Official implementation of NeuralPLexer, a deep generative model to jointly predict protein-ligand complex 3D structures and beyond.
NeuralPLexer enables accurate prediction of protein–ligand complex structure and conformational changes a, Method overview.
Feb 12, 2024 · NeuralPLexer adopts a deep generative model to sample the three-dimensional structures of the binding complex and their conformational changes ...
Missing: evaluation | Show results with:evaluation
May 23, 2024 · Preprocessed datasets and benchmark method predictions are available on Zenodo [Morehead et al., 2024] under a CC-BY 4.0 license, of which the ...
Sep 8, 2021 · We introduced a new deep learning model, PUResNet, to predict the ligand-binding sites on protein structures trained on a newly formed dataset, ...
The worry is that a model can yield good predictions for the wrong reasons (e.g. artefacts hidden in the data) and therefore will not generalize well for new ...
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Nov 9, 2023 · Here, we have deconstructed this by training neural networks only on proteins or ligands and carrying out the prediction to evaluate the bias in ...
These partitions are used as training and test set splits to explore NN model uncertainty. We demonstrate how the uncertainties estimated by the selected ...
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Feb 23, 2024 · Qiao et al. report a diffusion model-based generative AI approach known as NeuralPLexer that enables the prediction of protein–ligand ...
Feb 16, 2022 · This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning ...