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BuildAMol: a versatile Python toolkit for fragment-based molecular design.

Noah Kleinschmidt¹, Thomas Lemmin²

¹Institute of Biochemistry and Molecular Medicine, University of Bern, Buehlstrasse 28, 3012, Bern, Switzerland.

Journal of Cheminformatics

|August 25, 2024

Summary

BuildAMol is a flexible Python toolkit for molecular modeling, enabling expert-driven construction of diverse molecular structures. It supports various applications, from metal complexes to drug discovery pipelines, advancing computational biology.

Keywords:

Fragment-based molecular assembly Molecular modeling Python Supramolecular modeling

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Area of Science:

Computational biology
Cheminformatics
Molecular modeling

Background:

Existing molecular modeling systems are often specialized for specific molecule classes (e.g., proteins, ligands) and lack flexibility for expert-driven assembly.
Current manual or semi-automated tools have limitations in the scope of structures they can generate.

Purpose of the Study:

Introduce BuildAMol, a general-purpose, fragment-based molecular assembly toolkit designed for flexibility and extensibility.
Empower researchers with a user-friendly API for detailed manual or semi-automated construction of diverse molecular models.

Main Methods:

Developed BuildAMol as a Python-based toolkit with a well-documented, user-friendly API.
Demonstrated versatility through various use cases, including metal complex generation, dendrimer modeling, and integration into drug discovery pipelines.

Main Results:

BuildAMol provides a robust framework for expert-driven molecular model building.
The toolkit integrates modeling, modification, optimization, and visualization functions within a unified API.
Facilitates collaboration with other cheminformatics libraries and has a shallow learning curve.

Conclusions:

BuildAMol offers a versatile and accessible solution for molecular modeling, catering to a broad range of applications.
It serves as a valuable tool for advancing molecular research and innovation, including the integration of deep learning techniques.