The present era of machine learning (ML) and deep learning (DL) techniques and high computing power provides solutions to problems previously treated as untractable. One of them is computer-assisted synthesis planning (CASP) which consists of two tasks: reactions forward planning and retrosynthesis. The former is predicting the outcomes of reaction for given reactants. The latter is a method of planning the synthesis scheme of chemical compounds from simple precursors available in stock, to synthesized intermediates, and the target molecule. Synthesis planning remained a laborious, manual task until the 1960s when Corey formalized the idea of CASP and then implemented it in LHASA software. Over the years, new solutions were developed that automated subsequent planning elements, required less human intervention, and increased the speed and accuracy of algorithms . Over the last decade, several modern, ML-based CASP tools were independently developed: from closed vendor software, e.g. Synthia (previously Chematica) , to the closed source with the available interface, e.g. IBM RXN , and opensource ones, e.g. LillyMol , AiZynthFinder , ASKCOS Tree-builder , AutoSynRoute . Currently, a standard CASP tool consists of three modules: (i) the database of reaction templates and rules on how to apply them to analyzed molecules, (ii) algorithms searching for possible synthetic routes, (iii) a database of in-stock molecules. The aforementioned tools differ significantly in the design of every module. For example, the database of reaction templates may be manually encoded with a rule-based algorithm for reaction prediction, e.g. Synthia. It may be also au- tomatically extracted and reactions may be predicted with a neural network, e.g. LillyMol, AiZynthFinder, and ASKCOS Tree-builder. Finally, reactions may be predicted using a template-free seq2seq algorithm known from natural language processing as implemented IBM RXN.