Tensor algebra-based geometric methodology to codify central chirality on organic molecules.

Area of Science:

• Computational Chemistry
• Cheminformatics
• Quantitative Structure-Activity Relationship (QSAR) studies

Background:

• Traditional methods for encoding molecular chirality in Quantitative Structure-Activity Relationship (QSAR) models often use a simplified binary system (1 and -1) for R/S stereocenters.
• This binary approach may limit the ability to capture the nuanced impact of stereochemistry on molecular properties and biological activity.
• Existing QSAR models based on classical QuBiLS-MIDAS descriptors can be enhanced by incorporating more sophisticated chirality descriptors.

Purpose of the Study:

• To introduce a novel mathematical procedure for codifying chiral features of organic molecules within the QuBiLS-MIDAS framework.
• To generalize the weighting of molecular vectors beyond the conventional 1/-1 correction factors.
• To demonstrate the feasibility and advantages of using a wider range of correction factors for chirality encoding in QSAR modeling.

Main Methods:

• Developed a generalized mathematical procedure to assign real number values (in the range [-3, 3] with 0.25 steps) as correction factors for R/S labeled atoms.
• Introduced 'Chiral QuBiLS-MIDAS 3D-Molecular Descriptors' (3D-MDs) that can be non-symmetric and reduced to classical descriptors when symmetry is not codified.
• Applied these novel descriptors in QSAR modeling on four diverse chemical datasets, focusing on factors with opposed values.

Main Results:

• QSAR models built using the novel Chiral QuBiLS-MIDAS 3D-MDs with multiple correction factors demonstrated superior robustness and predictive ability.
• Performance improvements were observed compared to models using only the classical 1/-1 correction factor and existing literature models.
• The feasibility of the proposed method was successfully demonstrated across four distinct chemical datasets.

Conclusions:

• The novel strategy for codifying chirality offers a significant advancement over traditional methods for computing chirality-based descriptors.
• This approach provides a suitable alternative for developing QSAR models with enhanced predictive power for symmetry-dependent properties.
• The generalized correction factors allow for a more nuanced representation of stereochemical information in molecular descriptors.