Optimizing FPGA-based Accelerator Design for Large-Scale Molecular Similarity Search (Special Session Paper)

2021 ◽  
Author(s):  
Hongwu Peng ◽  
Shiyang Chen ◽  
Zhepeng Wang ◽  
Junhuan Yang ◽  
Scott A. Weitze ◽  
...  
Keyword(s):  
Similarity Search ◽  
Large Scale ◽  
Molecular Similarity ◽  
Special Session ◽  
Session Paper ◽  
Accelerator Design

FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper

2021 ◽  
Author(s):  
Daniel Manu ◽  
Yi Sheng ◽  
Junhuan Yang ◽  
Jieren Deng ◽  
Tong Geng ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Special Session ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Session Paper

Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework ICCAD Special Session Paper

2021 ◽  
Author(s):  
Muhammad Shafique ◽  
Alberto Marchisio ◽  
Rachmad Vidya Wicaksana Putra ◽  
Muhammad Abdullah Hanif
Keyword(s):  
Energy Efficient ◽  
Cross Layer ◽  
Special Session ◽  
Session Paper

Brain-Inspired Computing: Adventure from Beyond CMOS Technologies to Beyond von Neumann Architectures ICCAD Special Session Paper

2021 ◽  
Author(s):  
Hussam Amrouch ◽  
Jian-Jia Chen ◽  
Kaushik Roy ◽  
Yuan Xie ◽  
Indranil Chakraborty ◽  
...  
Keyword(s):  
Special Session ◽  
Von Neumann ◽  
Session Paper ◽  
Beyond Cmos

Intelligent Molecular Identification for High Performance Organosulfide Capture Using Active Machine Learning Algorithm

2021 ◽  
Author(s):  
Yuxiang Chen ◽  
Chuanlei Liu ◽  
Yang An ◽  
Yue Lou ◽  
Yang Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Similarity Search ◽  
Data Science ◽  
Molecular Similarity ◽  
Molecular Design ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Methyl Mercaptan ◽  
Model Framework ◽  
Modeling Framework

Machine learning and computer-aided approaches significantly accelerate molecular design and discovery in scientific and industrial fields increasingly relying on data science for efficiency. The typical method used is supervised learning which needs huge datasets. Semi-supervised machine learning approaches are effective to train unlabeled data with improved modeling performance, whereas they are limited by the accumulation of prediction errors. Here, to screen solvents for removal of methyl mercaptan, a type of organosulfur impurities in natural gas, we constructed a computational framework by integrating molecular similarity search and active learning methods, namely, molecular active selection machine learning (MASML). This new model framework identifies the optimal molecules set by molecular similarity search and iterative addition to the training dataset. Among all 126,068 compounds in the initial dataset, 3 molecules were identified to be promising for methyl mercaptan (MeSH) capture, including benzylamine (BZA), p-methoxybenzylamine (PZM), and N,N-diethyltrimethylenediamine (DEAPA). Further experiments confirmed the effectiveness of our modeling framework in efficient molecular design and identification for capturing methyl mercaptan, in which DEAPA presents a Henry's law constant 89.4% lower than that of methyl diethanolamine (MDEA).

Toward Security Closure in the Face of Reliability Effects ICCAD Special Session Paper

2021 ◽  
Author(s):  
Jens Lienig ◽  
Susann Rothe ◽  
Matthias Thiele ◽  
Nikhil Rangarajan ◽  
Mohammed Ashraf ◽  
...  
Keyword(s):  
Special Session ◽  
Session Paper ◽  
The Face

Similarity Search in Large-Scale Graph Databases

2017 ◽  
pp. 507-529 ◽  
Author(s):  
Peixiang Zhao
Keyword(s):  
Similarity Search ◽  
Large Scale ◽  
Graph Databases
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