Another field in organic chemistry that benefits from DS and ML is computer-assisted structure elucidation (CASE). Earlier, the CASE, such as DENDRAL , was solely based on rules, without any AI implementation. Furthermore, systems such as CHEMICS were built on top of sophisticated rules and larger databases without AI aid. In the middle 1990s, systems such as SpecSov and SISTEMAT , which combined the database approach and ANN, were released. For the following years, this approach has become the standard for CASE with some developments, such as incorporating genetic algorithms and decision trees . Owing to AI and ML algorithms, there has been considerable development in CASE in the last decade. One of the many challenges in CASE that has benefited from ML is the prediction of the chemical shifts in NMR, which is used to determine the validity of a proposed chemical structure. Message-passing neural networks have been trained to predict and store 13 C NMR shifts with high accuracy . Advanced ML algorithms such as SVM and XGBoost have been sed to train ML models to predict the chemical classes of natural products based on the naproc13 database . This is one of the most comprehensive natural products databases publicly available. The ML models predict the probability of a specific 13 C NMR spectrum belonging to a particular substance class with an average accuracy of 0.85-0.98, depending on the class. Thus, ML models such as ANN, SVM, and XGBoost tools can be valuable tools to aid structure elucidation. The use of computer and information technology in designing drugs and their synthesis plans has been of interest to chemists for decades and is now improved by ML and DS tools. Early synthesis planning systems such as SYNCHEM2 were based on chemical compound databases and decision rules, which limited the scope of the system owing to the existence of hundreds of reaction mechanisms. Therefore, to overcome such limitations, chemists have been applying DS and ML to retrosynthetic analysis, which is a task that requires experience and expertise. The availability of large reaction databases such as Reaxys has allowed the development of powerful ML models to predict and optimize reaction conditions such as temperature, catalysis, and solvents . Because optimizing reaction conditions is not a straightforward task in classical experiments, DS and ML have become valuable tools for organic chemists because they can recommend reaction conditions when other methods, such as quantum chemistry, fail . Even computational methods of choice for predicting reaction and reaction conditions, such as quantum chemistry and ab initio methods, are now combined with ML methods to accelerate orbital calculations . ML and DS have been applied to design the synthesis of a new metal-organic framework (MOF) . In this study, authors have used automated processes and natural language processing to extract the data available in databases and in the literature to create a new dataset that was used to train ML models for predicting the final experimental conditions and synthesis protocols of new MOF structures.