Topological distance based 3D descriptors for use in QSAR and diversity analysis

Area of Science:

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

Background:

• Topological autocorrelation methods calculate molecular descriptors by summing properties at specific topological distances.
• Existing methods do not fully leverage the 3D structural information inherent in the relationship between topological and Euclidean distances.

Purpose of the Study:

• To propose a modified autocorrelation approach that incorporates 3D structural information.
• To calculate steric, electronic, and indicator-variable-type molecular descriptors using the modified approach.
• To evaluate the utility of these descriptors in QSAR and cluster analysis.

Main Methods:

• Modification of the topological autocorrelation approach to include Euclidean distance information.
• Calculation of steric, electronic, and indicator-variable-type molecular descriptors.
• Application of the descriptors in QSAR studies using three diverse datasets.
• Comparative analysis with established 3D-QSAR methods.

Main Results:

• The developed descriptors effectively capture 3D structural information.
• The descriptors are suitable for both cluster and QSAR analysis.
• Generated QSAR models demonstrate high predictive accuracy.
• The performance of the new models is comparable to other leading 3D-QSAR methods.

Conclusions:

• The modified autocorrelation approach provides a valuable tool for molecular descriptor calculation.
• Incorporating 3D structural information enhances the predictive power of QSAR models.
• This method offers a competitive alternative to existing 3D-QSAR techniques.