# Language Models for Predicting Organic Synthesis Procedures
## 1 Introduction  
Organic chemistry is at the core of multiple industries and plays a pivotal role in the contemporary world. This field involves synthesizing chemicals by carefully choosing and mixing reactants and auxiliary components, such as catalysts, while also considering environmental and temporal factors like reaction temperature and length. Following synthesis, the compound is purified using methods like distillation, crystallization, chromatography, or extraction to ensure purity, with the method selected based on the compound's unique properties. Concise instructions or procedures, commonly referred to by chemists, outline the choice of solvents, precise temperature conditions, and reaction duration, which are critical in achieving the desired product with high yield and purity. The task of delineating the optimal procedure for a given combination of reactants typically necessitates a series of iterative experiments involving various chemical reagents and conditions. Chemists utilize scientific databases such as SciFinder and Reaxys [1] and the peer-reviewed literature to access detailed information on previous experiments and methods for creating knowncompounds. However, creating new compounds or using new combinations of reactants poses significant challenges because there are no established protocols. The emerging field of deep learning (DL) offers a promising solution by potentially predicting synthesis procedures in cases where traditional resources are unavailable. Recent advancements in DL have highlighted its efficacy in modeling chemical properties and reaction dynamics [2; 3; 4; 5; 6]. Interestingly, large language models have revolutionized various fields of artificial intelligence, demonstrating capabilities in natural language processing, text generation, and even specialized domains like chemistry. The rapid progress in LLM development has led to increasingly sophisticated models capable of understanding and generating complex, domain-specific content with high accuracy [7]. In the field of organic chemistry, LLMs present a promising path for predicting synthesis procedures, potentially simplifying the process of developing new compounds and optimizing existing ones [8]. By framing chemistry as a text-to-text problem [9], we leverage the power of language models to interpret and generate chemical language, offering a novel approach to challenges in the field. Viewing chemistry as a text-to-text problem involves representing chemical reactions and processes as sequences of text, allowing them to be processed by language models in a manner similar to natural language. This innovative approach enables the application of natural language processing techniques to chemical problems, to solve tasks such as reaction prediction, retrosynthesis planning, and procedure generation by treating chemical formulas, reactions, and methodologies as a form of specialized language that can be learned and generated by appropriately trained models. Large language models (LLMs) trained on diverse datasets can help in understanding and generating chemical language with precision. Organic synthesis encompasses a wide range of reaction types, including hydrogenation [10], oxidation [11], reduction [12], condensation [13], nucleophilic substitution [14], electrophilic addition [15], and redox reactions [16]. More specialized processes like the Suzuki coupling [17], Diels-Alder reaction [18], Aldol condensation [19], Wittig reaction [20], and Grignard reaction [21] enable chemists to synthesize increasingly complex molecules with precision and diversity. Across a variety of reactions, factors such as solvent choice, reaction temperature, and the use of catalysts influence yields, selectivity, and rates [22]. With the increasing complexity of molecules, especially with multiple functional groups, accurate and efficient synthetic methodologies are vital. However, identifying a synthetic pathway is insufficient, because a comprehensive experimental procedure is needed for each synthesis.