Prediction of Nucleophilicity and Electrophilicity Based on a Machine‐Learning Approach

Abstract

Nucleophilicity and electrophilicity dictate the reactivity of polar organic reactions. In the past decades, Mayr et al. established a quantitative scale for nucleophilicity (N) and electrophilicity (E), which proved to be a useful tool for the rationalization of chemical reactivity. In this study, a holistic prediction model was developed through a machine-learning approach. rSPOC, an ensemble molecular representation with structural, physicochemical and solvent features, was developed for this purpose. With 1115 nucleophiles, 285 electrophiles, and 22 solvents, the dataset is currently the largest one for reactivity prediction. The rSPOC model trained with the Extra Trees algorithm showed high accuracy in predicting Mayr's N and E parameters with R 2 of 0.92 and 0.93, MAE of 1.45 and 1.45, respectively. Furthermore, the practical applications of the model, for instance, nucleophilicity prediction of NADH, NADPH and a series of enamines showed potential in predicting molecules with unknown reactivity within seconds. An online prediction platform (http://isyn.luoszgroup.com/) was constructed based on the current model, which is available free to the scientific community.

Intramolecular chaperone-assisted dual-anchoring activation (ICDA): a suitable preorganization for electrophilic halocyclization

“… 36 Nevertheless, in light of the circumstance where the intramolecular reactions mentioned above undergo the same 5- exo -trig cyclization and involve the similar benzylic carbocation acceptors, we attempted to ignore the effect of the correction term 36 and utilized the nucleophilicity of the attacking groups to qualitatively evaluate the relative rate. The nucleophilicity parameters ( N ) of six molecules were predicted with the aid of the r SPOC model developed by Luo et al , 37 and the reaction rates of 1a, 5g, 5i, and 7a show a positive correlation with the N values of their equivalents (Fig. S23, † top).…”

“…The nucleophilicity parameters (N) of six molecules were predicted with the aid of the rSPOC model developed by Luo et al, 37 and the reaction rates of 1a, 5g, 5i, and 7a show a positive correlation with the N values of their equivalents (Fig. S23, † top).…”

Nucleophilicity of 4‐(Alkylthio)‐3‐imidazoline Derived Enamines

“…The same range of reactivities for 4 a – 4 j (6.59< N <9.64) is predicted by the current version of the reactivity structure and physicochemical (rSPOC) machine‐learning algorithm for the prediction of Mayr reactivity parameters, probably because a large set of kinetic data for enamines from Mayr's reactivity database [12f] was available for the training of the artificial intelligence (AI) tool. [22] However, comparison of individual experimental and predicted N values of the enamines shows some scatter (at average: Δ N =0.89, maximum Δ N =2.24 for 4 j ), which may partially be explained by the neglect of considering the individual susceptibility factors s N of enamines in the prognostic AI model. [22] …”

Transforming organic chemistry research paradigms: Moving from manual efforts to the intersection of automation and artificial intelligence

“…By 2023, this research experienced further advancements. Notably, S. Luo and L. Zhang unveiled an enhanced predictive machine learning model [81], as detailed in Figure 10B. Termed as rSPOC, this refined molecular representation amalgamates structural, physicochemical, and solvent features.…”