METADOCK 2: a high-throughput parallel metaheuristic scheme for molecular docking

Imbernón, Baldomero; Serrano, Antonio Martínez; Bueno-Crespo, Andrés; Abellán, José Luis; Pérez‐Sánchez, Horacio; Cecilia, José M.

doi:10.1093/bioinformatics/btz958

Cited by 12 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parallel drug-target interaction (DTI) research has been carried out using several schemes, including breadth-first search (BFS) [6], molecular docking with GPU [7], and BINDSURF, which is a virtual screening methodology to find a protein binding site for a ligand [8]. In [6], BFS is used to predict drug-target interaction in a graph and is optimized by parallelization using CUDA, which gained a speed-up of 51.33 times by using four threads.…”

Section: Jurnal Ilmu Komputer Dan Informatikamentioning

confidence: 99%

Herbal Compound Screening with GPU Computation on ZINC Database through Similarity Comparison Approach

Darmawan

Kusuma

Rahmawan

2022

khif

View full text Add to dashboard Cite

Covid-19 is a global pandemic that drives many researchers to strive to look for its solution, especially in the field of health, medicine, and total countermeasures. Early screening with in-silico processes is crucial to minimize the search space of the potential drugs to cure a disease. This research aims to find potential drugs of covid-19 disease in the ZINC database to be further investigated through the in-vitro method. About 997.402.117 chemical compounds are searched about their similarity to some of the confirmed drugs to combat coronavirus. Sequential computation would take months to accomplish this task. The general programming graphic processing unit approach is used to implement a similarity comparison algorithm in parallel, in order to speed up the process. The result of this study shows the parallel algorithm implementation can speed up the computation process up to 55 times faster, and also that some of the chemical compounds have high similarity scores and can be found in nature

show abstract

Section: Jurnal Ilmu Komputer Dan Informatikamentioning

confidence: 99%

Herbal Compound Screening with GPU Computation on ZINC Database through Similarity Comparison Approach

Darmawan

Kusuma

Rahmawan

2022

khif

View full text Add to dashboard Cite

show abstract

“…Here, we only select the freely representational and available molecular docking software. Some software such as GlamDock, 115 MolAICAl, 54,55 METADOCK 2, 116 ArgusLab, 117 and HomDock 118 are also efficient and accurate tools for molecular docking.…”

Section: De Novo Drug Design Using Deep Learning Models and Molecular...mentioning

confidence: 99%

Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

Bai

Liu

Tian

et al. 2021

WIREs Comput Mol Sci

View full text Add to dashboard Cite

De novo drug design is a stationary way to build novel ligands in the confined pocket of receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation is a dynamical way to study the interaction mechanism between the ligands and receptors based on the molecular force field. De novo drug design and MD simulation are effective tools for novel drug discovery. With the development of technology, deep learning methods, and interpretable machine learning (IML) have emerged in the research area of drug design. Deep learning methods and IML can be used further to improve the efficiency and accuracy of de novo drug design and MD simulations. The application summary of deep learning methods for de novo drug design, MD simulations, and IML can further promote the technical development of drug discovery. In this article, two major workflow methods and the related components of classical algorithm and deep learning are described for de novo drug design from a new perspective. The application progress of deep learning is also summarized for MD simulations. Furthermore, IML is introduced for the deep learning model interpretability of de novo drug design and MD simulations. Our paper deals with an interesting topic about deep learning applications of de novo drug design and MD simulations for the scientific community. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

show abstract

“…22 GPU has been applied to accelerate molecular docking in several tools. 11,12,21,[23][24][25][26][27][28][29][30][31] For example, AutoDock-GPU provides an OpenCL implementation of AutoDock4 to exploit both GPU and CPU parallel architectures. By exploring three levels of parallelism (runs, individual, fine-grained tasks) on the Lamarckian Genetic Algorithm (LGA) algorithm, AutoDock-GPU achieves the maximum of 350-fold acceleration on total runtime against the singlethreaded CPU implementation.…”

Section: Introductionmentioning

confidence: 99%

Accelerating AutoDock VINA with GPUs

Shidi

Ruiqi²,

Mengru³

et al. 2022

Preprint

View full text Add to dashboard Cite

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 the most common scenario on 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 seriously limit the popularity of AutoDock Vina and the flexibility of usage in modern drug discovery. Thus, the design of a new method for accelerating AutoDock Vina with GPUs is greatly needed for reducing the investment for large virtual screens, and also for a wide application in large-scale virtual screening on personal computers, station servers orcloud computing etc. Our proposed method Vina-GPU greatly raises the number of initial random conformations and reduces the search depth of each lane, and then a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmarks show that Vina-GPU reaches a maximum of 403- fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential of pushing the popularization of AutoDock Vina in large virtual screens. The Vina-GPU code and tool can be freely available at http:// www.noveldelta.com/Vina_GPU for academic usage.

show abstract

METADOCK 2: a high-throughput parallel metaheuristic scheme for molecular docking

Cited by 12 publications

References 20 publications

Herbal Compound Screening with GPU Computation on ZINC Database through Similarity Comparison Approach

Herbal Compound Screening with GPU Computation on ZINC Database through Similarity Comparison Approach

Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

Accelerating AutoDock VINA with GPUs

Contact Info

Product

Resources

About