xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on...

Area of Science:

• Computational chemistry
• Cheminformatics
• Quantitative structure-activity relationship (QSAR) studies

Background:

• Existing molecular descriptors often rely on specific molecular conformations, limiting their applicability.
• There is a need for alignment-free descriptors that account for molecular flexibility and 4D properties.
• The previously developed MaP descriptor offered invariance but required specific starting conformations.

Purpose of the Study:

• Introduce a novel, alignment-free molecular descriptor named xMaP (flexible MaP descriptor).
• Extend the MaP descriptor to the fourth dimension (4D) by incorporating conformational ensembles.
• Develop a robust QSAR method that is independent of molecular starting conformation and aids in visualization.

Main Methods:

• Generated molecular conformer ensembles via conformational searches.
• Computed molecular surface approximations and projected properties onto these surfaces.
• Clustered similar property areas into patches and converted their spatial distribution into an alignment-free descriptor.
• Applied chemometric regression tools to identify important descriptor variables for interpretation and visualization.

Main Results:

• The xMaP descriptor demonstrated robustness due to the use of conformer ensembles.
• Statistical performance of xMaP was comparable to alignment-dependent methods like GRID/PLS on benchmark datasets.
• xMaP proved effective in visualizing quantitative structure-activity relationships.

Conclusions:

• xMaP is a powerful, alignment-free 4D molecular descriptor suitable for QSAR studies.
• The descriptor's ability to use conformer ensembles enhances its reliability and applicability.
• xMaP offers a valuable tool for both predictive modeling and the interpretation of structure-activity relationships.