Exploring Machine Learning Tools for the Prediction of the Stability of New Togni-type Reagents

Area of Science:

• Computational Chemistry
• Machine Learning in Chemistry
• Chemical Synthesis

Background:

• Predicting chemical compound stability is challenging, especially for novel or unstable compounds.
• Existing machine learning approaches for stability prediction require extensive data, often unavailable for unstable compounds.
• Togni-type reagents are a class of kinetically unstable compounds where stability assessment is crucial.

Purpose of the Study:

• To develop a machine learning approach for predicting the intermediate structures of Togni-type reagents.
• To overcome the limitations of transition state analysis for stability prediction in chemical compounds.
• To enable prediction of stability and reactivity for a broader range of Togni-type reagents.

Main Methods:

• Replaced transition state searches with the identification of local minima along isomerization pathways.
• Utilized a dataset of 382 Togni-type reagents with known behavior.
• Trained a machine learning model to predict intermediate forms based on these minima.

Main Results:

• Successfully demonstrated the machine's ability to predict the intermediate form of Togni-type reagents.
• The new method provides a viable alternative to computationally intensive transition state calculations.
• The approach shows promise for predicting the stability and reactivity of these reagents.

Conclusions:

• Focusing on intermediate structures (local minima) is an effective strategy for machine learning-based stability prediction.
• This method expands the applicability of computational tools for assessing Togni-type reagent properties.
• The findings contribute to the rational design and discovery of novel chemical compounds.