GPU-optimized Approaches to Molecular Docking-based Virtual Screening in Drug Discovery: A Comparative Analysis

by   Emanuele Vitali, et al.

COVID-19 has shown the importance of having a fast response against pandemics. Finding a novel drug is a very long and complex procedure, and it is possible to accelerate the preliminary phases by using computer simulations. In particular, virtual screening is an in-silico phase that is needed to filter a large set of possible drug candidates to a manageable number. This paper presents the implementations and a comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. The first adopts a traditional approach that spreads the computation required to evaluate a single molecule across the entire GPU. The second uses a batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel, without considering the latency to process a single molecule. The paper describes the advantages and disadvantages of the proposed solutions, highlighting implementation details that impact the performance. Experimental results highlight the different performance of the two methods on several target molecule databases while running on NVIDIA A100 GPUs. The two implementations have a strong dependency with respect to the data to be processed. For both cases, the performance is improving while reducing the dimension of the target molecules (number of atoms and rotatable bonds). The two methods demonstrated a different behavior with respect to the size of the molecule database to be screened. While the latency one reaches sooner (with fewer molecules) the performance plateau in terms of throughput, the batched one requires a larger set of molecules. However, the performances after the initial transient period are much higher (up to 5x speed-up). Finally, to check the efficiency of both implementations we deeply analyzed their workload characteristics using the instruction roof-line methodology.


Improving computation efficiency using input and architecture features for a virtual screening application

Virtual screening is an early stage of the drug discovery process that s...

High Throughput Virtual Screening with Data Level Parallelism in Multi-core Processors

Improving the throughput of molecular docking, a computationally intensi...

Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks

Due to the current severe acute respiratory syndrome coronavirus 2 (SARS...

Exploiting OpenMP & OpenACC to Accelerate a Molecular Docking Mini-App in Heterogeneous HPC Nodes

In drug discovery, molecular docking is the task in charge of estimating...

3D Molecular Generation via Virtual Dynamics

Structure-based drug design, i.e., finding molecules with high affinitie...

Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening

We propose a benchmark to study surrogate model accuracy for protein-lig...

Please sign up or login with your details

Forgot password? Click here to reset