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Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...

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Multi-Objective Design of DNA-Stabilized Nanoclusters Using Variational Autoencoders With Automatic Feature

Elham Sadeghi¹, Peter Mastracco², Anna Gonzàlez-Rosell²

¹Department of Computer Science, University at Albany-SUNY, Albany, New York 12222, United States.

|September 17, 2024

Summary

This summary is machine-generated.

Researchers developed a generative model using variational autoencoders (VAEs) to design DNA-stabilized silver nanoclusters (Ag-DNAs) with specific fluorescence properties, including bright near-infrared emission for bioimaging.

Keywords:

DNA fluorescence interpretable machine learning near-infrared silver nanocluster variational autoencoder

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Area of Science:

Nanomaterials Science
Biotechnology
Computational Chemistry

Background:

DNA-stabilized silver nanoclusters (Ag-DNAs) exhibit tunable fluorescence properties based on their DNA sequence.
Existing machine learning models for Ag-DNA design struggle with simultaneous optimization of multiple properties and require extensive feature engineering.
Near-infrared (NIR) emission from Ag-DNAs is highly desirable for deep tissue bioimaging applications.

Purpose of the Study:

To develop a generative model for the multiobjective, continuous-property design of Ag-DNAs.
To enable simultaneous selection of multiple Ag-DNA properties, including fluorescence color and brightness.
To automate feature extraction and reduce expert input in the design process.

Main Methods:

Utilized variational autoencoders (VAEs) for a generative approach, learning both forward and inverse mappings between DNA sequences and Ag-DNA properties.
Trained the VAE model on an experimental dataset of DNA sequences and their corresponding Ag-DNA fluorescence properties.
Employed Shapley analysis to interpret the learned relationships between nucleobase patterns and fluorescence characteristics.

Main Results:

The VAE model successfully enabled the design of Ag-DNAs with desired fluorescence properties, including bright NIR emitters.
Achieved a 4-fold greater abundance of bright NIR Ag-DNAs compared to the training data.
Identified specific nucleobase patterns correlated with fluorescence color and brightness through Shapley analysis.

Conclusions:

The developed generative VAE model facilitates precise design of Ag-DNAs with tailored optical properties.
This approach significantly enhances the efficiency of designing biomolecular nanomaterials for specific applications like bioimaging.
The model's framework is adaptable for designing other biomolecular systems with sequence-dependent characteristics.