scholarly journals Using Stochastic Computing for Virtual Screening Acceleration

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2981
Author(s):  
Christiam F. Frasser ◽  
Carola de Benito ◽  
Erik S. Skibinsky-Gitlin ◽  
Vincent Canals ◽  
Joan Font-Rosselló ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Processing Speed ◽  
High Performance ◽  
Scientific Field ◽  
Screening Methods ◽  
Complex Data ◽  
Stochastic Computing ◽  
Classification Framework ◽  
Molecular Databases ◽  
Drug Leads

Stochastic computing is an emerging scientific field pushed by the need for developing high-performance artificial intelligence systems in hardware to quickly solve complex data processing problems. This is the case of virtual screening, a computational task aimed at searching across huge molecular databases for new drug leads. In this work, we show a classification framework in which molecules are described by an energy-based vector. This vector is then processed by an ultra-fast artificial neural network implemented through FPGA by using stochastic computing techniques. Compared to other previously published virtual screening methods, this proposal provides similar or higher accuracy, while it improves processing speed by about two or three orders of magnitude.

Parallelization of Molecular Docking: A Review

2018 ◽  
Vol 18 (12) ◽  
pp. 1015-1028 ◽  
Author(s):  
Dong Dong ◽  
Zhijian Xu ◽  
Wu Zhong ◽  
Shaoliang Peng
Keyword(s):  
Big Data ◽  
Molecular Docking ◽  
Computational Complexity ◽  
Virtual Screening ◽  
High Performance ◽  
Screening Methods ◽  
Small Molecule Ligands ◽  
Target Binding ◽  
Performance Computing ◽  
Target Binding Site

Molecular docking, as one of the widely used virtual screening methods, aims to predict the binding-conformations of small molecule ligands to the appropriate target binding site. Because of the computational complexity and the arrival of the big data era, molecular docking requests High- Performance Computing (HPC) to improve its performance and accuracy. We discuss, in detail, the advances in accelerating molecular docking software in parallel, based on the different common HPC platforms, respectively. Not only the existing suitable programs have been optimized and ported to HPC platforms, but also many novel parallel algorithms have been designed and implemented. This review focuses on the techniques and methods adopted in parallelizing docking software. Where appropriate, we refer readers to exemplary case studies.

METADOCK: A parallel metaheuristic schema for virtual screening methods

2017 ◽  
Vol 32 (6) ◽  
pp. 789-803 ◽  
Author(s):  
Baldomero Imbernón ◽  
José M Cecilia ◽  
Horacio Pérez-Sánchez ◽  
Domingo Giménez
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
High Performance ◽  
Optimization Technique ◽  
Screening Methods ◽  
Scoring Functions ◽  
Dynamic Assignment ◽  
Parallel Metaheuristics ◽  
Heterogeneous Architectures ◽  
Metaheuristic Methods

Virtual screening through molecular docking can be translated into an optimization problem, which can be tackled with metaheuristic methods. 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. 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 application decides the optimization technique at running time by setting a configuration schema. 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. 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.

scholarly journals Recent Advances in Discovery of Lead Structures from Microbial Natural Products: Genomics- and Metabolomics-Guided Acceleration

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2542
Author(s):  
Linda Sukmarini
Keyword(s):  
Natural Products ◽  
Genome Mining ◽  
Screening Methods ◽  
Technological Advancement ◽  
Natural Origin ◽  
Drug Discovery And Development ◽  
New Methodologies ◽  
Analytical Instrumentation ◽  
Microbial Natural Products ◽  
Drug Leads

Natural products (NPs) are evolutionarily optimized as drug-like molecules and remain the most consistently successful source of drugs and drug leads. They offer major opportunities for finding novel lead structures that are active against a broad spectrum of assay targets, particularly those from secondary metabolites of microbial origin. Due to traditional discovery approaches’ limitations relying on untargeted screening methods, there is a growing trend to employ unconventional secondary metabolomics techniques. Aided by the more in-depth understanding of different biosynthetic pathways and the technological advancement in analytical instrumentation, the development of new methodologies provides an alternative that can accelerate discoveries of new lead-structures of natural origin. This present mini-review briefly discusses selected examples regarding advancements in bioinformatics and genomics (focusing on genome mining and metagenomics approaches), as well as bioanalytics (mass-spectrometry) towards the microbial NPs-based drug discovery and development. The selected recent discoveries from 2015 to 2020 are featured herein.

NAND And NOR Logic-In-Memory Comprising Silicon Nanowire Feedback Field-Effect Transistors

2021 ◽  
Author(s):  
Yejin Yang ◽  
Juhee Jeon ◽  
Jaemin Son ◽  
Kyoungah Cho ◽  
Sangsig Kim
Keyword(s):  
Processing Speed ◽  
Field Effect ◽  
High Performance ◽  
Field Effect Transistors ◽  
Silicon Nanowire ◽  
Logic Gate ◽  
High Energy ◽  
Zero Bias ◽  
Computing Systems ◽  
Data Movement

Abstract The processing of large amounts of data requires a high energy efficiency and fast processing time for high-performance computing systems. However, conventional von Neumann computing systems have performance limitations because of bottlenecks in data movement between separated processing and memory hierarchy, which causes latency and high power consumption. To overcome this hindrance, logic-in-memory (LIM) has been proposed that performs both data processing and memory operations. Here, we present a NAND and NOR LIM composed of silicon nanowidre feedback field-effect transistors, whose configuration resembles that of CMOS logic gate circuits. The LIM can perform memory operations to retain its output logic under zero-bias conditions as well as logic operations with a high processing speed of nanoseconds. The newly proposed dynamic voltage-transfer characteristics verify the operating principle of the LIM. This study demonstrates that the NAND and NOR LIM has promising potential to resolve power and processing speed issues.

scholarly journals Practical Model Selection for Prospective Virtual Screening

2018 ◽  
Author(s):  
Shengchao Liu ◽  
Moayad Alnammi ◽  
Spencer S. Ericksen ◽  
Andrew F. Voter ◽  
Gene E. Ananiev ◽  
...  
Keyword(s):  
Random Forest ◽  
Virtual Screening ◽  
Protein Interactions ◽  
High Throughput Screening ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Screening Algorithm ◽  
Screening Performance ◽  
Wide Range ◽  
Public Datasets

AbstractVirtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the dataset and evaluation strategy. We consider a wide range of ligand-based machine learning and docking-based approaches for virtual screening on two protein-protein interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing which algorithm is best for prospective compound prioritization. Our workflow identifies a random forest as the best algorithm for these targets over more sophisticated neural network-based models. The top 250 predictions from our selected random forest recover 37 of the 54 active compounds from a library of 22,434 new molecules assayed on PriA-SSB. We show that virtual screening methods that perform well in public datasets and synthetic benchmarks, like multi-task neural networks, may not always translate to prospective screening performance on a specific assay of interest.

scholarly journals Generating Property-Matched Decoy Molecules Using Deep Learning

2020 ◽  
Author(s):  
Fergus Imrie ◽  
Anthony R. Bradley ◽  
Charlotte M. Deane
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Method Development ◽  
Screening Method ◽  
Screening Methods ◽  
Additional Risk ◽  
Link Type ◽  
Screening Performance ◽  
And Training ◽  
Virtual Screening Performance

An essential step in the development of virtual screening methods is the use of established sets of actives and decoys for benchmarking and training. However, the decoy molecules in commonly used sets are biased meaning that methods often exploit these biases to separate actives and decoys, rather than learning how to perform molecular recognition. This fundamental issue prevents generalisation and hinders virtual screening method development. We have developed a deep learning method (DeepCoy) that generates decoys to a user’s preferred specification in order to remove such biases or construct sets with a defined bias. We validated DeepCoy using two established benchmarks, DUD-E and DEKOIS 2.0. For all DUD-E targets and 80 of the 81 DEKOIS 2.0 targets, our generated decoy molecules more closely matched the active molecules’ physicochemical properties while introducing no discernible additional risk of false negatives. The DeepCoy decoys improved the Deviation from Optimal Embedding (DOE) score by an average of 81% and 66%, respectively, decreasing from 0.163 to 0.032 for DUD-E and from 0.109 to 0.038 for DEKOIS 2.0. Further, the generated decoys are harder to distinguish than the original decoy molecules via docking with Autodock Vina, with virtual screening performance falling from an AUC ROC of 0.71 to 0.63. The code is available at https://github.com/oxpig/DeepCoy. Generated molecules can be downloaded from http://opig.stats.ox.ac.uk/resources.

scholarly journals Constructing and Validating High-Performance MIEC-SVM Models in Virtual Screening for Kinases: A Better Way for Actives Discovery

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Huiyong Sun ◽  
Peichen Pan ◽  
Sheng Tian ◽  
Lei Xu ◽  
Xiaotian Kong ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Performance

Science Communication with Dinosaurs

2012 ◽  
pp. 587-611
Author(s):  
Phillip L. Manning ◽  
Peter L. Falkingham
Keyword(s):  
High Performance Computing ◽  
Public Engagement ◽  
Science Communication ◽  
High Performance ◽  
Computational Science ◽  
Data Sets ◽  
Complex Data ◽  
Multidisciplinary Research ◽  
Complex Data Sets ◽  
Performance Computing

Dinosaurs successfully conjure images of lost worlds and forgotten lives. Our understanding of these iconic, extinct animals now comes from many disciplines, not just the science of palaeontology. In recent years palaeontology has benefited from the application of new and existing techniques from physics, biology, chemistry, engineering, but especially computational science. The application of computers in palaeontology is highlighted in this chapter as a key area of development in studying fossils. The advances in high performance computing (HPC) have greatly aided and abetted multiple disciplines and technologies that are now feeding paleontological research, especially when dealing with large and complex data sets. We also give examples of how such multidisciplinary research can be used to communicate not only specific discoveries in palaeontology, but also the methods and ideas, from interrelated disciplines to wider audiences. Dinosaurs represent a useful vehicle that can help enable wider public engagement, communicating complex science in digestible chunks.

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