Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets

doi:10.1039/d3ra08147h

Taras Voitsitskyi, Volodymyr Bdzhola, Roman Stratiichuk, Ihor Koleiev, Zakhar Ostrovsky, Volodymyr Vozniak, Ivan Khropachov, Pavlo Henitsoi, Leonid Popryho, Roman Zhytar, Semen Yesylevskyy, Alan Nafiiev, Serhii Starosyla

Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets

General Chemical Engineering
General Chemistry

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We introduce introduces the PocketCFDM generative diffusion model, aimed at improving the prediction of small molecule poses in the protein binding pockets.

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Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets

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