# Language Models for Predicting Organic Synthesis Procedures
## 8 Conclusions  
This paper evaluated and compared various deep-learning-based algorithms that convert SMILES molecular notation into rich procedural texts of organic synthesis reactions. We have developed a novel dataset for this purpose, which is now publicly accessible online. Our research encompasses a comparison of BART, T5, FLAN-T5, molT5, and classic seq2 seq transformers, all of different sizes and configurations. Most of the models were investigated under three configurations: (1) with both a pre-trained tokenizer and model (denoted as _PP_); (2) a pre-trained tokenizer with a model of randomly initialized weights (denoted as _PC_); and (3) a fine-tuned tokenizer with a model of randomly initialized weights (denoted as _CC_). The experimental investigation revealed three top performers: _FLAN-T5-base-CC_, _molT5-base-PP_, and _molT5-large-PP_. The _molT5-large-PP_ model performs best in terms of BLEU (47.75), ROUGE-L (56.74), 50% Levenshtein match (45.73%), and BLEU g1k (48.52) scores. The highest scores of the 75% Levenshtein match (7.91%) metric are achieved by the _FLAN-T5-base-CC_ model (the pre-trained molT5 model was fine-tuned). The _molT5-base-PP_ configuration is not able to achieve top scores; however, it performed competitively close to the aforementioned top models. The results demonstrate the capability of language models to predict chemical synthesis procedures involving 24 possible distinct actions, many of which include various parameters like solvents, reaction agents, temperature, duration, solvent ratios, and other specific parameters. We demonstrate that only when the core reactants are used as input, models learn to correctly predict what ancillary components need to be included in the resulting procedure. These insights are valuable for AI researchers and chemists, suggesting that curated datasets and language model fine-tuning techniques can be tailored for specific reaction classes and practical applications. We anticipate that future research will focus on (1) the investigation of specific methods to control the generation procedures with additional parameters for enhanced outcome specificity and sequence control; (2) the augmentation of the dataset to encompass a broader range of organic reactions to increase model predictive capabilities; and (3) conducting real-world laboratory validations to assess the practical applicability and reliability of our models. Our goal is to refine these models further based on feedback from empirical trials in real-world settings.  
**Author Contributions:** Conceptualization, M.V. and J.K.-D.; methodology, M.V. and J.K.-D.; software, M.V.; validation, M.V. and J.K.-D.; data curation, M.V.; writing--original draft preparation, M.V.; writing--review and editing, J.K.-D.; visualization, M.V.; supervision, J.K.-D. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Vytautas Magnus University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Datasets and code can be found at ([[https://github.com/Mantas-it/Chem_Procedure_Prediction](https://github.com/Mantas-it/Chem_Procedure_Prediction)]([https://github.com/Mantas-it/Chem_Procedure_Prediction](https://github.com/Mantas-it/Chem_Procedure_Prediction)), accessed on 1 March 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest.