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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Self-Organizing Maps for In Silico Screening and Data Visualization.

Daniela Digles¹, Gerhard F Ecker²

¹Department of Medicinal Chemistry, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria phone/fax: +43-1-4277-55110/+43-1-4277-9551.

Molecular Informatics

|July 29, 2016

Summary

This summary is machine-generated.

Self-organizing maps, a type of unsupervised artificial neural network, are valuable in medicinal chemistry for in silico screening and data visualization. This review highlights their applications and parameter selection importance.

Keywords:

Chemoinformatics Computational chemistry Kohonen maps Self-organizing maps Virtual screening

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Area of Science:

Computational chemistry and cheminformatics.
Artificial intelligence and machine learning applications in drug discovery.

Background:

Self-organizing maps (SOMs) are unsupervised artificial neural networks widely adopted across scientific disciplines.
Their utility in medicinal chemistry is growing, particularly for handling complex datasets and predictive modeling.

Purpose of the Study:

To review the applications of SOMs in medicinal chemistry, focusing on in silico screening and data analysis.
To emphasize the critical role of parameter selection in optimizing SOM performance.
To summarize key modifications to the original SOM algorithm.

Main Methods:

Utilizing SOMs for virtual screening of chemical libraries to identify potential drug candidates.
Applying SOMs for clustering and visualizing large-scale chemical and biological datasets.
Analyzing the impact of different parameter choices on SOM outcomes.

Main Results:

SOMs enable efficient in silico screening, reducing the need for extensive experimental testing.
Clustering and visualization capabilities of SOMs facilitate the identification of patterns and relationships in complex data.
Demonstrated importance of parameter tuning for achieving meaningful results.

Conclusions:

Self-organizing maps offer powerful tools for accelerating drug discovery processes in medicinal chemistry.
Effective parameter selection and understanding algorithm modifications are crucial for successful SOM implementation.
SOMs are versatile for both predictive screening and exploratory data analysis.