Paper Info
Title
Ligand based virtual screening using SVM on GPU.
Abstract
In silico methods play an essential role in modern drug discovery methods. Virtual screening, an in silico method, is used to filter out the chemical space on which actual wet lab experiments are need to be conducted. Ligand based virtual screening is a computational strategy using which one can build a model of the target protein based on the knowledge of the ligands that bind successfully to the target. This model is then used to predict if the new molecule is likely to bind to the target. Support vector machine, a supervised learning algorithm used for classification, can be utilized for virtual screening the ligand data. When used for virtual screening purpose, SVM could produce interesting results. But since we have a huge ligand data, the time taken for training the SVM model is quite high compared to other learning algorithms. By parallelizing these algorithms on multi-core processors, one can easily expedite these discoveries. In this paper, a GPU based ligand based virtual screening tool (GpuSVMScreen) which uses SVM have been proposed and bench-marked. This data parallel virtual screening tool provides high throughput by running in short time. The proposed GpuSVMScreen can successfully screen large number of molecules (billions) also. The source code of this tool is available at http://ccc.nitc.ac.in/project/GPUSVMSCREEN.
Year
DOI
Venue
2019
10.1016/j.compbiolchem.2019.107143
Computational Biology and Chemistry
Keywords
Field
DocType
Virtual screening,Support vector machine,Machine learning,Kernel,Graphics processing unit,Parallel computing,Ligands
Drug discovery,Biology,Source code,Support vector machine,Supervised training,Artificial intelligence,Bioinformatics,Chemical space,Throughput,Virtual screening,Machine learning,In silico
Journal
Volume
ISSN
Citations 
83
1476-9271
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
P B Jayaraj100.34
Samyak Jain202.37