The utilisation of ML techniques has produced a surge of novel tools for materials discovery in recent years. However, various challenges emerge depending on the specific objectives. In the context of material synthesis, the primary challenge relates to the limited availability of the negative information for model learning and validation, highlighting the importance of better reporting of these data to enable more powerful predictive models in the future. This study presents an ML tool designed to help researchers identify the most promising synthesisable [metal, linker] combinations at the initial stages of novel MOF material design.