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Accurate design of high-affinity protein binding macrocycles using deep learning.
bioRxiv
November 2024
Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
The development of macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource-intensive and provide little control over binding mode. Despite considerable progress in physics-based methods for peptide design and deep-learning methods for protein design, there are currently no robust approaches for design of protein-binding macrocycles. Here, we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic peptide binders against protein targets of interest.
View Article and Find Full Text PDFAnalysis of postdisaster economy using high-resolution disaster and economy simulations.
Risk Anal
October 2024
Center for Spatial Information Science, The University of Tokyo, Tokyo, Japan.
We present an integrated framework that utilizes high-resolution seamless simulations of disasters and national economies for estimating the economic impacts of disasters. The framework consists of three components: a physics-based simulator to simulate the disaster and estimate the response of the infrastructure; a tool that estimates the losses suffered by the infrastructure based on its response; and an agent-based economic model (ABEM) that simulates the national economy considering the infrastructure damage and postdisaster decisions of the economic entities. The ABEM used in the framework has been implemented in a high-performance computing environment to simulate large economies at 1:1 scale.
View Article and Find Full Text PDFArtificial intelligence for geoscience: Progress, challenges, and perspectives.
Innovation (Camb)
September 2024
School of Computer Science, China University of Geosciences, Wuhan 430078, China.
This paper explores the evolution of geoscientific inquiry, tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence (AI) and data collection techniques. Traditional models, which are grounded in physical and numerical frameworks, provide robust explanations by explicitly reconstructing underlying physical processes. However, their limitations in comprehensively capturing Earth's complexities and uncertainties pose challenges in optimization and real-world applicability.
View Article and Find Full Text PDFInteracting internal waves explain global patterns of interior ocean mixing.
Nat Commun
August 2024
Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA.
Across the stable density stratification of the abyssal ocean, deep dense water is slowly propelled upward by sustained, though irregular, turbulent mixing. The resulting mean upwelling determines large-scale oceanic circulation properties like heat and carbon transport. In the ocean interior, this turbulent mixing is caused mainly by breaking internal waves: generated predominantly by winds and tides, these waves interact nonlinearly, transferring energy downscale, and finally become unstable, break and mix the water column.
View Article and Find Full Text PDFNeural general circulation models for weather and climate.
Nature
August 2024
Google Research, Mountain View, CA, USA.
Article Synopsis
- General circulation models (GCMs) are essential for weather and climate predictions, combining physics-based simulations with small-scale process representations like cloud formation.
- Recently, machine learning models have shown they can compete with GCMs for short-term weather forecasting but lack stability for long-term predictions.
- The newly developed NeuralGCM merges a differentiable atmospheric dynamics solver with machine learning, achieving competitive forecasts for both weather and climate while being more computationally efficient than traditional GCMs.
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