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- W4247259022 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, for the first time, data-driven models for predicting the entire sequence of synthesis steps starting from a textual representation of a chemical equation. 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. When evaluated on the ground truth data, the best performing model (transformer) achieves an accuracy of 72.7% for single action predictions, and a 100% match of the full action sequence for 3.6% of experimental procedures. 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.<br>" @default.
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- W4247259022 date "2020-10-21" @default.
- W4247259022 modified "2023-10-17" @default.
- W4247259022 title "Inferring Experimental Procedures from Text-Based Representations of Chemical Reactions" @default.
- W4247259022 doi "https://doi.org/10.26434/chemrxiv.13118423" @default.
- W4247259022 hasPublicationYear "2020" @default.
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