Voitsitskyi T, Bdzhola V, Stratiichuk R, Koleiev I, Ostrovsky Z, Vozniak V, Khropachov I, Henitsoi P, Popryho L, Zhytar R, Yesylevskyy S, Nafiiev A, Starosyla S. Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets.
RSC Adv 2024;
14:1341-1353. [PMID:
38174256 PMCID:
PMC10763617 DOI:
10.1039/d3ra08147h]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
This study introduces the PocketCFDM generative diffusion model, aimed at improving the prediction of small molecule poses in the protein binding pockets. The model utilizes a novel data augmentation technique, involving the creation of numerous artificial binding pockets that mimic the statistical patterns of non-bond interactions found in actual protein-ligand complexes. An algorithmic method was developed to assess and replicate these interaction patterns in the artificial binding pockets built around small molecule conformers. It is shown that the integration of artificial binding pockets into the training process significantly enhanced the model's performance. Notably, PocketCFDM surpassed DiffDock in terms of non-bond interaction and steric clash numbers, and the inference speed. Future developments and optimizations of the model are discussed. The inference code and final model weights of PocketCFDM are accessible publicly via the GitHub repository: https://github.com/vtarasv/pocket-cfdm.git.
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