Predicting Enzymatic Reactions with a Molecular Transformer

Kreutter, David K.; Schwaller, Philippe; Reymond, Jean‐Louis

doi:10.26434/chemrxiv.13161359

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…More recently, a new class of reaction fingerprints that are learned directly from data have emerged. Schwaller et al 28,37,38 used the Transformer 39 natural language processing model to learn fingerprints from reaction SMILES string 40 . Wei et al 41 developed the first learnable graph neural network (GNN) reaction fingerprints based on GNN molecule descriptors 42,43 .…”

Section: Introductionmentioning

confidence: 99%

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

et al. 2022

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“…Nevertheless, RetroBioCat has the merit of having pioneered the first chemoinformatic approach for easing the adoption of biocatalytic reactions in chemical reaction tasks. More recently, Kreutter et al 19 presented a forward reaction prediction model based on the Molecular Transformer. 20 This approach exploits a multitask transfer learning to train a Molecular Transformer architecture, originally trained with chemical reactions from the US Patent Office (USPTO) data set, with 32,000 enzymatic transformations, each one annotated with the corresponding enzyme name.…”

Section: Introductionmentioning

confidence: 99%

Molecular Transformer-aided Biocatalysed Synthesis Planning

Probst¹,

Manica²,

Teukam³

et al. 2021

Preprint

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Enzyme catalysts are an integral part of green chemistry strategies towards a more sustainable and resource-efficient chemical synthesis. However, the use of enzymes on unreported substrates and their specific stereo- and regioselectivity are domain-specific knowledge factors that require decades of field experience to master. This makes the retrosynthesis of given targets with biocatalysed reactions a significant challenge. Here, we use the molecular transformer architecture to capture the latent knowledge about enzymatic activity from a large data set of publicly available biochemical reactions, extending forward reaction and retrosynthetic pathway prediction to the domain of biocatalysis. We introduce the use of a class token based on the EC classification scheme that allows to capture catalysis patterns among different enzymes belonging to the same hierarchical families. The forward prediction model achieves an accuracy of 49.6% and 62.7%, top-1 and top-5 respectively, while the single-step retrosynthetic model shows a round-trip accuracy of 39.6% and 42.6%, top-1 and top-10 respectively. Trained models and curated data are made publicly available with the hope of promoting enzymatic catalysis and making green chemistry more accessible through the use of digital technologies.

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Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

Wen

Blau

Xie

et al. 2022

Preprint

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Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models, such as those based on graph neural networks (GNNs), must be trained on a large amount of labelled data in order to avoid overfitting the data and thus possessing low accuracy and transferability. In this work, we propose a strategy to leverage unlabelled data to learn accurate ML models for small labelled chemical reaction data. We focus on an old and prominent problem—classifying reactions into distinct families—and build a GNN model for this task. We first pretrain the model on unlabelled reaction data using unsupervised contrastive learning and then fine-tune it on a small number of labelled reactions. The contrastive pretraining learns by making the representations of two augmented versions of a reaction similar to each other but distinct from other reactions. We propose chemically consistent reaction augmentation methods that protect the reaction center and find they are the key for the model to extract relevant information from unlabelled data to aid the reaction classification task. The transfer learned model outperforms a supervised model trained from scratch by a large margin. Further, it consistently performs better than models based on traditional rule-driven reaction fingerprints, which have long been the default choice for small datasets. In addition to reaction classification, the effectiveness of the strategy is tested on regression datasets; the learned GNN-based reaction fingerprints can also be used to navigate the chemical reaction space, which we demonstrate by querying for similar reactions. The strategy can be readily applied to other predictive reaction problems to uncover the power of unlabelled data for learning better models with a limited supply of labels.

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Predicting Enzymatic Reactions with a Molecular Transformer

Cited by 3 publications

References 26 publications

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

Molecular Transformer-aided Biocatalysed Synthesis Planning

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

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