Benchmarking the Performance of Irregular Computations in AutoDock-GPU Molecular Docking.

Irregular applications can be found in different scientific fields. In computer-aided drug design, molecular docking simulations play an important role in finding promising drug candidates. AutoDock is a software application widely used for predicting molecular interactions at close distances. It is characterized by irregular computations and long execution runtimes. In recent years, a hardware-accelerated version of AutoDock, called AutoDock-GPU, has been under active development. This work benchmarks the recent code and algorithmic enhancements incorporated into AutoDock-GPU. Particularly, we analyze the impact on execution runtime of techniques based on early termination. These enable AutoDock-GPU to explore the molecular space as necessary, while safely avoiding redundant computations. Our results indicate that it is possible to achieve average runtime reductions of 50% by using these techniques. Furthermore, a comprehensive literature review is also provided, where our work is compared to relevant approaches leveraging hardware acceleration for molecular docking.

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research.

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU.

In this paper we describe our approaches to predict the binding mode of twenty BACE1 ligands as part of Grand Challenge 4 (GC4), organized by the Drug Design Data Resource. Calculations for all submissions (except for one, which used AutoDock4.2) were performed using AutoDock-GPU, the new GPU-accelerated version of AutoDock4 implemented in OpenCL, which features a gradient-based local search. The pose prediction challenge was organized in two stages. In Stage 1a, the protein conformations associated with each of the ligands were undisclosed, so we docked each ligand to a set of eleven receptor conformations, chosen to maximize the diversity of binding pocket topography. Protein conformations were made available in Stage 1b, making it a re-docking task. For all calculations, macrocyclic conformations were sampled on the fly during docking, taking the target structure into account. To leverage information from existing structures containing BACE1 bound to ligands available in the PDB, we tested biased docking and pose filter protocols to facilitate poses resembling those experimentally determined. Both pose filters and biased docking resulted in more accurate docked poses, enabling us to predict for both Stages 1a and 1b ligand poses within 2 Å RMSD from the crystallographic pose. Nevertheless, many of the ligands could be correctly docked without using existing structural information, demonstrating the usefulness of physics-based scoring functions, such as the one used in AutoDock4, for structure based drug design.

Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4.

Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson-Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can predict binding affinities more accurately. In this perspective, we decided to take part in Stage 2 of the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, AutoDock4, to that of MM-GBSA in predicting the binding affinities of a set of [Formula: see text]-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: (i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, (ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, (iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding affinity predictions.

Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition.

Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called "characteristic vectors") from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the "characteristic vectors"were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children.