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Title A database framework for rapid screening of structure-function relationships in PFAS chemistry
ID_Doc 65
Authors Su, A; Rajan, K
Published Scientific Data, 8, 1
Structure
Abstract

The article describes a database framework, PFAS-Map, that enables rapid screening of structure-function relationships in PFAS (Per- and Polyfluoroalkyl Substances) chemistry. The framework maps high-dimensional information associated with the SMILES approach of encoding molecular structure with functionality data, including bioactivity and physicochemical properties. PFAS-Map is a 3D unsupervised visualization tool that can automatically classify new PFAS chemistries based on current PFAS classification criteria.

Introduction

The article introduces PFAS, a class of chemicals with outstanding qualities in chemical and thermal stability, water repellency, and oil repellency, which have been used in various industrial and commercial products. However, the presence of PFASs in freshwater systems, wildlife, and even human blood has raised serious public concerns about unknown dangers due to PFAS's high persistence, bioaccumulation potential, toxicity, and ease of being transmitted or transported through the environment. The article highlights the need for a database framework that can rapidly explore systematics in structure-function relationships associated with new and emerging PFAS chemistries.

Methods

The article describes the methods used to develop the PFAS-Map framework, which includes SMILES standardization, descriptors calculation, PFAS structure classification, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE) visualization. The framework uses PaDEL-descriptors software to calculate molecular descriptors and fingerprints of the chemical structures, and RDKit to standardize SMILES from different sources.

Results

The article presents the results of using the PFAS-Map framework to classify PFAS substances, including the prediction and estimation of yet unmeasured fundamental physical properties of PFAS chemistries, uncovering hierarchical characteristics in existing classification schemes, and the fusion of data from diverse sources. The framework is also used to screen the relationship between PFAS structure and toxicity from two sets of experimental data.

Discussion

The article discusses the utility of the PFAS-Map framework, including its ability to detect and visualize sub-classifications of PFAS chemistry, screen the relationship between PFAS structure and toxicity, and predict the structure-function relationships of new PFAS chemistries. The framework is also shown to be versatile, allowing for the visualization of classification patterns and trends in structures-function relationships in PFAS chemistry.

Code and Data Availability

The article provides information on the code and data availability, including the availability of the PFAS-Map framework, datasets, and data pre-processing code. The framework is available on figshare, and the datasets and data pre-processing code are also available on figshare.

Conclusion

The article concludes that the PFAS-Map framework is a useful tool for rapidly screening structure-function relationships in PFAS chemistry, and that it has the potential to be widely used in the field of PFAS research. The framework is also shown to be versatile, allowing for the visualization of classification patterns and trends in structures-function relationships in PFAS chemistry.

Section 1: Introduction
(2 sentences)
The article introduces PFAS, a class of chemicals with outstanding qualities in chemical and thermal stability, water repellency, and oil repellency, which have been used in various industrial and commercial products. However, the presence of PFASs in freshwater systems, wildlife, and even human blood has raised serious public concerns about unknown dangers due to PFAS's high persistence, bioaccumulation potential, toxicity, and ease of being transmitted or transported through the environment.

Section 2: Methods
(2 sentences)
The article describes the methods used to develop the PFAS-Map framework, which includes SMILES standardization, descriptors calculation, PFAS structure classification, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE) visualization. The framework uses PaDEL-descriptors software to calculate molecular descriptors and fingerprints of the chemical structures, and RDKit to standardize SMILES from different sources.

Section 3: Results
(2 sentences)
The article presents the results of using the PFAS-Map framework to classify PFAS substances, including the prediction and estimation of yet unmeasured fundamental physical properties of PFAS chemistries, uncovering hierarchical characteristics in existing classification schemes, and the fusion of data from diverse sources. The framework is also used to screen the relationship between PFAS structure and toxicity from two sets of experimental data.

Section 4: Discussion
(2 sentences)
The article discusses the utility of the PFAS-Map framework, including its ability to detect and visualize sub-classifications of PFAS chemistry, screen the relationship between PFAS structure and toxicity, and predict the structure-function relationships of new PFAS chemistries. The framework is also shown to be versatile, allowing for the visualization of classification patterns and trends in structures-function relationships in PFAS chemistry.

Section 5: Code and Data Availability
(2 sentences)
The article provides information on the code and data availability, including the availability of the PFAS-Map framework, datasets, and data pre-processing code. The framework is available on figshare, and the datasets and data pre-processing code are also available on figshare.

Section 6: Conclusion
(2 sentences)
The article concludes that the PFAS-Map framework is a useful tool for rapidly screening structure-function relationships in PFAS chemistry, and that it has the potential to be widely used in the field of PFAS research. The framework is also shown to be versatile, allowing for the visualization of classification patterns and trends in structures-function relationships in PFAS chemistry.
Summary The authors have developed a database framework, called PFAS-Map, to rapidly explore structure-function relationships in Per- and Polyfluoroalkyl Substances (PFAS) chemistry. PFASs are a class of compounds that have been widely used in various industrial and commercial products due to their unique properties, but have also raised concerns due to their persistence, bioaccumulation, and toxicity. The PFAS-Map uses a combination of machine learning and unsupervised learning techniques to classify PFAS compounds into different classes and subclasses based on their molecular structure and functionality. The framework uses Simplified Molecular Input Line Entry System (SMILES) format to represent the molecular structure of PFAS compounds and calculates molecular descriptors and fingerprints to capture their properties. The PFAS-Map also includes a 3D visualization tool that can automatically classify new PFAS chemistries based on current PFAS classification criteria. The framework has been trained using data from the US Environmental Protection Agency's (EPA) PFAS master list and has been validated using experimental data. The authors have demonstrated the utility of PFAS-Map in detecting and visualizing sub-classifications of PFAS chemistry, screening the relationship between PFAS structure and toxicity, and predicting the structure-function relationships of new PFAS compounds. The PFAS-Map is an open-source framework that can be used to explore the vast and growing dataset of PFAS compounds and to identify new hazards associated with these compounds. The framework has the potential to accelerate the development of new PFAS-free products and to inform regulatory decisions. The authors hope that PFAS-Map will become a widely-used tool in the scientific community to address the growing concerns about PFASs.
Scientific Methods The research methods used in this article are:

1.
Data collection
: The authors used the US EPA PFAS Master List, which consists of all registered PFASs listed from within and outside the United States Environmental Protection Agency (US EPA). They also used data from other sources, such as PubChem, RDKit, and the CompTox Chemistry Dashboard.
2.
Data preprocessing
: The authors standardized the SMILES strings of the PFAS compounds using RDKit, and calculated 1D and 2D molecular descriptors using PaDEL-descriptors software. They also removed chemical structures with invalid or non-canonical SMILES, and duplicate chemical structures.
3.
Principal Component Analysis (PCA)
: The authors used PCA to reduce the dimensionality of the descriptors data and retain most of the information. They trained a PCA model with the descriptors data of EPA PFASs using Scikit-learn.
4.
t-Distributed Stochastic Neighbor Embedding (t-SNE)
: The authors used t-SNE to visualize the high-dimensional data in a lower-dimensional space. They optimized the step and perplexity hyperparameters using Scikit-learn.
5.
Unsupervised machine learning
: The authors used unsupervised machine learning techniques to classify the PFAS compounds into different categories, such as perfluoroalkyl acids (PFAAs), perfluoroalkane sulfonamidoethanols (FASAs), and fluorotelomer-based PFAAs.
6.
Visualization
: The authors used Plotly to visualize the 3D interactive graph of the PFAS-Map, which displays the classification results, t-SNE/PCA transformation results, and user-input PFAS activity/property data.
7.
Database framework
: The authors developed a database framework that enables rapid screening of structure-function relationships associated with new and emerging PFAS chemistries. The framework uses a combination of PCA, t-SNE, and unsupervised machine learning techniques to classify PFAS compounds and estimate their physicochemical properties.

Overall, the authors used a range of research methods to develop a comprehensive database framework for rapid screening of structure-function relationships in PFAS chemistry.
Article contribution The article "A Database Framework for Rapid Screening of Structure-Function Relationships in PFAS Chemistry" by An Su and Krishna Rajan presents a novel database framework, called PFAS-Map, for rapid screening of structure-function relationships in PFAS chemistry. The framework is designed to facilitate the classification, visualization, and analysis of PFAS compounds, which are critical for understanding their potential environmental and health impacts.

Contribution to Regenerative Economics:

1.
Environmental Impact Analysis
: PFAS-Map provides a platform for analyzing the environmental impact of PFAS compounds, which is essential for developing sustainable and regenerative practices. By understanding the structure-function relationships of PFAS compounds, researchers can identify potential pathways for remediation and mitigation.
2.
Sustainable Materials Development
: The framework can be used to identify PFAS-free alternatives for various industrial applications, such as non-stick coatings, fire-resistant materials, and water-repellent textiles. This can lead to the development of sustainable materials that reduce the environmental impact of PFAS.
3.
Circular Economy Approaches
: PFAS-Map can be used to analyze the chemical structure-function relationships of PFAS compounds and identify opportunities for recycling, reuse, and upcycling. This can contribute to a circular economy approach, where materials are kept in use for as long as possible, reducing waste and the demand for primary materials.
4.
Regulatory Framework Development
: The framework can be used to inform regulatory decisions related to PFAS compounds, such as the development of new classification criteria, toxicity testing protocols, and exposure limits. This can help to ensure that PFAS compounds are regulated in a way that balances human health and environmental protection.
5.
Open-Source Data Sharing
: The PFAS-Map framework is based on open-source data, which can facilitate data sharing and collaboration among researchers and policymakers. This can help to accelerate the development of regenerative economics approaches that prioritize environmental sustainability and human well-being.

In summary, the PFAS-Map framework has the potential to contribute to regenerative economics by providing a platform for analyzing the environmental impact of PFAS compounds, developing sustainable materials alternatives, promoting circular economy approaches, informing regulatory decisions, and facilitating open-source data sharing.
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