Machine Learning Enables Rapid Screening of Reactive Fly Ashes Based on Their Network Topology

2021 ◽  
Vol 9 (7) ◽  
pp. 2639-2650
Author(s):  
Yu Song ◽  
Kai Yang ◽  
Jingyi Chen ◽  
Kaixin Wang ◽  
Gaurav Sant ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Topology ◽  
Rapid Screening ◽  
Fly Ashes

scholarly journals RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

2020 ◽  
Author(s):  
Stefan Holderbach ◽  
Lukas Adam ◽  
Bhyravabhotla Jayaram ◽  
Rebecca Wade ◽  
Goutam Mukherjee
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Protein Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Rapid Screening ◽  
Scoring Functions ◽  
Energy Prediction ◽  
Molecular Features ◽  
Physicochemical Descriptors

The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback that a large number of poses must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast prefiltering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance better than for the original RASPD method and comparable to traditional scoring functions on a range of different test sets without the need for generating ligand poses. Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.

scholarly journals Rapid Biomarker Screening of Alzheimer's Disease by Machine Learning and Graphene-Assisted Raman Spectroscopy

2021 ◽  
Author(s):  
Ziyang Wang ◽  
Jiarong Ye ◽  
Li Ding ◽  
Tomotaroh Granzier-Nakajima ◽  
Shubhang Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Raman Spectroscopy ◽  
Brain Slices ◽  
Rapid Screening ◽  
Monolayer Graphene ◽  
Accurate Identification ◽  
Machine Learning Classification ◽  
The Brain

As the most common cause of dementia, Alzheimer's disease (AD) faces challenges in terms of understanding of pathogenesis, developing early diagnosis and developing effective treatment. Rapid and accurate identification of AD biomarkers in the brain will be critical to provide novel insights of AD. To this end, in the current work, we developed a system that can enable a rapid screening of AD biomarkers by employing Raman spectroscopy and machine learning analyses in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD, and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, we achieved significantly increased accuracy from 77% to 98% in machine learning classification. Further, we identified the Raman signature bands that are most important in classifying AD and non-AD samples. Based on these, we managed to identify AD-related biomolecules, which have been confirmed by biochemical studies. Our Raman-machine learning integrated method is promising to greatly accelerate the study of AD and can be potentially extended to human samples and various other diseases.

Accelerated identification of high-performance catalysts for low-temperature NH3-SCR by machine learning

2021 ◽  
Author(s):  
Yi Dong ◽  
Yu Zhang ◽  
Mingchu Ran ◽  
Xiao Zhang ◽  
Shaojun Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Low Temperature ◽  
High Performance ◽  
Rapid Screening ◽  
Learning Approach ◽  
Scr Catalyst ◽  
Nh3 Scr ◽  
Machine Learning Approach

A machine learning approach for SCR catalyst discovery is presented to realize the rapid screening of optimal catalysts.

High-throughput screening of bimetallic catalysts enabled by machine learning

2017 ◽  
Vol 5 (46) ◽  
pp. 24131-24138 ◽  
Author(s):  
Zheng Li ◽  
Siwen Wang ◽  
Wei Shan Chin ◽  
Luke E. Achenie ◽  
Hongliang Xin
Keyword(s):  
Machine Learning ◽  
Kinetic Analysis ◽  
High Throughput ◽  
Bimetallic Catalysts ◽  
High Throughput Screening ◽  
Rapid Screening ◽  
Learning Framework

We present a holistic machine-learning framework for rapid screening of bimetallic catalysts with the aid of the descriptor-based kinetic analysis.

Rapid Screening Method for Determination of PCDDs and PCDFs in Fly Ashes Using GC-Ion Trap MS/MS Based on Cellular Disruptor Extraction

2006 ◽  
Vol 41 (6) ◽  
pp. 1089-1103 ◽  
Author(s):  
RAFAEL M. LATTUADA ◽  
TANIA MARA PIZZOLATO ◽  
JOÃO HENRIQUE Z. DOS SANTOS ◽  
MARIA DO CARMO RUARO PERALBA
Keyword(s):  
Ion Trap ◽  
Screening Method ◽  
Rapid Screening ◽  
Fly Ashes ◽  
Rapid Screening Method

scholarly journals Applying machine learning in network topology optimization

2021 ◽  
Vol 2 (4) ◽  
pp. 91-99
Author(s):  
Zhouwei Gang ◽  
Qianyin Rao ◽  
Lin Guo ◽  
Lin Xi ◽  
Zezhong Feng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Topology Optimization ◽  
Human Resources ◽  
Network Topology ◽  
Absorption Capacity ◽  
Transmission Network ◽  
Breadth First Search ◽  
Communication Services ◽  
Node Number ◽  
Network Topology Optimization

Nowadays, telecommunications have become an indispensable part of our life, 5G technology brings better network speeds, helps the AR and VR industry, and connects everything. It will deeply change our society. Transmission is the vessel of telecommunications. While the vessel is not so healthy, some of them are overloaded, meanwhile, others still have lots of capacity. It not only affects the customer experience, but also affects the development of communication services because of a resources problem. A transmission network is composed of transmission nodes and links. So that the possible topology numbers equal to node number multiplied by number of links means it is impossible for humans to optimize. We use Al instead of humans for topology optimization. The AI optimization solution uses an ITU Machine Learning (ML) standard, Breadth-First Search (BFS) greedy algorithm and other mainstream algorithms to solve the problem. It saves a lot of money and human resources, and also hugely improves traffic absorption capacity. The author comes from the team named "No Boundaries". The team attend ITU AI/ML in 5G Challenge and won the Gold champions (1st place).

scholarly journals RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

2020 ◽  
Author(s):  
Stefan Holderbach ◽  
Lukas Adam ◽  
Bhyravabhotla Jayaram ◽  
Rebecca Wade ◽  
Goutam Mukherjee
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Protein Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Rapid Screening ◽  
Scoring Functions ◽  
Energy Prediction ◽  
Molecular Features ◽  
Physicochemical Descriptors

The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback that a large number of poses must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast prefiltering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance better than for the original RASPD method and comparable to traditional scoring functions on a range of different test sets without the need for generating ligand poses. Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.

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