Bisphosphine ligand conformer selection to enhance descriptor database representation: improving statistical...

Area of Science:

• Computational Chemistry
• Catalysis
• Molecular Modeling

Background:

• Conformer generation and selection are critical for computationally derived molecular features.
• Feature values can depend on conformer choice, impacting mechanistic understanding, especially in homogeneous enantioselective catalysis.
• High computational costs limit conformer analysis for large, flexible systems.

Purpose of the Study:

• To develop a practical and chemically intuitive conformer selection tool for bisphosphine-ligated palladium(II) dichloride complexes.
• To balance the representation of molecular conformations with computational expense.
• To improve the quality of statistical models using conformer-weighted features.

Main Methods:

• Development of a novel conformer selection methodology tailored for specific organometallic complexes.
• Generation of conformer-weighted molecular features.
• Application of weighted features to existing statistical modeling case studies.

Main Results:

• The developed tool offers a practical balance between conformational representation and computational cost.
• Conformer-weighted features significantly improved the predictive power in two statistical modeling case studies.
• The methodology demonstrated enhanced model quality compared to unweighted features.

Conclusions:

• The proposed conformer selection method is efficient and effective for bisphosphine-ligated palladium(II) dichloride complexes.
• This approach has the potential to be broadly applicable to other complex molecular systems.
• Improved molecular feature generation through conformer selection enhances predictive modeling in computational chemistry.