Bioactive Molecules: Perfectly Shaped for Their Target?

Area of Science:

• Computational chemistry
• Cheminformatics
• Drug discovery

Background:

• Understanding the relationship between molecular shape and biological activity is crucial for drug discovery.
• The MDL Drug Data Report (MDDR) contains valuable information on bioactive compounds and their targets.
• Describing molecular shape in a quantitative and comparable manner is essential for large-scale analysis.

Purpose of the Study:

• To identify specific molecular shape profiles characteristic of compounds active against particular biological targets.
• To evaluate the utility of Normalized Principal Moments of Inertia Ratios (NPRs) as a descriptor for molecular shape.
• To investigate the distribution of molecular shapes within drug compound datasets.

Main Methods:

• Extraction of target subsets from the MDL Drug Data Report (MDDR).
• Calculation of Normalized Principal Moments of Inertia Ratios (NPRs) to represent molecular shapes.
• Clustering analysis of NPR data in a triangular descriptor space.
• Assessment of the influence of 3D conformer generation methods on shape distribution.

Main Results:

• Significant differences in shape profiles were observed between MDDR target subsets and random datasets.
• Certain regions of the descriptor space were found to be sparsely populated by bioactive compounds for specific targets.
• A general bias towards rod-like molecular shapes was evident across the analyzed datasets.
• The choice of 3D conformer generator and the use of multiple conformations influenced the observed shape distributions.

Conclusions:

• NPRs are effective descriptors for differentiating molecular shapes relevant to biological activity.
• Drug compound datasets exhibit non-random, biased distributions of molecular shapes.
• The study provides insights into shape preferences for specific drug targets and highlights the importance of conformer generation in shape analysis.