Modern drug discovery typically faces large virtual screens from huge compound databases where multiple docking tools are involved for meeting various real scenes or improving the precision of virtual screens. Among these tools, AutoDock Vina and its numerous derivatives are the most popular and have become the standard pipeline for molecular docking in modern drug discovery. Our recent Vina-GPU method realized 14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX 3090 GPU in one virtual screening case. Further speedup of AutoDock Vina and its derivatives with graphics processing units (GPUs) is beneficial to systematically push their popularization in large-scale virtual screens due to their high benefit–cost ratio and easy operation for users. Thus, we proposed the Vina-GPU 2.0 method to further accelerate AutoDock Vina and the most common derivatives with new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused by the discrepancy in their docking algorithms, our Vina-GPU 2.0 adopts different GPU acceleration strategies. In virtual screening for two hot protein kinase targets, RIPK1 and RIPK3, from the DrugBank database, our Vina-GPU 2.0 reaches an average of 65.6-fold, 1.4-fold, and 3.6-fold docking acceleration against the original AutoDock Vina, QuickVina 2, and QuickVina-W while ensuring their comparable docking accuracy. In addition, we develop a friendly and installation-free graphical user interface tool for their convenient usage. The codes and tools of Vina-GPU 2.0 are freely available at , coupled with explicit instructions and examples.

Metallic sodium (Na) has been regarded as one of the most attractive anodes for Na-based rechargeable batteries due to its high specific capacity, low working potential, and high natural abundance. However, several important issues hinder the practical application of the metallic Na anode, including its high reactivity with electrolytes, uncontrolled dendrite growth, and poor processability. Metal nitrates are common electrolyte additives used to stabilize the solid electrolyte interphase (SEI) on Na anodes, though they typically suffer from poor solubility in electrolyte solvents. To address these issues, a Na/NaNO 3 composite foil electrode was fabricated through a mechanical kneading approach, which featured uniform embedment of NaNO 3 in a metallic Na matrix. During the battery cycling, NaNO 3 was reduced by metallic Na sustainably, which addressed the issue of low solubility of an SEI stabilizer. Due to the supplemental effect of NaNO 3 , a stable SEI with NaN x O y and Na 3 N species was produced, which allowed fast ion transport. As a result, stable electrochemical performance for 600 h was achieved for Na/NaNO 3 ||Na/NaNO 3 symmetric cells at a current density of 0.5 mA cm −2 and an areal capacity of 0.5 mAh cm −2 . A Na/NaNO 3 ||Na 3 V 2 (PO 4 ) 2 O 2 F cell with active metallic Na of ∼5 mAh cm −2 at the anode showed stable cycling for 180 cycles. In contrast, a Na||Na 3 V 2 (PO 4 ) 2 O 2 F cell only displayed less than 80 cycles under the same conditions. Moreover, the processability of the Na/NaNO 3 composite foil was also significantly improved due to the introduction of NaNO 3 , in contrast to the soft and sticky pure metallic Na. Mechanical kneading of soft alkali metals and their corresponding nitrates provides a new strategy for the utilization of anode stabilizers (besides direct addition into electrolytes) to improve their electrochemical performance.

Japanese foxtail is a grass weed in eastern China. This weed is controlled by fenoxaprop-P-ethyl, one of the most common acetyl-CoA carboxylase (ACCase)-inhibiting herbicides. Some Japanese foxtail populations have developed resistance to fenoxaprop-P-ethyl, owing to target-site mutations (amino acid substitutions) located within the carboxyl transferase domain of ACCase. In the present study, three mutations were detected in three fenoxaprop-P-ethyl–resistant Japanese foxtail populations: Ile-1781-Leu in JCJT-2, Ile-2041-Asn in JZJR-1, and Asp-2078-Gly in JCWJ-3. Two copies of ACCase (Acc1-1 and Acc1-2) were identified, but mutations were detected only in Acc1-1. The derived cleaved amplified polymorphic sequence (dCAPS) method detected these mutations successfully in Japanese foxtail. The mutation frequencies in JCJT-2, JZJR-1, and JCWJ-3 were approximately 98%, 92%, and 87%, respectively. Different cross-resistance patterns to ACCase inhibitors were found in the three resistant populations. JCJT-2 (Ile-1781-Leu) and JZJR-1 (Ile-2041-Asn) showed cross-resistance to haloxyfop-R-methyl, clodinafop-propargyl, and pinoxaden, but were susceptible to clethodim. JCWJ-3 (Asp-2078-Gly) showed cross-resistance to all tested ACCase-inhibiting herbicides.

Modern drug discovery typically faces large virtual screens from huge compound databases where multiple docking tools are involved for meeting various real scenes or improving the precision of virtual screens. Among these tools, AutoDock Vina and its numerous derivatives are the most popular and have become the standard pipeline for molecular docking in modern drug discovery. Our recent Vina-GPU method realized 14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX 3090 GPU in one virtual screening case. Further speedup of AutoDock Vina and its derivatives with GPUs is beneficial to systematically push their popularization in large-scale virtual screens due to their high benefit-cost ratio and easy operation for users. Thus, we proposed the Vina-GPU 2.0 method to further accelerate AutoDock Vina and the most common derivatives with new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused by the discrepancy of their docking algorithms, our Vina-GPU 2.0 adopts different GPU acceleration strategies. In virtual screening for two hot protein kinase targets RIPK1 and RIPK3 from the DrugBank database, our Vina-GPU 2.0 reaches an average of 65.6-fold,1.4-fold and 3.6-fold docking acceleration against the original AutoDock Vina, QuickVina 2 and QuickVina-W while ensuring their comparable docking accuracy. In addition, we develop a friendly and installation-free graphical user interface (GUI) tool for their convenient usage. The codes and tools of Vina-GPU 2.0 are freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU-2.0, coupled with explicit instructions and examples.