Data curation and the construction of reliable databases are major issues that also need to be addressed. Poorly curated databases complicate the assessment of the predictive performance of AI models. Combining efforts could, however, facilitate access to new and interesting data. Examples include natural products, metallodrugs, safety, preclinical, and toxicological databases, which complement the current data available in the public domain and offer new perspectives on the known data . There are, however, potential conflicts of interest related to the publication of sensitive data associated with intellectual property. For example, post-marketing (pharmacovigilance) data that might be biased related to the time and clarity of data shared.