Chemical Families and Molecular Trees: A Structural Approach to Understanding Chemical Relationships

In the complex world of chemistry, understanding and organizing the vast array of chemical compounds based on their structures and properties remains a significant challenge. This work introduces a novel approach to classifying chemicals through the concept of chemical families and molecular trees. Chemical families group compounds based on shared structural features, composition, and properties, while molecular trees visually represent the evolutionary or reaction-based relationships between these chemicals. By illustrating these concepts with examples such as methanol and formaldehyde, we demonstrate how molecular trees can reveal connections between compounds formed through chemical reactions, such as oxidation. This framework not only aids in visualizing chemical similarities and transformations but also facilitates the discovery of new compounds, understanding reaction pathways, and predicting chemical behavior. The approach bridges the gap between molecular structure and chemical function, offering a clear, intuitive way to organize chemical knowledge. Potential applications include chemical education, research in synthetic pathways, and the design of novel materials or pharmaceuticals through a structured understanding of chemical families and their evolutionary relationships.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-07
DOI
https://doi.org/10.5281/zenodo.16759506
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Chemical Families and Molecular Trees: A Structural Approach to Understanding Chemical Relationships

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Chemical Families and Molecular Trees: A Structural Approach to Understanding Chemical Relationships

article en

Abstract

In the complex world of chemistry, understanding and organizing the vast array of chemical compounds based on their structures and properties remains a significant challenge. This work introduces a novel approach to classifying chemicals through the concept of chemical families and molecular trees. Chemical families group compounds based on shared structural features, composition, and properties, while molecular trees visually represent the evolutionary or reaction-based relationships between these chemicals. By illustrating these concepts with examples such as methanol and formaldehyde, we demonstrate how molecular trees can reveal connections between compounds formed through chemical reactions, such as oxidation. This framework not only aids in visualizing chemical similarities and transformations but also facilitates the discovery of new compounds, understanding reaction pathways, and predicting chemical behavior. The approach bridges the gap between molecular structure and chemical function, offering a clear, intuitive way to organize chemical knowledge. Potential applications include chemical education, research in synthetic pathways, and the design of novel materials or pharmaceuticals through a structured understanding of chemical families and their evolutionary relationships.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 64%
Machine Learning in Materials Science
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