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Inductive Effects on Chemical Shift: Overview

Inductive Effects on Chemical Shift: Overview

The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...

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Related Experiment Video

Updated: Jun 15, 2025

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

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Exhaustive local chemical space exploration using a transformer model.

Alessandro Tibo¹, Jiazhen He², Jon Paul Janet²

¹Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden. alessandro.tibo@astrazeneca.com.

Nature Communications

|August 25, 2024

Summary

This study introduces a new molecular transformer model to explore chemical space. The model uses a similarity kernel to improve generative models, enabling better discovery of related molecules.

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Area of Science:

Computational chemistry
Machine learning in chemistry
Drug discovery

Background:

Understanding molecular similarity is key for optimization in chemistry.
Generative models can explore chemical space but lack similarity awareness.
Current methods struggle to measure coverage of chemical space regions.

Purpose of the Study:

To develop a generative model that explicitly accounts for molecular similarity.
To enable comprehensive exploration of molecular near-neighborhoods.
To improve the guidance of generative models using reinforcement learning.

Main Methods:

A source-target molecular transformer model was developed.
The model was regularized using a similarity kernel function.
Training was performed on a dataset of over 200 billion molecular pairs.

Main Results:

The model enforces a direct relationship between molecule generation and similarity.
Regularization significantly improved the correlation between generation probability and molecular similarity.
The approach enables exhaustive exploration of molecule near-neighborhoods.

Conclusions:

The proposed model effectively addresses limitations in current generative chemistry models.
This method enhances the ability to navigate and explore chemical similarity spaces.
It provides a mechanism for measuring and improving coverage of molecular regions.