Its results can also guide posterior application of more detailed VS methods in concrete binding sites of proteins, and its utilization can aid in drug discovery, design, repurposing and therefore help considerably in clinical research. We present BINDSURF, a novel VS methodology that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases.īINDSURF is an efficient and fast blind methodology for the determination of protein binding sites depending on the ligand, that uses the massively parallel architecture of GPUs for fast pre-screening of large ligand databases. However, it has been demonstrated that in many cases, diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. Most VS methods suppose a unique binding site for the target, usually derived from the interpretation of the protein crystal structure. Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. BUDE also exploits OpenCL to deliver effective performance portability across a broad spectrum of different computer architectures from different vendors, including GPUs from Nvidia and AMD, Intel’s Xeon Phi and multi-core CPUs with SIMD instruction sets. Our highly optimized OpenCL implementation of BUDE sustains 1.43 TFLOP/s on a single Nvidia GTX 680 GPU, or 46% of peak performance. In this work, we describe BUDE, the Bristol University Docking Engine, which has been ported to the OpenCL industry standard parallel programming language in order to exploit the performance of modern many-core processors. The latter simulates the binding of drug molecules to their targets, typically protein molecules. Traditional, lab-based methods are increasingly being augmented with computational methods, ranging from simple molecular similarity searches through more complex pharmacophore matching to more computationally intensive approaches, such as molecular docking. A cooperative scheduling of jobs optimizes the quality of the solution and the overall performance of the simulation, so opening a new path for further developments of virtual screening methods on high-performance contemporary heterogeneous platforms.ĭrug screening is an important part of the drug development pipeline for the pharmaceutical industry. Our proposed solution finds a good workload balance via dynamic assignment of jobs to heterogeneous resources which perform independent metaheuristic executions when computing different molecular interactions required by the scoring functions in use. The application decides the optimization technique at running time by setting a configuration schema. This paper introduces METADOCK, a novel molecular docking methodology based on parameterized and parallel metaheuristics and designed to leverage heterogeneous computers based on heterogeneous architectures. The interaction between two chemical compounds (typically a protein, enzyme or receptor, and a small molecule, or ligand) is calculated by using highly computationally demanding scoring functions that are computed at several binding spots located throughout the protein surface. Virtual screening through molecular docking can be translated into an optimization problem, which can be tackled with metaheuristic methods. The efficient exploitation of these systems enables HYPERDOCK to improve ligand–receptor binding. The different levels of parallelism can be used to exploit the parallelism offered by computational systems composed of multicore CPU + multi-GPUs. HYPERDOCK exploits the parallelism of METADOCK and includes parallelism at its own level. Multiple metaheuristics are explored, so the process is computationally demanding. HYPERDOCK represents a step forward the exploration for satisfactory metaheuristics is systematically approached by means of hyperheuristics working on top of the metaheuristic schema of METADOCK. METADOCK is a tool for the application of metaheuristics to VS in heterogeneous clusters of computers based on central processing unit (CPU) and graphics processing unit (GPU). The computational requirements of VS, along with the size of the databases, propitiate the use of high-performance computing. Virtual screening (VS) methods aid clinical research by predicting the interaction of ligands with pharmacological targets.
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