After a molecule has been virtually screened for potential bioactivity and toxicology, a chemical synthesis pathway is required for their evaluation in relevant models of disease. Despite knowledge of hundreds of thousands of transformation steps, novel molecules cannot be efficiently synthesized due to novel structural features or conflicting reactivities. AI can help to identify possible and less complicated synthesis routes for compounds simultaneously or sequentially with prediction of bioactivity. Computer-aided synthesis planning can also suggest millions of structures that can be synthesized and predict multiple synthesis routes for each of them. New AI methods can support multiple applications such as analog series identification (fragmentation), de novo drug design signatures study, SAR visualization, reactivity predictions, similarity searching, and visualization of chemical space. Two examples of such methods are Extended Similarity Indices developed by the research group of Miranda-Quintana, and the Structure-Activity Relationships Matrix approach and its deep learning extension by Bajorath et al. A strategy still to be consolidated is data expansion or augmentation using multiple layers of inputs. This approximation could allow the generation of the most representative similarity searching to identify chemical mimetics capable of reverting disease signatures (instead of altering one molecule activity). For example, drug design approaches might be developed for reverting (or preventing) molecular pathway alterations or for predicting toxicity or safety issues for marketed drugs.