# Generative LLMs in Organic Chemistry: Transforming
## 2 Related Work  
Predicting the optimal procedures for organic reactions, including esterification, is a complex task due to several factors. Firstly, esterification is an umbrella term for a foundational reaction in organic chemistry, which is responsible for forming esters from diverse processes [12]. The Fischer esterification, a classical example, involves the transformation of a carboxylic acid and an alcohol with an acid catalyst [13]. A more contemporary technique is the Steglich esterification, which engages carbodiimides, like EDAC (1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide) and DMAP (4-Dimethylaminopyridine), to expedite the interaction between carboxylic acids and alcohols [14]. Additionally, the Mitsunobu reaction, which deploys DEAD (Diethyl azodicarboxylate) along with triphenylphosphine, offers an alternative for the synthesis of esters from primary and secondary alcohols [15]. The solvent's role in esterification reactions cannot be understated, dictating aspects like the equilibrium and reaction speed. Familiar solvents, such as DMF (Dimethylformamide), DCM (Dichloromethane), and THF (Tetrahydrofuran), are usually used in these reactions [16; 17]. Historically, chemists have relied on empirical knowledge, documented procedures, and iterative experimentation to determine the best conditions for a given synthesis [18]. However, with the increasing complexity of organic molecules containing various functional groups and the need for efficient and sustainable synthesis, there is a growing demand for computational tools that can predict the procedures and parameters of reactions. Several computational methods have been developed to predict reaction conditions, utilizing databases of known reactions. These methods, while promising, often require extensive computational resources and may not always provide accurate predictions for novel or less-studied reactions [19]. The evolution of computational methods has seen a pivot toward using machine learning and artificial intelligence to interpret reaction datasets. These techniques excel at discerning patterns within the data, thereby possibly improving the predictions for novel reactions. Contrasting with previous approaches, such as the one employing a transformer-based sequence-to-sequence model which attained a BLEU score of 54.7 through text-based representations [20], our research undertakes a similar predictive task but diverges in several key aspects. We introduce a novel and specific dataset for the esterification reaction, employ an alternative procedural notation, and utilize a distinct linguistic framework. Furthermore, our methodological innovation is in processing only the SMILES representations of the molecules that are implicated in the reactions, focusing the input specifically on the transformative elements of the reactions. Deep learning (DL) methodologies have shown remarkable success in various chemistry tasks [21]. Recent advancements in transformer architectures have significantly impacted molecular generation in drug discovery. Noteworthy models such as MolBERT, ChemGPT, T5 Chem, MolFormer, Chemformer, and BARTSmiles, which employ NLP techniques, demonstrate this influence [22; 23; 24; 25; 26; 27]. For example, Chemformer and T5 Chem are pre-trained on extensive SMILES strings from ZINC-15 [28] and Pubchem, respectively, and are fine-tuned for chemical prediction tasks. Additionally, graph transformers, such as RetroExplainer, have been used in retrosynthesis, automating organic chemistry processes through a transparent, deep learning-guided molecular assembly process [29]. Further broadening the scope of DL methodologies, Galactic has been trained on diverse scientific data, including chemical information, with a character-based approach to SMILES tokenization [30]. A novel contribution to this field is nach0, an encoder-decoder LLM pre-trained on the scientific literature and molecule strings, and excelling in various chemical and biological tasks with its ability to generate high-quality molecular and textual outputs [31].