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Bioactive Molecules: Perfectly Shaped for Their Target?

Matthias Wirth¹, Wolfgang H B Sauer²

¹Merck Serono S.A. 9, Chemin des Mines, 1202 Genève, Switzerland, Merck Serono is a division of Merck KGaA, Darmstad, Germany phone: +41 (0)22 414 9454. matthias.wirth@merckserono.net.

Molecular Informatics

|July 29, 2016

Summary

This summary is machine-generated.

This study reveals distinct molecular shape profiles for compounds targeting specific biological molecules. Normalized Principal Moments of Inertia Ratios (NPRs) effectively differentiate these shapes, showing a bias towards rod-like molecules in drug datasets.

Keywords:

Clustering assessment Drug likeness Molecular shape Multiconformational analysis NPR descriptor

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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.