Generative LLMs in Organic Chemistry: Transforming Esterification Reactions into Natural Language Procedures

Abstract

This paper presents a novel approach to predicting esterification procedures in organic chemistry by employing generative large language models (LLMs) to interpret and translate SMILES molecular notation into detailed procedural texts of synthesis reactions. Esterification reaction is important in producing various industrial intermediates, fragrances, and flavors. Recognizing the challenges of accurate prediction in complex chemical landscapes, we have compiled and made publicly available a curated dataset of esterification reactions to enhance research collaboration. We systematically compare machine learning algorithms, ranging from the conventional k-nearest neighbors (kNN) to advanced sequence-to-sequence transformer models, including FLAN-T5 and ChatGPT-based variants. Our analysis highlights the FLAN-T5 model as the standout performer with a BLEU score of 51.82, suggesting that the model has significant potential in enhancing reaction planning and chemical synthesis. Our findings contribute to the growing field of AI in chemistry, offering a promising direction for enhancing the efficiency of reaction planning and chemical synthesis.