Molecular docking via quantum approximate optimization algorithm

Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions and advance novel therapeutics. Here, we introduce a potential alternative solution to this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a quantum computer. Our method was applied to analyze diverse biological systems, including the SARS-CoV-2 Mpro complex with PM-2-020B, the DPP-4 complex with piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with JP-III-048. The DC-QAOA exhibits superior performance, providing more accurate and biologically relevant docking results, especially for larger molecular docking problems. Moreover, QAOA-based algorithms demonstrate enhanced hardware compatibility in the noisy intermediate-scale quantum era, indicating their potential for efficient implementation under practical docking scenarios. Our findings underscore quantum computing's potential in drug discovery and offer valuable insights for optimizing protein-ligand docking processes.Comment: 10 pages, 5 figures, All comments are welcom

FIELD MEASUREMENT AND NUMERICAL STUDY OF EXTERNAL WIND PRESSURE OF RIBBED COOLING TOWER

The hyperbolic thin-shell cooling tower is a typical wind-sensitive structure. The full-size measurement is the most direct and important way to study the distribution of wind pressure on the surface of the cooling tower. Due to the limitations of engineering conditions and meteorological conditions, the field measured data are relatively lacking, and the field test data of ribbed cooling towers are less. In order to analyze the wind pressure distribution on the surface of the cooling tower, we chose a ribbed cooling tower in Toksun County, Xinjiang, China, where there are strong winds all year round, and field measurements were carried out to understand the wind load characteristics of the tower under the perennial dominant wind direction and the maximum wind direction. It was found that the absolute value of the negative pressure on the leeward side was larger than that in the code and the fluctuating wind pressure coefficient fluctuates greatly when the field measured wind speed was greater than 10m/s (15 meters above the ground). For circular section cooling tower, the Reynolds number (Re) has great influence on wind pressure. With the increase of Re, the absolute value of the average negative pressure of the tail wind pressure coefficient increases, which should be paid attention to in design. The regression curves of the average wind pressure coefficients measured on site under several typical working conditions are given by using the least square method, and its form is consistent with the standard (but the coefficients are different). In addition, Fluent software was used to calculate the external wind pressure of the cooling tower, and the field measured results were compared with the Chinese code, German code and numerical calculation, and the results were consistent

The Artificial Neural Networks Applied to Servo Control Systems

This chapter utilizes the direct neural control (DNC) based on back propagation neural networks (BPN) with specialized learning architecture applied to the speed control of DC servo motor. The proposed neural controller can be treated as a speed regulator to keep the motor in constant speed, and be applied to DC servo motor speed control. The proposed neural control applied to position control for hydraulic servo system is also studied for some modern robotic applications. A tangent hyperbolic function is used as the activation function, and the back propagation error is approximated by a linear combination of error and error!s differential. The simulation and experiment results reveal that the proposed neural controller is available to DC servo control system and hydraulic servo system with high convergent speed, and enhances the adaptability of the control system

Unextendible Maximally Entangled Bases in Cpd⊗Cqd\mathbb{C}^{pd}\otimes \mathbb{C}^{qd}

The construction of unextendible maximally entangled bases is tightly related to quantum information processing like local state discrimination. We put forward two constructions of UMEBs in $\mathbb {C}^{pd}\otimes \mathbb {C}^{qd}$($p\leq q$) based on the constructions of UMEBs in $\mathbb {C}^{d}\otimes \mathbb {C}^{d}$ and in $\mathbb {C}^{p}\otimes \mathbb {C}^{q}$, which generalizes the results in [Phys. Rev. A. 94, 052302 (2016)] by two approaches. Two different 48-member UMEBs in $\mathbb {C}^{6}\otimes \mathbb {C}^{9}$ have been constructed in detail

Central engine afterglow of Gamma-ray Bursts

Before 2004, nearly all GRB afterglow data could be understood in the context of the external shocks model. This situation has changed in the past two years, when it became clear that some afterglow components should be attributed to the activity of the central engine; i.e., the {\it central engine afterglow}. We review here the afterglow emission that is directly related to the GRB central engine. Such an interpretation proposed by Katz, Piran & Sari, peculiar in pre-{\it Swift} era, has become generally accepted now.Comment: 4 pages including 1 figure. Presented at the conference "Astrophysics of Compact Objects" (July 1-7, 2007; Huangshan, China