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AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.

A new heterobimetallic nitrilotriacetatoperoxotitanate complex of titanium and lead [Pb(H2O)3]2[Ti2(O2)2O(nta)2]·4H2O (C6H6O6N=H3nta) was isolated in pure crystals directly from the solution containing tetrabutyl orthotitanate, hydrogen peroxoide, lead acetate, and nitrilotriacetic acid at pH = 2.0–4.0. The isolated complex was characterized by elemental analyses, IR spectrum, thermal analysis (TG), and single-crystal X-ray diffraction. The single-crystal X-ray structural analysis revealed that the titanium atom is N,O,O′,O′′-chelated by the nitrilotriacetate and O,O′-chelated by the peroxo group and was coordinated to the bridging O atom in an overall pentagonal-bipyramidal geometry. The thermal decomposition of this precursor led to the formation of phase-pure lead titanate (PbTiO3) at ≥450 °C. The morphology, microstructure, and crystalline of the resulting PbTiO3 product have been characterized by BET, transmission electron microscopy, and powder X-ray diffraction. The TEM micrographs revealed that the size of the as-synthesized crystallines to be 50–100 nm range. The BET measurement revealed that the PbTiO3 powders had a surface area of 5.6 m2/g.

Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure but rather determined from the equilibrium distribution of structures. Conventional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. Here we introduce a deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG uses deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system such as a chemical graph or a protein sequence. This framework enables the efficient generation of diverse conformations and provides estimations of state densities, orders of magnitude faster than conventional methods. We demonstrate applications of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst–adsorbate sampling and property-guided structure generation. DiG presents a substantial advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in the molecular sciences. Methods for predicting molecular structure predictions have so far focused on only the most probable conformation, but molecular structures are dynamic and can change when performing their biological functions, for example. Zheng et al. use a graph transformer approach to learn the equilibrium distribution of molecular systems and show that this can be helpful for a number of downstream tasks, including protein structure prediction, ligand docking and molecular design.

AutoDock Vina and its derivatives have established themselves as a prevailing pipeline for virtual screening in contemporary drug discovery. Our Vina-GPU method leverages the parallel computing power of GPUs to accelerate AutoDock Vina, and Vina-GPU 2.0 further enhances the speed of AutoDock Vina and its derivatives. Given the prevalence of large virtual screens in modern drug discovery, the improvement of speed and accuracy in virtual screening has become a longstanding challenge. In this study, we propose Vina-GPU 2.1, aimed at enhancing the docking speed and precision of AutoDock Vina and its derivatives through the integration of novel algorithms to facilitate improved docking and virtual screening outcomes. Building upon the foundations laid by Vina-GPU 2.0, we introduce a novel algorithm, namely Reduced Iteration and Low Complexity BFGS (RILC-BFGS), designed to expedite the most time-consuming operation. Additionally, we implement grid cache optimization to further enhance the docking speed. Furthermore, we employ optimal strategies to individually optimize the structures of ligands, receptors, and binding pockets, thereby enhancing the docking precision. To assess the performance of Vina-GPU 2.1, we conduct extensive virtual screening experiments on three prominent targets, utilizing two fundamental compound libraries and seven docking tools. Our results demonstrate that Vina-GPU 2.1 achieves an average 4.97-fold acceleration in docking speed and an average 342% improvement in EF1% compared to Vina-GPU 2.0. The source code and tools for Vina-GPU 2.1 are freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU-2.1, accompanied by comprehensive instructions and illustrative examples.Competing Interest StatementThe authors have declared no competing interest.

Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science.

Trans-δ-viniferin (TVN), as a natural extract, is a resveratrol dimer with attractive biological activities, particularly its anti-tumor character. However, the mechanism of TVN interfering with cancerous proliferation has not been fully understood. Herein in this study, we found that TVN could trigger cancerous mitochondrial membrane potential (ΔΨm) reduction, with intracellular reactive oxidative species (ROS) level increasing, leading to apoptosis, which makes TVN a promising candidate for lung cancer cells A549 treatment. Therefore, this study provides TVN as an option to meet the demand for higher antitumor availability with lower biotoxicity and other clinical applications.

T-joint, an important type of welding joint, is widely used in automobiles, ships, aerospace, and other industries. The variation in the temperature field and the stress field for T-joint is more complicated than that in other joint methods, and it is difficult to control the welding quality. The temperature field and stress field in 316L stainless steel T-joint were analyzed in this research by using simulation software, considering the thermal–metallurgical–mechanical coupling behavior, the isotropic hardening model and moving heat source condition. In the end, the simulation results were verified by the test results, and it was shown that the numerical simulation results agreed with the test results, which proved the reliability of the simulation results in 316L T-joint. The simulation results showed that the temperature distribution of T-joint cross section changed from a fan shape to an elliptical shape and the temperature distribution range increased with time. The temperature field was distributed symmetrically along the center of the weld. There was a large temperature difference between the weld zone and the base metal. The peak temperature was obtained in the second pass, and the peak temperature in the web plate was significantly higher than that in the flange plate; with farther away from the weld, a little variation happened in the thermal cycle curve. Whether it was longitudinal residual stress or transverse residual stress presented a hat-like distribution trend along the weld direction. The maximum tensile longitudinal residual stress appeared in the middle, while the maximum transverse residual stress appeared near the middle. In the vertical weld, the larger tensile stress in longitudinal and transverse direction appeared near the weld zone.

The majority of advanced breast cancers exhibit strong aggressiveness, heterogeneity, and drug resistance, and currently, the lack of effective treatment strategies is one of the main challenges that cancer research must face. Therefore, developing a feasible preclinical model to explore tailored treatments for refractory breast cancer is urgently needed. We established organoid biobanks from 17 patients with breast cancer and characterized them by immunohistochemistry (IHC) and next generation sequencing (NGS). In addition, we in the first combination of patient‐derived organoids (PDOs) with mini‐patient‐derived xenografts (Mini‐PDXs) for the rapid and precise screening of drug sensitivity. We confirmed that breast cancer organoids are a high‐fidelity three‐dimension (3D) model in vitro that recapitulates the original tumour's histological and genetic features. In addition, for a heavily pretreated patient with advanced drug‐resistant breast cancer, we combined PDO and Mini‐PDX models to identify potentially effective combinations of therapeutic agents for this patient who were alpelisib + fulvestrant. In the drug sensitivity experiment of organoids, we observed changes in the PI3K/AKT/mTOR signalling axis and oestrogen receptor (ER) protein expression levels, which further verified the reliability of the screening results. Our study demonstrates that the PDO combined with mini‐PDX model offers a rapid and precise drug screening platform that holds promise for personalized medicine, improving patient outcomes and addressing the urgent need for effective therapies in advanced breast cancer.