SynMOF database, we trained multiple ML models to predict synthesis conditions of a diverse set of MOFs unseen during training. The input representation of the MOF structures is of crucial importance for the ML models performance. In this study, we used two types of representations as an input for the ML models training: One based on molecular fingerprints of the linkers, extended with encodings of the metal type and its oxidation state (Fig. , SI Section 3.1), and the recently developed MOF representation by Kulik and coworkers (SI Section 3.2). It is to be noted that the MOF field is still expanding, and an increasing amount of new structures and corresponding synthesis parameters will be available over time that can be used for training and refinement of ML models to achieve the highest possible performance. In this case, representation learning methods such as graph neural networks will then likely become more accurate than models relying on handcrafted feature representations. The prediction of synthesis time and temperature was achieved via regression models, such as random forests or neural networks (SI Sections 3.3, 3.4, 3.5). To predict discrete synthesis parameters, such as solvent and additives, classification models could be, in principle, used. However, for multiple reasons this turns out to be impractical: There is a wide variety of possible solvents and additives reported in literature, leading to a large number of categories, and, in turn, strongly imbalanced datasets. Furthermore, the properties of solvents can be very similar, making them interchangeable in synthesis, which leads to ambiguous solutions. In practice, also combinations of various solvents are required for successful MOF synthesis. Therefore, we developed a ML model which predicts solvent properties, such as partition coefficients, boiling point (SI Section 3.6), rather than the specific solvent. A nearest neighbor search in solvent property space yields lists of possible solvents that have properties similar to those predicted by the ML model. In this way, new solvents can be incorporated easily, and even solvents occurring only once in literature can be used to train the model. In the case of additives, we found that the main parameter that distinguishes different additives is their acidity/basicity strength. Thus, we split the dataset into three groups (acidic, basic or no additive) and used a classification model for additive prediction.