Recent advances in multidimensional QSAR (4D-6D): a critical review

Area of Science:

• Computational Chemistry
• Medicinal Chemistry
• Drug Discovery

Background:

• Quantitative Structure-Activity Relationship (QSAR) is a reliable computational technique for predicting molecular properties and biological activity.
• QSAR models have evolved from 0D to 2D, with limitations leading to the development of 3D-QSAR.
• 3D-QSAR utilizes molecular properties and non-covalent interactions for drug design, with advancements like COMFA, COMSA, and COMMA.

Purpose of the Study:

• To review the evolution and advancements of multidimensional QSAR (3D to 6D-QSAR).
• To emphasize the advantages of higher-dimensional QSAR models over lower-dimensional ones.
• To highlight recent reports and future directions in predictive QSAR studies for drug discovery.

Main Methods:

• Review of existing literature on 0D to 6D-QSAR methodologies.
• Analysis of advancements and limitations of different QSAR dimensions.
• Focus on comparative advantages of multidimensional QSAR approaches.

Main Results:

• 3D-QSAR, while useful, has limitations that spurred the development of 4D, 5D, and 6D-QSAR.
• Multidimensional QSAR approaches offer enhanced predictive power for drug discovery.
• The review covers recent developments and comparative benefits across 3D to 6D-QSAR models.

Conclusions:

• Higher-dimensional QSAR models represent new paradigms in drug discovery due to improved predictive capabilities.
• Understanding the advantages of each QSAR dimension is crucial for effective drug design.
• The future of QSAR lies in these advanced multidimensional approaches for more accurate predictions.