Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models

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End-to-end protein–ligand complex structure generation with diffusion-based generative models (2022, methodology): “In particular, NeuralPLexer [34], similar work to ours, proposed to model the structure of protein-ligand complexes with a hierarchical diffusion model.”
Equivariant 3D-conditional diffusion model for molecular linker design (2022, unknown): “More details on datasets, baselines and metrics can be found in Methods.”
SE(3) diffusion model with application to protein backbone generation (2023, background): “Past works have developed diffusion models over different representations of protein structures without pretraining (Wu et al., 2022; Trippe et al., 2022; Anand & Achim, 2022; Qiao et al., 2022; Ingraham et al., 2022).”
How accurately can one predict drug binding modes using AlphaFold models? (2023, unknown): “, 2021)—for tasks related to drug design, with mixed results (Díaz-Rovira et al., 2023; He et al., 2022; Heo & Feig, 2022; C. Lee et al., 2022; S. Lee et al., 2022; Liang et al., 2022; Qiao et al., 2022; Scardino et al., 2022; Wong et al., 2022).”