Structure-reactivity modeling using mixture-based representation of chemical reactions

Area of Science:

• Computational chemistry
• Chemical informatics

Background:

• Accurate prediction of chemical reaction outcomes is crucial for drug discovery and materials science.
• Existing reaction representation methods often rely on explicit reaction center labeling, which can be complex and limit applicability.

Purpose of the Study:

• To develop a novel, more robust method for representing chemical reactions.
• To improve the prediction accuracy of chemical reaction models, particularly for novel products.

Main Methods:

• Representing reactions as a combination of reactant and product mixtures, encoded using simplex descriptors (SiRMS).
• Generating feature vectors via concatenation or difference of product and reactant descriptors.
• Implementing a rigorous "product-out" cross-validation (CV) strategy for realistic performance evaluation.
• Applying the methodology to model E2 reaction rate constants, incorporating a fragment control domain applicability approach.

Main Results:

• The new "mixture" reaction representation eliminates the need for explicit reaction center labeling.
• The "product-out" CV strategy provides a more reliable estimation of prediction accuracy for novel reactions.
• Models utilizing the fragment control domain applicability approach showed significantly increased prediction accuracy.
• The proposed method outperformed models relying on explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.

Conclusions:

• The novel mixture-based reaction representation is effective and overcomes limitations of previous methods.
• The "product-out" CV strategy is essential for accurately assessing model generalizability to new reactions.
• This approach enhances the predictive power of computational models for chemical kinetics and reaction outcomes.