Predicting molecular docking of per- and polyfluoroalkyl substances to blood protein using generative artificial intelligence algorithm DiffDock

This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins using a generative machine-learning algorithm, DiffDock, specialized in protein–ligand blind-docking learning and prediction. Concerns about the...

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Published inBioTechniques Vol. 76; no. 1; pp. 14 - 26
Main Authors B Fortela, Dhan Lord, Mikolajczyk, Ashley P, Carnes, Miranda R, Sharp, Wayne, Revellame, Emmanuel, Hernandez, Rafael, Holmes, William E, Zappi, Mark E
Format Journal Article
LanguageEnglish
Published England Future Science Ltd 01.01.2024
Taylor & Francis Group
Subjects
Algorithms
Artificial Intelligence
Blood Proteins
Fluorocarbons
generative artificial intelligence
human health
Humans
molecular docking
Molecular Docking Simulation
per- and polyfluoroalkyl substances
target-based screening
molecular docking
per- and polyfluoroalkyl substances
blood proteins
generative artificial intelligence
human health
target-based screening
Online AccessGet full text
ISSN0736-6205
1940-9818
1940-9818
DOI10.2144/btn-2023-0070

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Abstract This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins using a generative machine-learning algorithm, DiffDock, specialized in protein–ligand blind-docking learning and prediction. Concerns about the chemical pathways and accumulation of PFAs in the environment and eventually in the human body has been rising due to empirical findings that levels of PFAs in human blood has been rising. DiffDock may offer a fast approach in determining the fate and potential molecular pathways of PFAs in human body. This study demonstrates the capability of generative AI algorithm DiffDock to accelerate protein PFA molecular docking computations that can lead to efficient studies of PFA fate in the human body.
AbstractList This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins using a generative machine-learning algorithm, DiffDock, specialized in protein-ligand blind-docking learning and prediction. Concerns about the chemical pathways and accumulation of PFAs in the environment and eventually in the human body has been rising due to empirical findings that levels of PFAs in human blood has been rising. DiffDock may offer a fast approach in determining the fate and potential molecular pathways of PFAs in human body.This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins using a generative machine-learning algorithm, DiffDock, specialized in protein-ligand blind-docking learning and prediction. Concerns about the chemical pathways and accumulation of PFAs in the environment and eventually in the human body has been rising due to empirical findings that levels of PFAs in human blood has been rising. DiffDock may offer a fast approach in determining the fate and potential molecular pathways of PFAs in human body.
This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins using a generative machine-learning algorithm, DiffDock, specialized in protein–ligand blind-docking learning and prediction. Concerns about the chemical pathways and accumulation of PFAs in the environment and eventually in the human body has been rising due to empirical findings that levels of PFAs in human blood has been rising. DiffDock may offer a fast approach in determining the fate and potential molecular pathways of PFAs in human body. This study demonstrates the capability of generative AI algorithm DiffDock to accelerate protein PFA molecular docking computations that can lead to efficient studies of PFA fate in the human body.
This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins using a generative machine-learning algorithm, DiffDock, specialized in protein-ligand blind-docking learning and prediction. Concerns about the chemical pathways and accumulation of PFAs in the environment and eventually in the human body has been rising due to empirical findings that levels of PFAs in human blood has been rising. DiffDock may offer a fast approach in determining the fate and potential molecular pathways of PFAs in human body.
Author Holmes, William E
Hernandez, Rafael
Carnes, Miranda R
Revellame, Emmanuel
Zappi, Mark E
B Fortela, Dhan Lord
Mikolajczyk, Ashley P
Sharp, Wayne
AuthorAffiliation 1Department of Chemical Engineering, University of Louisiana, Lafayette, LA 70504, USA
2Energy Institute of Louisiana, University of Louisiana, Lafayette, LA 70504, USA
3Department of Civil Engineering, University of Louisiana, Lafayette, LA 70504, USA
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Keywords molecular docking
per- and polyfluoroalkyl substances
blood proteins
generative artificial intelligence
human health
target-based screening
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Snippet This study computationally evaluates the molecular docking affinity of various perfluoroalkyl and polyfluoroalkyl substances (PFAs) towards blood proteins...
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SubjectTerms Algorithms
Artificial Intelligence
Blood Proteins
Fluorocarbons
generative artificial intelligence
human health
Humans
molecular docking
Molecular Docking Simulation
per- and polyfluoroalkyl substances
target-based screening
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Title Predicting molecular docking of per- and polyfluoroalkyl substances to blood protein using generative artificial intelligence algorithm DiffDock
URI http://dx.doi.org/10.2144/btn-2023-0070
https://www.ncbi.nlm.nih.gov/pubmed/37947020
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https://doi.org/10.2144/btn-2023-0070
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