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Regioselectivity and Stereochemistry of Hydroboration

Regioselectivity and Stereochemistry of Hydroboration

A significant aspect of hydroboration–oxidation is the regio- and stereochemical outcome of the reaction.
Hydroboration proceeds in a concerted fashion with the attack of borane on the π bond, giving a cyclic four-centered transition state. The –BH2 group is bonded to the less substituted carbon and –H to the more substituted carbon. The concerted nature requires the simultaneous addition of –H and –BH2 across the same face of the alkene giving syn...

Regioselectivity of Electrophilic Additions to Alkenes: Markovnikov's Rule

Regioselectivity of Electrophilic Additions to Alkenes: Markovnikov's Rule

If a set of reactants can yield multiple constitutional isomers, but one of the isomers is obtained as the major product, the reaction is said to be regioselective. In such reactions, bond formation or breaking is favored at one reaction site over others.
The hydrohalogenation of an unsymmetrical alkene can yield two haloalkane products, depending on which vinylic carbon takes up the halogen. However, one product usually predominates, where hydrogen adds to the vinylic carbon bearing the...

Regioselectivity and Stereochemistry of Acid-Catalyzed Hydration

Regioselectivity and Stereochemistry of Acid-Catalyzed Hydration

The rate of acid-catalyzed hydration of alkenes depends on the alkene's structure, as the presence of alkyl substituents at the double bond can significantly influence the rate.

Regioselectivity of Electrophilic Additions-Peroxide Effect

Regioselectivity of Electrophilic Additions-Peroxide Effect

In the presence of organic peroxides, the addition of hydrogen bromide to an alkene yields the isomer that is not predicted by Markovnikov’s rule. For example, the addition of hydrogen bromide to 2-methylpropene in the presence of peroxides gives 1-bromo-2-methylpropane. This addition reaction proceeds via a free radical mechanism, which reverses the regioselectivity. The free radical reaction mechanism involves three stages: initiation, propagation, and termination.

Predicting Products: Substitution vs. Elimination

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

Electrophilic 1,2- and 1,4-Addition of HX to 1,3-Butadiene

Electrophilic 1,2- and 1,4-Addition of HX to 1,3-Butadiene

The electrophilic addition of hydrogen halides such as HBr to alkenes and nonconjugated dienes gives a single product as per Markovnikov’s rule.

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Updated: Jun 27, 2025

Versatile CO2 Transformations into Complex Products: A One-pot Two-step Strategy

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Leveraging Language Model Multitasking To Predict C-H Borylation Selectivity.

Ruslan Kotlyarov¹, Konstantinos Papachristos², Geoffrey P F Wood²

¹Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.

Journal of Chemical Information and Modeling

|May 6, 2024

Summary

This summary is machine-generated.

Predicting regioselectivity in C-H borylation reactions is challenging. A fine-tuned T5Chem language model accurately predicts reaction products and identifies reactive sites in drug-like molecules.

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Area of Science:

Organic Chemistry
Computational Chemistry
Medicinal Chemistry

Background:

C-H borylation is crucial for synthesizing pharmaceutical lead candidates due to versatile downstream coupling reactions.
Regioselectivity prediction in C-H borylation, particularly for complex drug-like molecules with heterocycles, remains a significant challenge.

Purpose of the Study:

To evaluate the efficacy of a language model, T5Chem, for predicting C-H borylation reaction outcomes.
To assess the model's performance in both product generation and site reactivity classification for borylation reactions.

Main Methods:

Utilized a dataset of borylation reactions sourced from Reaxys.
Fine-tuned a T5Chem multitask language model, originally trained on USPTO_500_MT patent data.
Evaluated the model's ability to predict reaction products and classify reactive C-H bonds.

Main Results:

The fine-tuned T5Chem model achieved 79% accuracy in generating the correct C-H borylation product.
The model demonstrated high performance in classifying reactive aromatic C-H bonds, with 95% accuracy and 88% positive predictive value.
Performance surpassed that of specialized graph-based neural networks.

Conclusions:

Language models, specifically T5Chem, offer a powerful and accurate approach for predicting C-H borylation regioselectivity.
This computational strategy can aid in the efficient design and synthesis of pharmaceutical compounds.
The model's predictive capabilities exceed current graph-based methods, paving the way for improved reaction prediction tools.