621 research outputs found
Precipitation nowcasting with generative diffusion models
In recent years traditional numerical methods for accurate weather prediction
have been increasingly challenged by deep learning methods. Numerous historical
datasets used for short and medium-range weather forecasts are typically
organized into a regular spatial grid structure. This arrangement closely
resembles images: each weather variable can be visualized as a map or, when
considering the temporal axis, as a video. Several classes of generative
models, comprising Generative Adversarial Networks, Variational Autoencoders,
or the recent Denoising Diffusion Models have largely proved their
applicability to the next-frame prediction problem, and is thus natural to test
their performance on the weather prediction benchmarks. Diffusion models are
particularly appealing in this context, due to the intrinsically probabilistic
nature of weather forecasting: what we are really interested to model is the
probability distribution of weather indicators, whose expected value is the
most likely prediction.
In our study, we focus on a specific subset of the ERA-5 dataset, which
includes hourly data pertaining to Central Europe from the years 2016 to 2021.
Within this context, we examine the efficacy of diffusion models in handling
the task of precipitation nowcasting. Our work is conducted in comparison to
the performance of well-established U-Net models, as documented in the existing
literature. Our proposed approach of Generative Ensemble Diffusion (GED)
utilizes a diffusion model to generate a set of possible weather scenarios
which are then amalgamated into a probable prediction via the use of a
post-processing network. This approach, in comparison to recent deep learning
models, substantially outperformed them in terms of overall performance.Comment: 21 pages, 6 figure
Protein-Ligand Binding Affinity Directed Multi-Objective Drug Design Based on Fragment Representation Methods
Drug discovery is a challenging process with a vast molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities
EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs
Generative adversarial networks (GANs) have proven successful in image
generation tasks. However, GAN training is inherently unstable. Although many
works try to stabilize it by manually modifying GAN architecture, it requires
much expertise. Neural architecture search (NAS) has become an attractive
solution to search GANs automatically. The early NAS-GANs search only
generators to reduce search complexity but lead to a sub-optimal GAN. Some
recent works try to search both generator (G) and discriminator (D), but they
suffer from the instability of GAN training. To alleviate the instability, we
propose an efficient two-stage evolutionary algorithm-based NAS framework to
search GANs, namely EAGAN. We decouple the search of G and D into two stages,
where stage-1 searches G with a fixed D and adopts the many-to-one training
strategy, and stage-2 searches D with the optimal G found in stage-1 and adopts
the one-to-one training and weight-resetting strategies to enhance the
stability of GAN training. Both stages use the non-dominated sorting method to
produce Pareto-front architectures under multiple objectives (e.g., model size,
Inception Score (IS), and Fr\'echet Inception Distance (FID)). EAGAN is applied
to the unconditional image generation task and can efficiently finish the
search on the CIFAR-10 dataset in 1.2 GPU days. Our searched GANs achieve
competitive results (IS=8.810.10, FID=9.91) on the CIFAR-10 dataset and
surpass prior NAS-GANs on the STL-10 dataset (IS=10.440.087, FID=22.18).
Source code: https://github.com/marsggbo/EAGAN.Comment: Accepted in ECCV2022, Guohao Yin and Xin He contributed equall
Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates
Computational drug design based on artificial intelligence is an emerging
research area. At the time of writing this paper, the world suffers from an
outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus
replication is via protease inhibition. We propose an evolutionary
multi-objective algorithm (EMOA) to design potential protease inhibitors for
SARS-CoV-2's main protease. Based on the SELFIES representation the EMOA
maximizes the binding of candidate ligands to the protein using the docking
tool QuickVina 2, while at the same time taking into account further objectives
like drug-likeliness or the fulfillment of filter constraints. The experimental
part analyzes the evolutionary process and discusses the inhibitor candidates.Comment: 15 pages, 7 figures, submitted to PPSN 202
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