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arXiv · 2401.17267

ReacLLaMA: Merging chemical and textual information in chemical reactivity AI models

Aline Hartgers·Ramil Nugmanov·Kostiantyn Chernichenko·Joerg Kurt Wegner
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Abstract

Chemical reactivity models are developed to predict chemical reaction outcomes in the form of classification (success/failure) or regression (product yield) tasks. The vast majority of the reported models are trained solely on chemical information such as reactants, products, reagents, and solvents, but not on the details of a synthetic protocol. Herein incorporation of procedural text with the aim to augment the Graphormer reactivity model and improve its accuracy is presented. Two major approaches are used: training an adapter Graphormer model that is provided with a GPT-2-derived latent representation of the text procedure (ReacLLaMA-Adapter) and labeling an unlabeled part of a dataset with the LLaMA 2 model followed by training the Graphormer on an extended dataset (Zero-Shot Labeling ReacLLaMA). Both methodologies enhance the discernment of unpromising reactions, thereby providing more accurate models with improved specificity.

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Aline Hartgers, Ramil Nugmanov, Kostiantyn Chernichenko, Joerg Kurt Wegner. 2024-01-30. ReacLLaMA: Merging chemical and textual information in chemical reactivity AI models. https://arxiv.org/abs/2401.17267

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