Department of Computer Science, National Institute of Technology Calicut, India.
Department of Computer Science, National Institute of Technology Calicut, India.
Comput Biol Chem. 2019 Dec;83:107143. doi: 10.1016/j.compbiolchem.2019.107143. Epub 2019 Nov 10.
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.
在计算机辅助药物设计中，计算机模拟方法发挥着至关重要的作用。虚拟筛选作为一种计算机模拟方法，可用于筛选出需要实际进行湿实验的化学空间。配体虚拟筛选是一种基于配体与靶标成功结合的知识构建靶标蛋白模型的计算策略。然后，该模型可用于预测新分子是否可能与靶标结合。支持向量机是一种用于分类的监督学习算法，可用于虚拟筛选配体数据。当用于虚拟筛选目的时，SVM 可以产生有趣的结果。但是，由于我们有大量的配体数据，与其他学习算法相比，训练 SVM 模型所需的时间非常长。通过在多核处理器上并行化这些算法，我们可以轻松加速这些发现。在本文中，提出并基准测试了一种基于 GPU 的配体虚拟筛选工具（GpuSVMScreen），该工具使用 SVM。这种数据并行虚拟筛选工具通过在短时间内运行提供高吞吐量。该工具可以成功筛选大量的分子（数十亿个）。该工具的源代码可在 http://ccc.nitc.ac.in/project/GPUSVMSCREEN 上获取。
