Inferring experimental procedures from text-based representations of chemical reactions

Alain C. Vaucher (), Philippe Schwaller, Joppe Geluykens, Vishnu H. Nair, Anna Iuliano and Teodoro Laino
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Alain C. Vaucher: IBM Research Europe
Philippe Schwaller: IBM Research Europe
Joppe Geluykens: IBM Research Europe
Vishnu H. Nair: IBM Research Europe
Anna Iuliano: Università di Pisa
Teodoro Laino: IBM Research Europe

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract The experimental execution of chemical reactions is a context-dependent and time-consuming process, often solved using the experience collected over multiple decades of laboratory work or searching similar, already executed, experimental protocols. Although data-driven schemes, such as retrosynthetic models, are becoming established technologies in synthetic organic chemistry, the conversion of proposed synthetic routes to experimental procedures remains a burden on the shoulder of domain experts. In this work, we present data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation, for application in batch organic chemistry. We generated a data set of 693,517 chemical equations and associated action sequences by extracting and processing experimental procedure text from patents, using state-of-the-art natural language models. We used the attained data set to train three different models: a nearest-neighbor model based on recently-introduced reaction fingerprints, and two deep-learning sequence-to-sequence models based on the Transformer and BART architectures. An analysis by a trained chemist revealed that the predicted action sequences are adequate for execution without human intervention in more than 50% of the cases.

Date: 2021
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DOI: 10.1038/s41467-021-22951-1

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