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

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.

scholarly journals Publisher Correction: High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble

2020 ◽  
Vol 27 (6) ◽  
pp. 604-604
Author(s):  
Laura R. Ganser ◽  
Janghyun Lee ◽  
Atul Rangadurai ◽  
Dawn K. Merriman ◽  
Megan L. Kelly ◽  
...  
Keyword(s):  
High Resolution ◽  
Virtual Screening ◽  
High Performance

scholarly journals A Review on Parallel Virtual Screening Softwares for High-Performance Computers

2022 ◽  
Vol 15 (1) ◽  
pp. 63
Author(s):  
Natarajan Arul Murugan ◽  
Artur Podobas ◽  
Davide Gadioli ◽  
Emanuele Vitali ◽  
Gianluca Palermo ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
High Throughput Screening ◽  
High Performance ◽  
Parallel Implementation ◽  
Scoring Function ◽  
Scoring Functions ◽  
Screening Programs ◽  
Lead Compounds ◽  
High Performance Computers

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein–ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.

scholarly journals Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Fang-Chung Chen
Keyword(s):  
Virtual Screening ◽  
Conjugated Polymers ◽  
Ensemble Learning ◽  
High Performance ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Organic Photovoltaic ◽  
Support Vector ◽  
Learning Approaches

Herein, we report virtual screening of potential semiconductor polymers for high-performance organic photovoltaic (OPV) devices using various machine learning algorithms. We particularly focus on support vector machine (SVM) and ensemble learning approaches. We found that the power conversion efficiencies of the device prepared with the polymer candidates can be predicted with their structure fingerprints as the only inputs. In other words, no preliminary knowledge about material properties was required. Additionally, the predictive performance could be further improved by “blending” the results of the SVM and random forest models. The resulting ensemble learning algorithm might open up a new opportunity for more precise, high-throughput virtual screening of conjugated polymers for OPV devices.

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