Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions

Abstract

This work describes a method to vectorize and Machine-Learn, ML, non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry. Models trained on this representation predict correct face of approach in ca. 90 % of Michael additions or Diels-Alder cycloadditions. These accuracies are significantly higher than those based on traditional ML descriptors, energetic calculations, or intuition of experienced synthetic chemists. Our results also emphasize the importance of ML models being provided with relevant mechanistic knowledge; without such knowledge, these models cannot easily "transfer-learn" and extrapolate to previously unseen reaction mechanisms.

Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

“…[6] In this area, Sigmansmulti-variant linear regression approach [10] has reached remarkable success in the predictive modelling of asymmetric transformations. Using machine learning (ML) strategies,independent studies from Denmark, [11] Sunoj, [12] Corminboeuf [13] and Grzybowski [14] have shown that AI model is able to capture the statistical pattern of chiral induction and achieve accurate enantioselectivity prediction in stereoselective synthesis.I n addition, as eries of innovative ML technologies have been applied in the prediction of reactivity, [15] chemo- [16] and regioselectivity [17] of organic transformations.…”

Data-driven multi-objective optimization tactics for catalytic asymmetric reactions

“…While logistic regression is an established ML technique, it is underutilized in organic chemistry. 38,39,40 The algorithm identified a bivariate classification using buried volume and total ligand dipole (Fig. 5A, graph 2 and Fig.…”

RegioML: predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning

“…Many useful reactions are underutilised in synthetic organic chemistry because of an inability to predict the regioselectivity of the reaction, 1 and there is thus an increasing interest in developing regioselectivity prediction methods for such reactions. Recent examples include nucleophilic 2,3 and electrophilic aromatic substitution reactions, 4–9 Diels–Alder reactions, 10,11 Heck reactions, 12 radical C–H functionalisation of heterocycles, 13 and reactions such as alkylations, Michael additions, and aldol condensations that proceed through proton abstraction. 14 These methods have been based on either quantum chemical (QM) calculations, 2,5,6 machine learning (ML) trained on experimental data, 8,10–12 or a combination of the two where QM has either provided descriptors for the ML model 3,9 or was used to augment the training data.…”

“…Recent examples include nucleophilic 2,3 and electrophilic aromatic substitution reactions, 4–9 Diels–Alder reactions, 10,11 Heck reactions, 12 radical C–H functionalisation of heterocycles, 13 and reactions such as alkylations, Michael additions, and aldol condensations that proceed through proton abstraction. 14 These methods have been based on either quantum chemical (QM) calculations, 2,5,6 machine learning (ML) trained on experimental data, 8,10–12 or a combination of the two where QM has either provided descriptors for the ML model 3,9 or was used to augment the training data. 13,14 However, these approaches have rarely been compared on the same dataset.…”