Decomposing Retrosynthesis into Reactive Center Prediction and Molecule Generation

Abstract

Chemical retrosynthesis has been a crucial and challenging task in organic chemistry for several decades. In early years, retrosynthesis is accomplished by the disconnection approach which is labor-intensive and requires expert knowledge. Afterward, rulebased methods have dominated in retrosynthesis for years. In this study, we revisit the disconnection approach by leveraging deep learning (DL) to boost its performance and increase the explainability of DL. Concretely, we propose a novel graph-based deeplearning framework, named DeRetro, to predict the set of reactants for a target product by executing the process of disconnection and reactant generation orderly. Experimental results report that DeRetro achieves new state-of-the-art performance in predicting the reactants. In-depth analyses also demonstrate that even without the reaction type as input, DeRetro retains its retrosynthesis performance while other methods show a significant decrease, resulting in a large margin of 19% between DeRetro and previous state-of-the-art rule-based method. These results have established DeRetro as a power-ful and useful computational tool in solving the challenging problem of retrosynthetic analysis.

Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning

“…Retrosynthesis has seen increased attention from the data science and cheminformatics communities recently with a number of machine learning efforts leveraging reaction templates or rules, [1][2][3][4] techniques adapted from natural language processing, [5][6][7][8] and graph based models. 9,10 However, only the template and rulebased methods are capable of making a connection from the prediction directly back to the source of the template or rule, which is most likely a reaction that was successfully performed in a laboratory. This ability to provide evidence and reasoning behind the prediction of a molecular transformation makes template-based methods an attractive choice for use in software tools designed for synthetic chemists, and guided the choice to pursue this method in this work.…”

Predicting Retrosynthetic Pathways Using a Combined Linguistic Model and Hyper-Graph Exploration Strategy

“…Here, we dispute the previous use of top-N accuracy [6,9,12,16,[32][33][34][35][36][37] and to introduce four different metrics, namely, round-trip accuracy, coverage, class diversity and Jensen-Shannon divergence [50], as seen in Figure 3, to evaluate single step retrosynthetic models and through them retrosynthetic tools as a whole. All these four metrics have been critically designed and assessed with the help of human domain experts (see Section 4.2 for a detailed description).…”

“…While this extensive production of AI models for Organic chemistry was made possible by the availability of public data [28,29], the noise contained in this data and generated by the text-mining extraction process is heavily reducing their potential. In fact, while rule-based systems [30] demonstrated, through wet-lab experiments, the capability to design target molecules with less purification steps and hence, leading to savings in time and cost [31], the AI approaches [6,9,12,16,[32][33][34][35][36][37][38] still have a long way to go.…”

Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning

“…Retrosynthesis has seen increased attention from the data science and cheminformatics communities recently with a number of machine learning efforts leveraging reaction templates or rules, [1][2][3][4] techniques adapted from natural language processing, [5][6][7][8] and graph based models. 9,10 However, only the template and rulebased methods are capable of making a connection from the prediction directly back to the source of the template or rule, which is most likely a reaction that was successfully performed in a laboratory. This ability to provide evidence and reasoning behind the prediction of a molecular transformation makes template-based methods an attractive choice for use in software tools designed for synthetic chemists, and guided the choice to pursue this method in this work.…”