DiffDA: a diffusion model for weather-scale data assimilation

The generation of initial conditions via accurate data assimilation is crucial for reliable weather forecasting and climate modeling. We propose the DiffDA as a machine learning based data assimilation method capable of assimilating atmospheric variables using predicted states and sparse observations. We adapt the pretrained GraphCast weather forecast model as a denoising diffusion model. Our method applies two-phase conditioning: on the predicted state during both training and inference, and on sparse observations during inference only. As a byproduct, this strategy also enables the post-processing of predictions into the future, for which no observations are available.Through experiments based on a reanalysis dataset, we have verified that our method can produce assimilated global atmospheric data consistent with observations at 0.25degree resolution. The experiments also show that the initial conditions that are generated via our approach can be used for forecast models with a loss of lead time of at most 24 hours when compared to initial conditions of state-of-the-art data assimilation suites. This enables to apply the method to real world applications such as the creation of reanalysis datasets with autoregressive data assimilation.

Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?

Molecular docking, given a ligand molecule and a ligand binding site (called ``pocket'') on a protein, predicting the binding mode of the protein-ligand complex, is a widely used technique in drug design. Many deep learning models have been developed for molecular docking, while most existing deep learning models perform docking on the whole protein, rather than on a given pocket as the traditional molecular docking approaches, which does not match common needs. What's more, they claim to perform better than traditional molecular docking, but the approach of comparison is not fair, since traditional methods are not designed for docking on the whole protein without a given pocket. In this paper, we design a series of experiments to examine the actual performance of these deep learning models and traditional methods. For a fair comparison, we decompose the docking on the whole protein into two steps, pocket searching and docking on a given pocket, and build pipelines to evaluate traditional methods and deep learning methods respectively. We find that deep learning models are actually good at pocket searching, but traditional methods are better than deep learning models at docking on given pockets. Overall, our work explicitly reveals some potential problems in current deep learning models for molecular docking and provides several suggestions for future works.