Unassisted Noise-Reduction of Chemical Reactions Data Sets

Toniato, Alessandra; Schwaller, Philippe; Cardinale, Antonio; Geluykens, Joppe; Laino, Teodoro

doi:10.26434/chemrxiv.12395120.v1

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…The current machine learning revolution in automated synthesis can significantly accelerate novel materials and molecules' development. In the last years, natural language processing methods emerged as robust and effective approaches in the field of organic chemistry, showing promising results in reaction prediction (1; 2; 3; 4), retrosynthesis planning (5; 6; 7; 8), data curation (9) and synthesis action generation (10; 11). In those studies the encoder-decoder transformer models introduced by Vaswani et al (12) excel among all other neural network architectures.…”

Section: Introductionmentioning

confidence: 99%

Data augmentation strategies to improve reaction yield predictions and estimate uncertainty

Schwaller

Vaucher²,

Laino³

et al. 2020

Preprint

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Chemical reactions describe how precursor molecules react together and transform into products. The reaction yield describes the percentage of the precursors successfully transformed into products relative to the theoretical maximum. The prediction of reaction yields can help chemists navigate reaction space and accelerate the design of more effective routes. Here, we investigate the best-studied high-throughput experiment data set and show how data augmentation on chemical reactions can improve yield predictions' accuracy, even when only small data sets are available. Previous work used molecular fingerprints, physics-based or categorical descriptors of the precursors. In this manuscript, we fine-tune natural language processing-inspired reaction transformer models on different augmented data sets to predict yields solely using a text-based representation of chemical reactions. When the random training sets contain 2.5% or more of the data, our models outperform previous models, including those using physics-based descriptors as inputs. Moreover, we demonstrate the use of test-time augmentation to generate uncertainty estimates, which correlate with the prediction errors.

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Section: Introductionmentioning

confidence: 99%

Data augmentation strategies to improve reaction yield predictions and estimate uncertainty

Schwaller

Vaucher²,

Laino³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Exploring machine learning methods for improving or even building new innovative ways of data curation is an opportunity for future research in chemistry and materials science. Such methods for automatic data curation have recently received attention in many disciplines, including the chemical sciences. − For instance, by coupling uncertainty estimation methods exploiting the statistical nature of machine learning methods, one can identify mistakes and anomalies in big data. For example, in cases where the machine learning model is confident in its predictions but large discrepancies are observed with the reported data, the user can be warned to double-check the entry to avoid mistakes in databases.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

The Role of Machine Learning in the Understanding and Design of Materials

2020

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Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.

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“…On the smoothed data sets, the performance of our models more than triples in the gram scale and doubles on the sub-gram scale, achieving R 2 scores of 0.277 and 0.388, respectively. The removal of noisy reactions [32] or reaction data augmentation techniques [33] could potentially lead to further improvements.…”

Section: Gram Versus Sub-gram Scalementioning

confidence: 99%

Prediction of Chemical Reaction Yields using Deep Learning

Schwaller¹,

Vaucher²,

Laino³

et al. 2020

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<div>Artificial intelligence is driving one of the most important revolutions in organic chemistry. </div><div>Multiple platforms, including tools for reaction prediction and synthesis planning based on machine learning, successfully became part of the organic chemists' daily laboratory, assisting in domain-specific synthetic problems. Unlike reaction prediction and retrosynthetic models, the prediction of reaction yields has received less attention in spite of the enormous potential of accurately predicting reaction conversion rates. Reaction yields models, describing the percentage of the reactants converted to the desired products, could guide chemists and help them select high-yielding reactions and score synthesis routes, reducing the number of attempts. So far, yield predictions have been predominantly performed for high-throughput experiments using a categorical (one-hot) encoding of reactants, concatenated molecular fingerprints, or computed chemical descriptors. Here, we extend the application of natural language processing architectures to predict reaction properties given a text-based representation of the reaction, using an encoder transformer model combined with a regression layer. We demonstrate outstanding prediction performance on two high-throughput experiment reactions sets. An analysis of the yields reported in the open-source USPTO data set shows that their distribution differs depending on the mass scale, limiting the dataset applicability in reaction yields predictions. </div>

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Unassisted Noise-Reduction of Chemical Reactions Data Sets

Cited by 7 publications

References 10 publications

Data augmentation strategies to improve reaction yield predictions and estimate uncertainty

Data augmentation strategies to improve reaction yield predictions and estimate uncertainty

The Role of Machine Learning in the Understanding and Design of Materials

Prediction of Chemical Reaction Yields using Deep Learning

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