To this end, we envisioned an ML-assisted approach to identify a suitable triarylborane that is able to efficiently catalyze the reductive alkylation of amino acids and peptides with H2 through the construction of an in-silico library that includes a variety of unprecedented triarylboranes. Moreover, through the construction of this in-silico library, we aimed to contribute to the structural diversification of triarylboranes beyond the archetypical B(C6F5)3, They identified this combination of borane and DBU using a cheminformatics-assisted approach that profiled the theoretically predicted catalyst activity based on the intrinsic acidity and basicity of the Lewis components. Herein, we report the construction of an in-silico library with 54 triarylboranes, including 46 unprecedented structures, which was used for the ML-assisted identification of the optimal borane for the catalytic reductive alkylation of amino acids and peptides with aldehydes and H2 (Figure ). We also explored the functional-group compatibility of the present system using the functional group evaluation (FGE) kit recently proposed by Morimoto and Oshima et al., which is based on the concept of robustness screening that has been proposed by Glorius et al.