Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space

Lee, Alpha A.; Yang, Qingyi; Sresht, Vishnu; Bolgar, Peter; Hou, Xinjun; Klug-McLeod, Jacquelyn; Butler, Christopher R.

doi:10.1039/c9cc05122h

Cited by 110 publications

(102 citation statements)

References 20 publications

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“…Schwaller et al [22] recently proposed to ignore reactant and reagent roles for the reaction prediction task. In contrast to previous works [32,33,35,36], the single-step retrosynthetic model presented here predicts reactants and reagents. In an effort to simplify the prediction task, the most common precursors with a length of more than 50 tokens were replaced by molecule tokens.…”

Section: Molecule Representationmentioning

confidence: 58%

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

Schwaller

Petraglia²,

Zullo³

et al. 2019

Preprint

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We present an extension of our Molecular Transformer architecture combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention. The single-step retrosynthetic model sets a new state of the art for predicting reactants as well as reagents, solvents and catalysts for each retrosynthetic step. We introduce new metrics (coverage, class diversity, round-trip accuracy and Jensen-Shannon divergence) to evaluate the single-step retrosynthetic models, using the forward prediction and a reaction classification model always based on the transformer architecture. The hypergraph is constructed on the fly, and the nodes are filtered and further expanded based on a Bayesian-like probability. We critically assessed the end-to-end framework with several retrosynthesis examples from literature and academic exams. Overall, the frameworks has a very good performance with few weaknesses due to the bias induced during the training process. The use of the newly introduced metrics opens up the possibility to optimize entire retrosynthetic frameworks through focusing on the performance of the single-step model only.

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Section: Molecule Representationmentioning

confidence: 58%

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

Schwaller

Petraglia²,

Zullo³

et al. 2019

Preprint

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“…An expected feature of machine-learning methods for predictive chemistry is that retraining models on proprietary data ought to allow companies to achieve better predictive ability on chemistries that are used in-house. 58 These in-house chemistries may not be well represented in public or published data sets, which most of the CASP systems are trained on. Researchers from AstraZeneca and the University of Bern applied a workflow for retrosynthetic template extraction 28 and training/application 29 to several public and proprietary data sets and compared the performance of the different models.…”

Section: Section 2: How Is Casp Currently Used In the Pharmaceutical mentioning

confidence: 99%

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis

et al. 2020

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Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.

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“…Schwaller and Lee's group successfully applied a Molecular Transformer model to uncertainty-calibrated chemical reaction prediction [9]. Lee also used the Transformer model to unify reaction prediction and retrosynthesis across pharma chemical space [10]. Experiments by the authors on two machine translation tasks showed that the Transformer was superior to the seq2seq model [5].…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning: Making Retrosynthetic Predictions Based on a Small Chemical Reaction Dataset Scale to a New Level

et al. 2020

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Effective computational prediction of complex or novel molecule syntheses can greatly help organic and medicinal chemistry. Retrosynthetic analysis is a method employed by chemists to predict synthetic routes to target compounds. The target compounds are incrementally converted into simpler compounds until the starting compounds are commercially available. However, predictions based on small chemical datasets often result in low accuracy due to an insufficient number of samples. To address this limitation, we introduced transfer learning to retrosynthetic analysis. Transfer learning is a machine learning approach that trains a model on one task and then applies the model to a related but different task; this approach can be used to solve the limitation of few data. The unclassified USPTO-380K large dataset was first applied to models for pretraining so that they gain a basic theoretical knowledge of chemistry, such as the chirality of compounds, reaction types and the SMILES form of chemical structure of compounds. The USPTO-380K and the USPTO-50K (which was also used by Liu et al.) were originally derived from Lowe’s patent mining work. Liu et al. further processed these data and divided the reaction examples into 10 categories, but we did not. Subsequently, the acquired skills were transferred to be used on the classified USPTO-50K small dataset for continuous training and retrosynthetic reaction tests, and the pretrained accuracy data were simultaneously compared with the accuracy of results from models without pretraining. The transfer learning concept was combined with the sequence-to-sequence (seq2seq) or Transformer model for prediction and verification. The seq2seq and Transformer models, both of which are based on an encoder-decoder architecture, were originally constructed for language translation missions. The two algorithms translate SMILES form of structures of reactants to SMILES form of products, also taking into account other relevant chemical information (chirality, reaction types and conditions). The results demonstrated that the accuracy of the retrosynthetic analysis by the seq2seq and Transformer models after pretraining was significantly improved. The top-1 accuracy (which is the accuracy rate of the first prediction matching the actual result) of the Transformer-transfer-learning model increased from 52.4% to 60.7% with greatly improved prediction power. The model’s top-20 prediction accuracy (which is the accuracy rate of the top 20 categories containing actual results) was 88.9%, which represents fairly good prediction in retrosynthetic analysis. In summary, this study proves that transferring learning between models working with different chemical datasets is feasible. The introduction of transfer learning to a model significantly improved prediction accuracy and, especially, assisted in small dataset based reaction prediction and retrosynthetic analysis.

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Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space

Abstract: We develop a machine learning model that tackles both reaction prediction and retrosynthesis by learning from the same dataset. The model is generalizable across chemical space.

Cited by 110 publications

References 20 publications

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

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

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis

Transfer Learning: Making Retrosynthetic Predictions Based on a Small Chemical Reaction Dataset Scale to a New Level

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