based approaches ( Gelernter et al. , 1990 ; Satoh & Funatsu , 1999 ) . Templates encode transformation rules as regular expressions operating on SMILES strings and are typically extracted directly from the available training reactions . The primary limitation of such templates is coverage , i.e. , it is possible that none of the templates applies to a test molecule . In order to better generalize to newer or broader chemical spaces , recently developed template-free approaches cast the problem as a sequence-to-sequence prediction task . These approaches were first explored by Liu et al . ( 2017 ) using LSTM models ; the current state-of-the-art performance on this task uses Transformer models ( Lin et al. , 2019 ; Karpov et al. , 2019 ) . Out-of-the-box Transformers nevertheless do not effectively generalize to rare reactions . For instance , model accuracy drops by 25 % on reactions with 10 or fewer representative instances in the training set.1 Another key issue is diversity . Manufacturing processes involve a number of additional criteria — such as green chemistry ( having low detrimental effects on the environment ) . It is therefore helpful to generate a diverse collection of alternative ways of synthesizing the given target molecule . However , predicted reactions are unlikely to encompass multiple reaction classes ( see Figure 2 ) without additional guidance . This is because the training data only provides a single reactant set for each input target , even if this is not the only valid reaction to synthesize the target . We extend molecular Transformers to address both of these challenges . First , we propose a novel pre-training approach to drive molecular representations to better retain alternative reaction possibilities . Our approach is reminiscent of successful pre-training schemes in natural language processing ( NLP ) applications ( Devlin et al. , 2018 ) . However , rather than using conventional token masking methods , we adopt chemically-relevant auxiliary tasks . Each training instance presents a single way to decompose a target molecule into its reactants . Here , we add alternative proxy decompositions for each target molecule by either 1 ) randomly removing bond types that can possibly break during reactions , or 2 ) transforming the target based on templates . While neither of these two auxiliary tasks are guaranteed to construct valid chemical reactions , they are closely related to the task of interest . Indeed , representations trained in this manner provide useful initializations for the actual retrosynthesis problem . To improve the diversity of predicted reactions , we incorporate latent variables into the generation process . Specifically , we merge the Transformer architecture with a discrete mixture over reactions . The role of the latent variable is to encode distinct modes that can be related to underlying reaction classes . Even though the training data only presents one reaction for each target molecule , our model learns to associate each reaction with a latent class , and in the process covers multiple reaction classes across the training set . At test time , a diverse collection of reactions is then obtained by collecting together predictions resulting from conditioning on each latent class . Analogous mixture models have shown promise in generating diverse predictions in natural language translation tasks ( He et al. , 2018 ; Shen et al. , 2019 ) . We demonstrate similar gains in the chemical context . We evaluate our model on the benchmark USPTO-50k dataset , and compare it against state-ofthe-art template-free baselines using the Transformer model . We focus our evaluation on top-10 accuracy , because there are many equally valuable reaction transformations for each input target , though only one is presented in the data . Compared to the baseline , we achieve better performance overall , with over 13 % increase in top-10 accuracy for our best model . When we create a split of the data based on different reaction templates ( a task that any template-based model would fail on ) , we similarly observe a performance increase for our model . Additionally , we demonstrate that our model outputs exhibit significant diversity through both quantitative and human evaluations . 2 RELATED WORK . Template-based Models Traditional methods for retrosynthetic reaction prediction use templatebased models . Templates , or rules , denote the exact atom and bond changes for a chemical reaction . 1See Appendix A for details on how this dataset is constructed . Coley et al . ( 2018 ) applies these templates for a given target compound based on similar reactions in the dataset . Going one step further , Segler & Waller ( 2017 ) learns the associations between molecules and templates through a neural network . Baylon et al . ( 2019 ) uses a hierarchical network to first predict the reaction group and then the correct template for that group . However , to have the flexibility to generalize beyond extracted rules , we explore template-free generative models . Molecule Generation There are two different approaches to generative tasks for molecules , demonstrated through graph and SMILES representations . The graph-generation problem has been explored in Li et al . ( 2018 ) as a node-by-node generation algorithm , but this model does not guarantee the validity of the output chemical graph . Jin et al . ( 2018a ; b ) improves upon this method using a junction-tree encoder-decoder that forces the outputs to be constrained in the valid chemical space ; however , these models require complex , structured decoders . We focus on the generative task of SMILES string representations of the molecules , which has been explored in Kusner et al . ( 2017 ) and Gómez-Bombarelli et al . ( 2018 ) . Pre-training Pre-training methods have been shown to vastly improve the performance of Transformer models in NLP tasks without additional data supervision . Devlin et al . ( 2018 ) use a masked language modeling objective to help their model learn effective representations for downstream tasks . Similar pre-training methods on molecules have been explored by Hu et al . ( 2019 ) , where they mask out atoms in molecular graphs . Meanwhile , our work does not use a masked objective , but instead creates pre-training tasks that are relevant to the retrosynthesis prediction problem . 3 BACKGROUND . Given an input target molecule , the task of retrosynthetic reaction prediction is to output likely reactants that can form the target product . Formally , we express a molecule as a text string via its SMILES representation , and cast our task into a sequence-to-sequence ( seq2seq ) prediction problem ( example shown in Figure 1 ) . For this task , the input target is always a single molecule , while the output predictions are usually a set of more than one molecule concatenated by separators “ . ” . To provide more intuition for this generative task , we describe some properties of SMILES strings . Each SMILES string is 1-D encoding of a 2-D molecular graph . If the predicted SMILES does not adhere to the SMILES grammar , then a valid molecular graph can not be reconstructed . Moreover , each molecule has many equivalent SMILES representations , as a single instance of its SMILES is just a graph traversal starting at some arbitrary node . Therefore , two very different SMILES string can encode the same molecule ( see Appendix B ) , and the model needs to be robust to the given input . One method , proposed by Schwaller et al . ( 2019 ) , augments the input data with different SMILES strings of the same input target molecule . For our model architecture , we apply a Transformer model for the seq2seq task , which has an encoder-decoder structure ( Vaswani et al. , 2017 ; Schwaller et al. , 2019 ) . The encoder maps an input sequence of tokens ( from the SMILES string ) to a sequence of continuous representations , which are then fed to the decoder to generate an output sequence of tokens one element at a time , auto-regressively . Once the model is trained , a beam search procedure is used at inference time to find likely output sequences . The main building block of the Transformer architecture lies in its global self-attention layers , which are well-suited for predictions of the latent graph structure of SMILES strings . For example , two tokens that are far apart in the SMILES string could be close together topologically in the corresponding molecular graph . The global connectivity of the Transformer model allows it to better leverage this information . Additionally , since SMILES follow a rigid grammar requiring long rangedependencies , these dependencies can be more easily learned through global attention layers ( see Appendix B ) . 4 APPROACH . Despite the flexible architecture of Transformer models , we recognize that there are ways to improve model generalization . Additionally , there is no inductive bias for proposing diverse outputs . We propose two techniques to enhance the base molecular Transformer model , which we describe now . 4.1 PRE-TRAINING . In the data , each input target molecule is associated with a single reaction transformation — though there are many equally good chemical reactions . Therefore , for each input target , we construct several new prediction examples that are chemically meaningful , and pre-train the model on these auxiliary examples . We do so without requiring additionally data , or data supervision . The two variants of our method are described in detail below , with examples shown in Figure 3 . Random pre-training For each input target molecule , we generate new examples by selecting a random bond to break . The types of bonds that we consider are acyclic single bonds , because these are the bonds most commonly broken in chemical reactions . As we break an acyclic bond , the input molecule is necessarily broken up into two output molecules , each being a subgraph of the input molecule . Although the examples generated by this method do not cover the entire space of chemical reactions ( for instance some reactions do not break any bonds at all ) , these examples are easy to generate and cover a diverse range of transformations . Template-based pre-training Instead of randomly breaking bonds , we can also use the templates extracted from the training data to create reaction examples . An example of a template is shown in Figure 4 : each template matches a specific pattern in the input molecule , and transforms that pattern according to the template specifications . When the matched pattern is a single acyclic bond , this method will generate similar outputs as the random pre-training method , except that templates usually add additional pieces ( functional groups ) to the output example . As shown in Figure 3 , both examples are derived from the same bond broken in the input target molecule , but for the template-based example , an additional functional group was added , matching a more realistic reaction context . On average , for a random input molecule , there are 10 different possible examples that can be extracted from the random pre-training method , while there are over 200 different possible examples that can be extracted using the template-based pre-training method . However , many of these 200 examples represent similar chemical transformations , only differing in the type of functional group added . More broadly speaking , we can say that the template pre-training method generates more chemically valid reactions compared to the random pre-training method . However , the advantage of the random pre-training method is that it can break bonds that are not represented within the templates , thereby perhaps conferring a higher ability to generalize . As routine , the model is pre-trained on these automatically constructed auxiliary tasks , and then used as initialization to be fine-tuned on the actual retrosynthesis data .

Summary: Given a target compound, the authors suggest a method to predict likely chemical reactants to produce the target. The authors provide a transformer based model to predict the reactants. Existing methods do not generalize well for rare reactions and the training data has only one reactant set for each target even though that may not be the only way to synthesize the compound. To solve this problem, the authors use a pretraining method similar to BERT. Instead of just using token masking, they provide alternate proxy decompositions for a target molecule by randomly removing bond types that are likely to break and by transforming the target based on known templates.