A5087976552- Computational Drug Discovery Methods
- Machine Learning in Materials Science
- Protein Structure and Dynamics
- Protein Degradation and Inhibitors
- Metabolomics and Mass Spectrometry Studies
- Horticultural and Viticultural Research
- Matrix Theory and Algorithms
- Quantum-Dot Cellular Automata
- Evolutionary Algorithms and Applications
- Advanced Biosensing Techniques and Applications
- Advanced Wireless Communication Techniques
- Iterative Methods for Nonlinear Equations
- Advanced Optimization Algorithms Research
- Metaheuristic Optimization Algorithms Research
- Viral Infectious Diseases and Gene Expression in Insects
- Water Quality Monitoring and Analysis
- Cooperative Communication and Network Coding
- Error Correcting Code Techniques
- Quantum Computing Algorithms and Architecture
- Neural Networks and Applications
- Innovative Microfluidic and Catalytic Techniques Innovation
Southeast University
2023-2025
Nanjing University of Posts and Telecommunications
2021-2024
Microsoft Research Asia (China)
2024
Nanjing Health and Health Commission
2023
Central China Normal University
2019
AutoDock Vina is one of the most popular molecular docking tools. In latest benchmark CASF-2016 for comparative assessment scoring functions, won best power among all Modern drug discovery facing a common scenario large virtual screening hits from huge compound databases. Due to seriality characteristic algorithm, there no successful report on its parallel acceleration with GPUs. Current typically relies stack computing as well allocation resource and tasks, such VirtualFlow platform. The...
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 screens. Among these tools, AutoDock Vina and its numerous derivatives most popular have become standard pipeline molecular in modern discovery. Our recent Vina-GPU method realized 14-fold acceleration against on a piece NVIDIA RTX 3090 GPU one screening case. Further speedup with graphics processing...
Abstract Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications not functions a single molecular but rather determined from the equilibrium distribution structures. Conventional methods obtaining these distributions, such as dynamics simulation, computationally expensive and often intractable. Here we introduce framework, called Distributional Graphormer (DiG), an attempt to...
Simulated Annealing (SA) algorithm is not effective with large optimization problems for its slow convergence. Hence, several parallel (pSA) methods have been proposed, where the increase of searching threads can boost speed Although satisfactory solutions be obtained by these methods, there no rigorous mathematical analyses on their effectiveness. Thus, this article introduces a probabilistic model, which theorem about effectiveness multiple initial states SA (MISPSA) has proven. The also...
Simplifying the Euclidean projection onto check polytope is an efficient way to reduce computational complexity of alternating direction method multipliers (ADMM) decoding algorithm for low-density parity-check (LDPC) codes. Existing algorithms require sorting operation or iterative operation, which happens be most complex part projection. In this letter, a novel and fast proposed without operations. algorithm, line segment replaces approach approximate at low complexity. Simulation results...
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 Vina, 2.0 further enhances speed derivatives. Given prevalence large screens modern discovery, improvement accuracy has become longstanding challenge. In this study, we propose 2.1, aimed at enhancing docking precision through integration novel algorithms facilitate...
Simulated Annealing (SA) algorithm is not effective with large optimization problems for its slow convergence. Hence, several parallel (pSA) methods have been proposed, where the increase of searching threads can boost speed Although these obtain satisfactory solutions to problems, there no rigorous mathematical analyses on their effectiveness. Thus, this paper introduces a probabilistic model, which theorem about effectiveness multiple initial states SA (MISPSA) has proven. The also...
Abstract 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 Vina, 2.0 further enhances speed derivatives. Given prevalence large screens modern discovery, improvement accuracy has become longstanding challenge. In this study, we propose 2.1, aimed at enhancing docking precision through integration novel algorithms...
AutoDock VINA is one of the most-used docking tools in early stage modern drug discovery. It uses a Monte-Carlo based iterated search method and multithreading parallelism scheme on multicore machines to improve accuracy speed. However, virtual screening from huge compound databases common for discovery, which puts forward great demand higher speed VINA. Therefore, we propose fast VINA-GPU, expands lanes into thousands ones coupling with largely reduced number steps each lane. Furthermore,...
AutoDock Vina (Vina) stands out among numerous molecular docking tools due to its precision and comparatively high speed, playing a key role in the drug discovery process. Hardware acceleration of on FPGA platforms offers energy-efficiency approach speed up However, previous FPGA-based accelerators exhibit several shortcomings: 1) Simple uniform quantization results inevitable accuracy drop; 2) Due Vina's complex computing process, evaluation optimization phase for hardware design becomes...
AutoDock Vina is one of the most popular molecular docking tools. In latest benchmark CASF-2016 for comparative assessment scoring functions, won best power among all Modern drug discovery facing common scenario on large virtual screening hits from huge compound databases. Due to seriality characteristic algorithm, there no successful report its parallel acceleration with GPUs. Current typically relies stack computing as well allocation resource and tasks, such VirtualFlow platform. The vast...
AutoDock Vina is one of the most popular molecular docking tools. In latest benchmark CASF-2016 for comparative assessment scoring functions, won best power among all Modern drug discovery facing common scenario on large virtual screening hits from huge compound databases. Due to seriality characteristic algorithm, there no successful report its parallel acceleration with GPUs. Current typically relies stack computing as well allocation resource and tasks, such VirtualFlow platform. The vast...
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 screens. Among these tools, AutoDock Vina and its numerous derivatives most popular have become standard pipeline molecular in modern discovery. Our recent Vina-GPU method realized 14-fold acceleration against on a piece NVIDIA RTX 3090 GPU one screening case. Further speedup with GPUs is beneficial to...
AutoDock VINA is one of the most-used docking tools in early stage modern drug discovery. It uses a Monte-Carlo based iterated search method and multithreading parallelism scheme on multicore machines to improve accuracy speed. However, virtual screening from huge compound databases common for discovery, which puts forward great demand higher speed VINA. Therefore, we propose fast VINA-GPU, expands lanes into thousands ones coupling with largely reduced number steps each lane. Furthermore,...
AutoDock VINA is one of the most-used docking tools in early stage modern drug discovery. It uses a Monte-Carlo based iterated search method and multithreading parallelism scheme on multicore machines to improve accuracy speed. However, virtual screening from huge compound databases common for discovery, which puts forward great demand higher speed VINA. Therefore, we propose fast VINA-GPU, expands lanes into thousands ones coupling with largely reduced number steps each lane. Furthermore,...
AutoDock VINA is one of the most-used docking tools in early stage modern drug discovery. It uses a Monte-Carlo based iterated search method and multithreading parallelism scheme on multicore machines to improve accuracy speed. However, virtual screening from huge compound databases common for discovery, which puts forward great demand higher speed VINA. Therefore, we propose fast VINA-GPU, expands lanes into thousands ones coupling with largely reduced number steps each lane. Furthermore,...