Dhan Lord B Fortela, Ashley P Mikolajczyk, Miranda R Carnes, Wayne Sharp, Emmanuel Revellame, Rafael Hernandez, William E Holmes, Mark E Zappi
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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 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.
期刊介绍:
BioTechniques is a peer-reviewed, open-access journal dedicated to publishing original laboratory methods, related technical and software tools, and methods-oriented review articles that are of broad interest to professional life scientists, as well as to scientists from other disciplines (e.g., chemistry, physics, computer science, plant and agricultural science and climate science) interested in life science applications for their technologies.
Since 1983, BioTechniques has been a leading peer-reviewed journal for methods-related research. The journal considers:
Reports describing innovative new methods, platforms and software, substantive modifications to existing methods, or innovative applications of existing methods, techniques & tools to new models or scientific questions
Descriptions of technical tools that facilitate the design or performance of experiments or data analysis, such as software and simple laboratory devices
Surveys of technical approaches related to broad fields of research
Reviews discussing advancements in techniques and methods related to broad fields of research
Letters to the Editor and Expert Opinions highlighting interesting observations or cautionary tales concerning experimental design, methodology or analysis.