First of all, we compare our approach to MMP analysis, a popular algorithm for lead optimization. This method is based on extraction of transformation rules from (in the terminology of ML) a training set of molecules, and application of these rules to known actives. Transformations captured in this way are generic and generally applicable, the method is based on the additivity principle, which is one of the main assumptions in medicinal chemistry, and it yields multiple suggested molecules. On the other hand, combinatorial explosion of generated structures limits its applicability, and the additivity principle often violates in practice. By contrast, the method used in this work generates only prioritized structures, evading a combinatorial explosion by taking into account a wider molecular context of input molecules, not limited by the MMP chemical environment radius, and, being highly non-linear by construction, does not imply the additivity.