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A View on Molecular Complexity from the GDB Chemical Space.

Ye Buehler¹, Jean-Louis Reymond¹

¹Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012 Bern, Switzerland.

Journal of Chemical Information and Modeling

|May 15, 2025

Summary

Molecular complexity, measured by non-divalent nodes (MC1, MC2), correlates with synthesis difficulty. These metrics apply to diverse chemical databases, aiding molecule selection for research.

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Area of Science:

Computational Chemistry
Medicinal Chemistry
Organic Synthesis

Background:

Selecting molecules for investigation involves understanding the relationship between molecular complexity, synthetic accessibility, and biological properties.
Vast chemical spaces, such as those in Generated DataBases (GDBs), contain many compounds with high molecular complexity, posing synthesis challenges.

Purpose of the Study:

To define and evaluate simple measures of molecular complexity (MC1, MC2) based on non-divalent nodes in molecular graphs.
To assess the correlation of these complexity measures with potential synthesis difficulties.
To determine the applicability of MC1 and MC2 to diverse chemical compound sets.

Main Methods:

Calculated molecular complexity using fraction (MC1) and number (MC2) of non-divalent nodes in molecular graphs.
Applied MC1 and MC2 to molecules from Generated DataBases (GDBs), ZINC (commercial screening compounds), ChEMBL (bioactive molecules), and COCONUT (natural products).
Compared MC1 and MC2 with existing measures of molecular complexity and synthetic accessibility.

Main Results:

An increasing fraction or number of non-divalent nodes in molecular graphs serves as a simple indicator of molecular complexity.
MC1 and MC2 effectively quantify molecular complexity and correlate with potential synthesis difficulties across various chemical datasets.
The proposed metrics demonstrate broad applicability to diverse chemical libraries, including screening compounds, drugs, and natural products.

Conclusions:

MC1 and MC2 provide valuable, easily computable metrics for assessing molecular complexity and predicting synthesis challenges.
These metrics can aid in the strategic selection of molecules for synthesis and biological investigation.
The study highlights the utility of graph-based properties for understanding molecular characteristics relevant to drug discovery and chemical synthesis.