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While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they’re computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.

 

A memristor array has emerged as a potential computing hardware for artificial intelligence (AI). It has an inherent memory effect that allows information storage in the form of easily programmable electrical conductance, making it suitable for efficient data processing without shuttling of data between the processor and memory. To realize its full potential for AI applications, fine-tuning of internal device dynamics is required to implement a network system that employs dynamic functions. Here, we provide a perspective on multicationic entropy-stabilized oxides as a widely tunable materials system for memristor applications. We highlight the potential for efficient data processing in machine learning tasks enabled by the implementation of “task specific” neural networks that derive from this material tunability.

 

Electronic switches based on the migration of high-density point defects, or memristors, are poised to revolutionize post-digital electronics. Despite significant research, key mechanisms for filament formation and oxygen transport remain unresolved, hindering our ability to predict and design device properties. For example, experiments have achieved 10 orders of magnitude longer retention times than predicted by current models. Here, using electrical measurements, scanning probe microscopy, and first-principles calculations on tantalum oxide memristors, we reveal that the formation and stability of conductive filaments crucially depend on the thermodynamic stability of the amorphous oxygen-rich and oxygen-poor compounds, which undergo composition phase separation. Including the previously neglected effects of this amorphous phase separation reconciles unexplained discrepancies in retention and enables predictive design of key performance indicators such as retention stability. This result emphasizes non-ideal thermodynamic interactions as key design criteria in post-digital devices with defect densities substantially exceeding those of today’s covalent semiconductors. 

This study proposes a surrogate-based cyber-physical aerodynamic shape optimization (SB-CP-ASO) approach for high-rise buildings under wind loading. Three components are developed in the SB-CP-ASO procedure: (1) an adaptive subtractive manufacturing technique, (2) a high-throughput wind tunnel testing procedure, and (3) a flexible infilling strategy. The downtime of the procedure is minimized through a parallel manufacturing and testing (llM&T) technique. An unexplored double-section setback strategy with various cross-sections and transitions positions is used to demonstrate the performance of the proposed procedure. A total of 173 physical specimens were evaluated to reach the optimization convergence within the reserved testing window. Further analysis of promising shapes considering multiple design wind speeds is suggested to achieve target performance objectives at various hazard levels. Practical information on setback and cross-section modification strategies is discussed based on the optimization results. In comparison with a square benchmark model, the roof drifts for promising candidates with similar building volumes are reduced by more than 70% at wind speeds higher than 50 m/s. This procedure is expected to provide an efficient platform between owners, architects, and structural engineers to identify ideal candidates within a defined design space for real-world applications of high-rise buildings.