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.

scholarly journals Accelerating organic solar cell material's discovery: high-throughput screening and big data

2021 ◽  
Author(s):  
Xabier Rodríguez-Martínez ◽  
Enrique Pascual-San-José ◽  
Mariano Campoy-Quiles
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Organic Solar Cell ◽  
State Of The Art ◽  
Review Article ◽  
Machine Learning Algorithms ◽  
Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

scholarly journals High-throughput computational-experimental screening protocol for the discovery of bimetallic catalysts

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Byung Chul Yeo ◽  
Hyunji Nam ◽  
Hyobin Nam ◽  
Min-Cheol Kim ◽  
Hong Woo Lee ◽  
...  
Keyword(s):  
High Throughput ◽  
Bimetallic Catalysts ◽  
High Throughput Screening ◽  
Direct Synthesis ◽  
Catalytic Performance ◽  
Platinum Group Metals ◽  
Pt Catalyst ◽  
Electronic Density ◽  
Bimetallic Alloy ◽  
Screening Protocol

AbstractTo accelerate the discovery of materials through computations and experiments, a well-established protocol closely bridging these methods is required. We introduce a high-throughput screening protocol for the discovery of bimetallic catalysts that replace palladium (Pd), where the similarities in the electronic density of states patterns were employed as a screening descriptor. Using first-principles calculations, we screened 4350 bimetallic alloy structures and proposed eight candidates expected to have catalytic performance comparable to that of Pd. Our experiments demonstrate that four bimetallic catalysts indeed exhibit catalytic properties comparable to those of Pd. Moreover, we discover a bimetallic (Ni-Pt) catalyst that has not yet been reported for H2O2 direct synthesis. In particular, Ni61Pt39 outperforms the prototypical Pd catalyst for the chemical reaction and exhibits a 9.5-fold enhancement in cost-normalized productivity. This protocol provides an opportunity for the catalyst discovery for the replacement or reduction in the use of the platinum-group metals.

scholarly journals A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

2019 ◽  
Vol 10 (36) ◽  
pp. 8374-8383 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Aditya Sonpal ◽  
Mojtaba Haghighatlari ◽  
Andrew J. Schultz ◽  
Johannes Hachmann
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Refractive Index ◽  
High Throughput ◽  
Neural Network Model ◽  
High Throughput Screening ◽  
Deep Neural Network ◽  
Organic Molecules ◽  
High Refractive Index ◽  
Computational Pipeline

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.

scholarly journals Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

2021 ◽  
Author(s):  
Jeremy Feinstein ◽  
ganesh sivaraman ◽  
Kurt Picel ◽  
Brian Peters ◽  
Alvaro Vazquez-Mayagoitia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
High Throughput ◽  
High Throughput Screening ◽  
Organic Chemical ◽  
Learning Method ◽  
Toxicity Data ◽  
Chemical Structures ◽  
Machine Learning Methods ◽  
Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

scholarly journals Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes

2020 ◽  
Author(s):  
Xinzhe Zhu ◽  
Chi-Hung Ho ◽  
Xiaonan Wang
Keyword(s):  
Machine Learning ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Production Process ◽  
Environmental Impacts ◽  
High Throughput ◽  
High Throughput Screening ◽  
Molecular Descriptors ◽  
Trifluoroacetic Anhydride ◽  
Chemical Materials

<p><a></a><a>The production process of many active pharmaceutical ingredients such as sitagliptin could cause severe environmental problems due to the use of toxic chemical materials and production infrastructure, energy consumption and wastes treatment. The environmental impacts of sitagliptin production process were estimated with life cycle assessment (LCA) method, which suggested that the use of chemical materials provided the major environmental impacts. Both methods of Eco-indicator 99 and ReCiPe endpoints confirmed that chemical feedstock accounted 83% and 70% of life-cycle impact, respectively. Among all the chemical materials used in the sitagliptin production process, </a><a>trifluoroacetic anhydride </a>was identified as the largest influential factor in most impact categories according to the results of ReCiPe midpoints method. Therefore, high-throughput screening was performed to seek for green chemical substitutes to replace the target chemical (i.e. trifluoroacetic anhydride) by the following three steps. Firstly, thirty most similar chemicals were obtained from two million candidate alternatives in PubChem database based on their molecular descriptors. Thereafter, deep learning neural network models were developed to predict life-cycle impact according to the chemicals in Ecoinvent v3.5 database with known LCA values and corresponding molecular descriptors. Finally, 1,2-ethanediyl ester was proved to be one of the potential greener substitutes after the LCA data of these similar chemicals were predicted using the well-trained machine learning models. The case study demonstrated the applicability of the novel framework to screen green chemical substitutes and optimize the pharmaceutical manufacturing process.</p>

Development of a dual-fluorescence reporter system for high-throughput screening of L-aspartate-α-decarboxylase

2020 ◽  
Vol 52 (12) ◽  
pp. 1420-1426
Author(s):  
Mingyue Fei ◽  
Xudan Mao ◽  
Yiyang Chen ◽  
Yalan Lu ◽  
Lin Wang ◽  
...  
Keyword(s):  
High Throughput ◽  
Industrial Production ◽  
High Throughput Screening ◽  
Relative Activity ◽  
Expression Level ◽  
Rapid Screening ◽  
Dual Fluorescence ◽  
Reporter System ◽  
The Cost ◽  
Chemical Synthesis Route

Abstract β-Alanine (3-aminopropionic acid) holds great potential in industrial application. It can be obtained through a chemical synthesis route, which is hazardous to the environment. It is well known that l-aspartate-α-decarboxylase (ADC) can convert l-aspartate to β-alanine in bacteria. However, due to the low activity of ADC, industrial production of β-alanine through the green biological route remains unclear. Thus, improving the activity of ADC is critical to reduce the cost of β-alanine production. In this study, we established a dual-fluorescence high-throughput system for efficient ADC screening. By measuring the amount of β-alanine and the expression level of ADC using two different fluorescence markers, we can rapidly quantify the relative activity of ADC variants. From a mutagenesis library containing 2000 ADC variants, we obtained a mutant with 33% increased activity. Further analysis revealed that mutations of K43R and P103Q in ADC significantly improved the yield of β-alanine produced by the whole-cell biocatalysis. Compared with the previous single-fluorescence method, our system can not only quantify the amount of β-alanine but also measure the expression level of ADC with different fluorescence, making it able to effectively screen out ADC variants with improved relative activity. The dual-fluorescence high-throughput system for rapid screening of ADC provides a good strategy for industrial production of β-alanine via the biological conversion route in the future.

scholarly journals Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries

RSC Advances ◽  
2018 ◽  
Vol 8 (69) ◽  
pp. 39414-39420 ◽  
Author(s):  
Omar Allam ◽  
Byung Woo Cho ◽  
Ki Chul Kim ◽  
Seung Soon Jang
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
High Throughput Screening ◽  
Density Functional ◽  
Electrode Materials ◽  
Screening Method ◽  
Li Ion Batteries ◽  
Functional Theory ◽  
Learning Framework ◽  
Li Ion

In this study, we utilize a density functional theory-machine learning framework to develop a high-throughput screening method for designing new molecular electrode materials.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer

2015 ◽  
Vol 11 (12) ◽  
pp. 3362-3377 ◽  
Author(s):  
Vinay Randhawa ◽  
Anil Kumar Singh ◽  
Vishal Acharya
Keyword(s):  
Machine Learning ◽  
Oral Cancer ◽  
High Throughput ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Systematic Approach ◽  
Molecular Descriptor ◽  
Classification Model

Network-based and cheminformatics approaches identify novel lead molecules forCXCR4, a key gene prioritized in oral cancer.

scholarly journals Recovery of NanoLuc Luciferase-Tagged Canine Distemper Virus for Facilitating Rapid Screening of Antivirals in vitro

2020 ◽  
Vol 7 ◽  
Author(s):  
Fuxiao Liu ◽  
Qianqian Wang ◽  
Yilan Huang ◽  
Ning Wang ◽  
Youming Zhang ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Canine Distemper Virus ◽  
Reverse Genetics ◽  
Rapid Screening ◽  
Canine Distemper ◽  
Virus Propagation ◽  
The Family ◽  
Conventional Methods

Canine distemper virus (CDV), belonging to the genus Morbillivirus in the family Paramyxoviridae, is a highly contagious pathogen, affecting various domestic, and wild carnivores. Conventional methods are too cumbersome to be used for high-throughput screening of anti-CDV drugs. In this study, a recombinant CDV was rescued using reverse genetics for facilitating screening of anti-CDV drug in vitro. The recombinant CDV could stably express the NanoLuc® luciferase (NLuc), a novel enzyme that was smaller and “brighter” than others. The intensity of NLuc-catalyzed luminescence reaction indirectly reflected the anti-CDV effect of a certain drug, due to a positive correlation between NLuc expression and virus propagation in vitro. Based on such a characteristic feature, the recombinant CDV was used for anti-CDV assays on four drugs (ribavirin, moroxydine hydrochloride, 1-adamantylamine hydrochloride, and tea polyphenol) via analysis of luciferase activity, instead of via conventional methods. The result showed that out of these four drugs, only the ribavirin exhibited a detectable anti-CDV effect. The NLuc-tagged CDV would be a rapid tool for high-throughput screening of anti-CDV drugs.

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