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With the help of this approach, new carbene reaction 12 and two new elimination reactions leading to the formation of synthetically important dienes 13 were discovered. Then, an alternative method based on the generation of the complete sets of non-isomorphic spanning subgraphs of a given graph was suggested. Its application led to the discovery of previously unknown reactions: the thermal decomposition of α-formyl-oxy ketones 1, 9, and the formation of a cage molecule from N-methoxycarbonyl homopyrrole and tropone 10. This approach supports the hierarchical representation of organic reactions and deals explicitly with heteroatoms and charges, keeps track of rings in molecules 10. Another approach implemented in the IGOR 1, 9 and IGOR2 10 programs concerned the algebraic model of constitutional chemistry developed by Dugundji and Ugi 11. In the 1970s, these studies were significantly expanded by Hendrickson 3, Arens 4, 5, 6, Zefirov, and Tratch 7, 8 who considered various formal schemes describing bonds redistribution for different types of pericyclic reactions. The beginning of a systematic approach to the search for new reactions was laid in 1967 by Balaban, who applied the graph theory for systematical enumeration of pericyclic reactions proceeding through a 6-membered transition state 2. Most of the new reactions have been discovered by plain luck, and it has been up to the chemists to notice the discovery and apply their “chemical intuition” to study it in detail 1. Such reactions are often given the names of their discoverers, which is the highest recognition of their contribution to organic chemistry. Each new reaction enriches the arsenal of synthetic tools and opens new horizons in the development and optimization of new drugs and materials. The discovery of new organic reactions has always been in the focus of synthetic organic chemistry. These can be critically analyzed by the expert, cleaned of irrelevant functional groups and eventually experimentally attempted, herewith enlarging the synthetic purpose of popular synthetic pathways. Novel latent space points were sampled around a map area populated by Suzuki reactions and decoded to corresponding reactions. The autoencoder latent space was visualized on a generative topographic map. A sequence-to-sequence autoencoder with bidirectional Long Short-Term Memory layers was trained on on-purpose developed “SMILES/CGR” strings, encoding reactions of the USPTO database. Furthermore, when coupled to reaction space cartography, de novo reaction design may be focused on the desired reaction class. Here we show that “creative” AI may be as successfully taught to enumerate novel chemical reactions that are stoichiometrically coherent. The “creativity” of Artificial Intelligence (AI) in terms of generating de novo molecular structures opened a novel paradigm in compound design, weaknesses (stability & feasibility issues of such structures) notwithstanding.