Recent Developments in Structure-Based Virtual Screening Approaches

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, as well as a low number of new drugs that are approved each year. To solve these problems, new and innovate technologies are needed that make the drug discovery process of small-molecules more time and cost-efficient, and which allow to target previously undruggable target classes such as protein-protein interactions. Structure-based virtual screenings have become a leading contender in this context. In this review, we give an introduction to the foundations of structure-based virtual screenings, and survey their progress in the past few years. We outline key principles, recent success stories, new methods, available software, and promising future research directions. Virtual screenings have an enormous potential for the development of new small-molecule drugs, and are already starting to transform early-stage drug discovery.Comment: 22 pages, 2 figure

GPU-accelerated Chemical Similarity Assessment for Large Scale Databases

The assessment of chemical similarity between molecules is a basic operation in chemoinformatics, a computational area concerning with the manipulation of chemical structural information. Comparing molecules is the basis for a wide range of applications such as searching in chemical databases, training prediction models for virtual screening or aggregating clusters of similar compounds. However, currently available multimillion databases represent a challenge for conventional chemoinformatics algorithms raising the necessity for faster similarity methods. In this paper, we extensively analyze the advantages of using many-core architectures for calculating some commonly-used chemical similarity coe_cients such as Tanimoto, Dice or Cosine. Our aim is to provide a wide-breath proof-of-concept regarding the usefulness of GPU architectures to chemoinformatics, a class of computing problems still uncovered. In our work, we present a general GPU algorithm for all-to-all chemical comparisons considering both binary fingerprints and floating point descriptors as molecule representation. Subsequently, we adopt optimization techniques to minimize global memory accesses and to further improve e_ciency. We test the proposed algorithm on different experimental setups, a laptop with a low-end GPU and a desktop with a more performant GPU. In the former case, we obtain a 4-to-6-fold speed-up over a single-core implementation for fingerprints and a 4-to-7-fold speed-up for descriptors. In the latter case, we respectively obtain a 195-to-206-fold speed-up and a 100-to-328-fold speed-up.National Institutes of Health (U.S.) (grant GM079804)National Institutes of Health (U.S.) (grant GM086145

Acceleration of parasitic multistatic radar system using GPGPU

This dissertation details the implementation of PMR [Parasitic Multistatic Radar] signal processing chain in the GPGPU [General Purpose Graphic Processing Units] platform. The primary objective of the project is to accelerate the signal processing chain without compromising the algorithm efficiency and to prove that GPGPUs are a promising platform for parasitic radar signal processing

Accelerating Scientific Computing Models Using GPU Processing

GPGPUs offer significant computational power for programmers to leverage. This computational power is especially useful when utilized for accelerating scientific models. This thesis analyzes the utilization of GPGPU programming to accelerate scientific computing models. First the construction of hardware for visualization and computation of scientific models is discussed. Several factors in the construction of the machines focus on the performance impacts related to scientific modeling. Image processing is an embarrassingly parallel problem well suited for GPGPU acceleration. An image processing library was developed to show the processes of recognizing embarrassingly parallel problems and serves as an excellent example of converting from a serial CPU implementation to a GPU accelerated implementation. Genetic algorithms are biologically inspired heuristic search algorithms based on natural selection. The Tetris genetic algorithm with A* pathfinding discusses memory bound limitations that can prevent direct algorithm conversions from the CPU to the GPU. An analysis of an existing landscape evolution model, CHILD, for GPU acceleration explores that even when a model shows promise for GPU acceleration, the underlying data structures can have a significant impact upon that ability to move to a GPU implementation. CHILD also offers an example of creating tighter MATLAB integration between existing models. Lastly, a parallel spatial sorting algorithm is discussed as a possible replacement for current spatial sorting algorithms implemented in models such as smoothed particle hydrodynamics

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.Comment: Accepted for publication at the ACM Computing Surveys (CSUR) journal, 201