Computer-assisted organic chemistry has a huge potential for predicting chemical reaction conditions and for automating synthetic chemistry. In recent years, machine learning (ML) based approaches have been successfully applied to screen libraries of druglike molecules, for quantitative structureactivity relationships (QSAR), for retrosynthetic planning , and for reaction condition prediction. Reactivity prediction is a hard problem that often require specific experimental datasets to train ML models. Traditionally, creating such experimental databases requires a large number of manual experiments to check the feasibility of available starting materials to react together. However, with careful training of ML models using both positive and negative reaction data, it is possible to train on smaller datasets to test specific synthetic objectives. The results from ML models are helpful in building a chemical library that is otherwise tedious to explore by screening each reaction to check substrate feasibility under certain reaction conditions. To date, there is limited literature precedence for prospective prediction of desired chemical reactions and interpreting its reactivity using machine learning methods. We provide a first report, to the best of our knowledge, of a fast and one-pot multicomponent reaction to explore heterogenous reactivity of N-sulfonylimines by training a humaninterpretable machine learning model that identifies chemical patterns of reactivity to predict and test new reactions prospectively. We selected N-sulfonylimines as our model substrate because N-sulfonylimines are one of the important synthons in organic chemistry that are being used for a variety of chemical transformations. Nsulfonylimine is a good source of an electrophilic carbon for radical and nucleophilic addition reactions. There are several reports available for N-sulfonylimines reactions where a carbon-nitrogen double bond is exploited. Notably, the use sulfamidate , a cyclic N-sulfonylimine, has been used to prepare interesting heterocyclic scaffolds. Sulfamidate is transformed into a fused heterocycle using a Michael addition , cycloaddition , arylation , alkenylation , or alkynylation strategy by leveraging electrophilicity of cyclic N-sulfonylimines (Scheme 1). Scheme 1: Strategy to explore N-sulfonylimine reactivity towards multicomponent reaction However, among reported synthetic strategies, construction of direct C-C bond between the imine carbon and the (het)aromatic partner is underrepresented in the literature. Specifically, a synthetic strategy for the direct C-C bond linkage between sulfamidate and oxadiazole has not been explored till date. The oxadiazole scaffold finds a unique presence in many biologically active compounds, pharmaceutical agents, and is a privileged scaffold in material science. Among different types of fivemembered heterocycles, 1,3,4-oxadiazole plays an important role in organic synthesis and medicinal chemistry representing broad spectrum bioactivity as anticancer, antimicrobial, antiviral, and antifungal pharmacological agents (Figure ). For example, the recently discovered CA-170 contains a 1,3,4oxadiazole moiety and is a promising immune checkpoint inhibitor in the tumor microenvironment as a dual antagonist of Programmable death ligand-1 and V-domain Ig suppressor of T-cell activation. Although the structure of CA-170 is not disclosed, a speculated structure is shown in Figure . Conventional approaches to synthesize 1,3,4-oxadiazole is a multistep procedure that includes transformation of carboxylic acid into acyl chloride. Then a nucleophilic substitution reaction with hydrazide to produce an amide bond followed by cyclization step to get a 1,3,4-oxadiazole. Multicomponent reactions (MCRs) have attracted medicinal chemists to prepare chemical libraries of biologically important molecules and drugs using two or more building blocks, often in reduced synthetic steps or one-pot experimental settings. These building blocks are either commercially available or easily synthesizable in the lab. Importantly, MCRs are extremely useful in diversity-oriented drug discovery to prepare diverse chemical libraries in a short time scale compare to traditional synthesis using sequential reactions. But, MCR is highly dependent on the nature of the solvents, catalysts, concentrations and equivalent of reagents being used. Once a set of parameters have been defined, a specific MCR can be used in combinatorial chemistry as well as synthesis by automation. However, it does not warrant the formation of desire products due to the variable reactivity of starting materials. The understanding of chemical reactivity of starting materials for a particular MCR would be useful to identify which starting materials to use. This important information of chemical reactivity can used to make a machine learning model which can suggest a type of starting materials to be used to get desire product successfully which would also reduce the waste of valuable reagents, time and efforts. Keeping this in mind, we selected a model MCR using N-sulfonylimine and carboxylic acid containing starting materials by taking inspiration from the previously reported MCR albeit using different reactants. Ramazani et al. reported a four-component reaction yielding 1,3,4-oxadiazole scaffold using aromatic aldehyde, benzoic acid, N-isocyano triphenylphosphorane (PINC), and secondary amine as reaction partners. The formation of 1,3,4-oxadiazole involves an essential reactant, PINC which is the nucleophilic partner that reacts with the imine. This species is generated in situ from the amine and aldehyde and reacts with a carboxylic acid followed by cyclization to yield 1,3,4-oxadiazole. A similar strategy was extended by Yudin et al. to perform an intramolecular reaction for the synthesis of oxadiazole containing cyclic peptide or macrocycle where two end terminals are stapled to form oxadiazole ring. This strategy also relies upon in situ imine formation from an aldehyde, a secondary amine, and additional amine group. It is noteworthy that in situ formations of imines are not always favorable as it is highly dependent upon its starting materials -an aldehyde and an amine, potentially limiting the use of these approaches. To address this issue, we provide the first report to use Nsulfonylimine as a substrate for a fast and single-step approach to synthesize sulfamidate embedded 1,3,4-oxadiazole using a MCR. We started our investigation with the idea that several types of cyclic N-sulfonylimines (aldimines or ketimines), acyclic N-sulfonylimines, and aromatic imines can be synthesized. To determine the reactivity pattern of various imines with carboxylic acids, we used Fukui reaction parameters calculated using Density Functional Theory (DFT) and identified the most suitable imines using the electrophilicity of the carbon atom (Figure ). Both cyclic and acyclic N-sulfonylimines are highly susceptible toward nucleophilic attack of carboxylic acids. Therefore, we started using the model substrate cyclic Nsulfonylimine (sulfamidate) 1a, which can be easily synthesized from substituted salicylaldehydes. We initially selected benzoic acid as the reaction partner because of its moderate nucleophilic tendency (Figure ) and the selection of optimized conditions for future use with a chemically diverse range of carboxylic acids. Further, the synthesis of other derivatives with the optimized condition would serve as a training dataset to develop a machine learning model.