ArticleRSC advances2024
Augmenting a training dataset of the generative diffusion model for molecular docking with artificial binding pockets.
Article in RSC advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Elastic Net Regression to Predict CDK2 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Designing Novel Compound Candidates Against SARS-CoV-2 Using Generative Deep Neural Networks and Cheminformatics.International journal of molecular sciences · 2025Article
- Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions.Nature communications · 2025Article
- Diffusion Models in De Novo Drug Design.Journal of chemical information and modeling · 2024Review
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
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