# Generative LLMs in Organic Chemistry: Transforming
## 8 Conclusions  
The aim of this paper is to systematically compare machine learning algorithms, which could interpret and translate SMILES molecular notation into detailed procedural texts of synthesis reactions. A curated dataset of 1200 esterification reactions was prepared specifically for our task and shared publicly. The approach in this paper encompasses a set of fine-tuned generative transformer models (based on FLAN-T5, GPT-3.5-turbo, davinci-002, and babbage-002) and a traditional memory-based algorithm (TF-IDF+kNN) for the prediction of esterification procedures. The FLAN-T5 model emerged as the top performer (with BLEU--51.82), closely followed by GPT-3.5-turbo (BLEU--47.99). The TF-IDF+kNN algorithm, especially using the Levenshtein distance metric, is a good alternative for generative transformer models for our problem. The results illustrate the capabilities of fine-tuned LLMs to be used in the field of chemical synthesis procedure planning and optimization. The novelty of this research stems from the diversity of methodologies examined, highlighted by the first instance of the implementation of a fine-tuned GPT-3.5-turbo model to predict chemical synthesis procedures for esterification reaction, which encompasses 28 distinct actions. The findings of this study apply to AI researchers and chemists who could utilize various datasets and LLM fine-tuning techniques to create specific solutions that are relevant to particular reaction classes. As the field advances, we anticipate an increase in the availability of services tailored for LLM tuning and creation, making these powerful tools more accessible to scientists without programming expertise. As a natural progression, our future research will focus on: (1) further probing into sequence-centric models, such as GPT transformers and other versions of T5 models, given their demonstrated efficacy. Examining the potential for these models to capture more complex reaction mechanisms and possibly predict multi-step synthesis procedures is essential; (2) augmenting our dataset to encompass a broader range of organic reactions and potentially multiple classes of reactions. Such tests measure models' generalizability across various types of chemical reactions; and (3) validating our models in real-world laboratory settings to discern their practical utility and reliability. Through these empirical trials, we would hope to refine the models based on real-world feedback.  
Conceptualization, J.K.-D. and M.V.; software, M.V.; writing--original draft preparation, M.V.; writing--review and editing, J.K.-D. and L.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The datasets and models used in this paper can be found: [[https://github.com/Mantas-it/LLM_Esterification](https://github.com/Mantas-it/LLM_Esterification)]([https://github.com/Mantas-it/LLM_Esterification](https://github.com/Mantas-it/LLM_Esterification)) (accessed on 9 November 2023). Authors [PERSON] and [PERSON] were employed by the company JSC SynHet. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.