Identification of a novel aminopolycarboxylic acid siderophore gene cluster encoding the biosynthesis of ethylenediaminesuccinic acid hydroxyarginine (EDHA)

Metallomics ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 722-734 ◽  
Author(s):  
Marius Spohn ◽  
Simone Edenhart ◽  
Mohammad Alanjary ◽  
Nadine Ziemert ◽  
Daniel Wibberg ◽  
...  
Keyword(s):  
Gene Cluster ◽  
Computational Screening ◽  
Screening Approach ◽  
Aminopolycarboxylic Acid

A computational screening approach enabled the detection of a novel aminopolycarboxylic acid gene cluster that encodes the biosynthesis of EDHA.

Hybrid Screening Approach for Very Small Fragments: X-ray and Computational Screening on FKBP51

2020 ◽  
Vol 63 (11) ◽  
pp. 5856-5864
Author(s):  
Sebastian W. Draxler ◽  
Margit Bauer ◽  
Christian Eickmeier ◽  
Simon Nadal ◽  
Herbert Nar ◽  
...  
Keyword(s):  
X Ray ◽  
Computational Screening ◽  
Screening Approach ◽  
Hybrid Screening

Discovery of new $${\varvec{Mycobacterium~tuberculosis}}$$ M y c o b a c t e r i u m t u b e r c u l o s i s proteasome inhibitors using a knowledge-based computational screening approach

2015 ◽  
Vol 19 (4) ◽  
pp. 1003-1019 ◽  
Author(s):  
Rukmankesh Mehra ◽  
Reena Chib ◽  
Gurunadham Munagala ◽  
Kushalava Reddy Yempalla ◽  
Inshad Ali Khan ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Proteasome Inhibitors ◽  
Knowledge Based ◽  
Computational Screening ◽  
Screening Approach

scholarly journals Erratum to: Discovery of new Mycobacterium tuberculosis proteasome inhibitors using a knowledge-based computational screening approach

2015 ◽  
Vol 20 (1) ◽  
pp. 367-367 ◽  
Author(s):  
Rukmankesh Mehra ◽  
Reena Chib ◽  
Gurunadham Munagala ◽  
Kushalava Reddy Yempalla ◽  
Inshad Ali Khan ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Proteasome Inhibitors ◽  
Knowledge Based ◽  
Computational Screening ◽  
Screening Approach

scholarly journals A High-Throughput Screening Approach for the Optoelectronic Properties of Conjugated Polymers

2018 ◽  
Author(s):  
Liam Wilbraham ◽  
Enrico Berardo ◽  
Lukas Turcani ◽  
Kim Jelfs ◽  
Martijn Zwijnenburg
Keyword(s):  
Conjugated Polymers ◽  
Electronic Properties ◽  
High Throughput ◽  
High Throughput Screening ◽  
Density Functional ◽  
Computational Cost ◽  
Quality Data ◽  
Minor Variation ◽  
Computational Screening ◽  
Screening Approach

<p>We propose a general high-throughput computational screening approach for the optical and electronic properties of conjugated polymers. This approach makes use of the recently developed xTB family of low-computational-cost density functional tight-binding methods from Grimme and co-workers, calibrated here to (TD-)DFT data computed for a representative diverse set of (co-)polymers. Parameters drawn from the resulting calibration using a linear model can then be applied to the xTB derived results for new polymers, thus generating near DFT-quality data with orders of magnitude reduction in computational cost. As a result, after an initial computational investment for calibration, this approach can be used to quickly and accurately screen on the order of thousands of polymers for target applications. We also demonstrate that the (opto)electronic properties of the conjugated polymers show only a very minor variation when considering different conformers and that the results of high-throughput screening are therefore expected to be relatively insensitive with respect to the conformer search methodology applied.</p>

scholarly journals Discovery of Record-Breaking Metal-Organic Frameworks for Methane Storage using Evolutionary Algorithm and Machine Learning

2020 ◽  
Author(s):  
Sangwon Lee ◽  
Baekjun Kim ◽  
Jihan Kim
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Metal Organic Frameworks ◽  
Working Capacity ◽  
Computational Screening ◽  
Screening Approach ◽  
Systematic Strategy ◽  
Metal Organic ◽  
New Type ◽  
Modular Nature

In the past decade, there has been a rise in a number of computational screening works to facilitate finding optimal metal-organic frameworks (MOF) for variety of different applications. Unfortunately, most of these screening works are limited to its initial set of materials and result in brute-force type of a screening approach. In this work, we present a systematic strategy that can find materials with desired property from an extremely diverse and large MOF set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm<sup>3</sup> cm<sup>−3</sup> and 96 MOFs with methane working capacity over 208 cm<sup>3</sup> cm<sup>−3</sup>, which is the current world record. We believe that this methodology can facilitate a new type of a screening approach that takes advantage of the modular nature in MOFs, and can readily be extended to other important applications as well.

scholarly journals Discovery of Record-Breaking Metal-Organic Frameworks for Methane Storage using Evolutionary Algorithm and Machine Learning

2020 ◽  
Author(s):  
Sangwon Lee ◽  
Baekjun Kim ◽  
Jihan Kim
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Metal Organic Frameworks ◽  
Working Capacity ◽  
Computational Screening ◽  
Screening Approach ◽  
Systematic Strategy ◽  
Metal Organic ◽  
New Type ◽  
Modular Nature

In the past decade, there has been a rise in a number of computational screening works to facilitate finding optimal metal-organic frameworks (MOF) for variety of different applications. Unfortunately, most of these screening works are limited to its initial set of materials and result in brute-force type of a screening approach. In this work, we present a systematic strategy that can find materials with desired property from an extremely diverse and large MOF set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm<sup>3</sup> cm<sup>−3</sup> and 96 MOFs with methane working capacity over 208 cm<sup>3</sup> cm<sup>−3</sup>, which is the current world record. We believe that this methodology can facilitate a new type of a screening approach that takes advantage of the modular nature in MOFs, and can readily be extended to other important applications as well.

Two-Dimensional Metal Dichalcogenides and Oxides for Hydrogen Evolution: A Computational Screening Approach

2015 ◽  
Vol 6 (9) ◽  
pp. 1577-1585 ◽  
Author(s):  
Mohnish Pandey ◽  
Aleksandra Vojvodic ◽  
Kristian S. Thygesen ◽  
Karsten W. Jacobsen
Keyword(s):  
Hydrogen Evolution ◽  
Two Dimensional ◽  
Computational Screening ◽  
Screening Approach ◽  
Metal Dichalcogenides

Experimental Validation of a Computational Screening Approach to Predict Redox Potentials for a Diverse Variety of Redox-Active Organic Molecules

2020 ◽  
Vol 124 (44) ◽  
pp. 24105-24114
Author(s):  
Alexandra R. McNeill ◽  
Samantha E. Bodman ◽  
Amy M. Burney ◽  
Chris D. Hughes ◽  
Deborah L. Crittenden
Keyword(s):  
Experimental Validation ◽  
Organic Molecules ◽  
Redox Potentials ◽  
Computational Screening ◽  
Screening Approach ◽  
Redox Active

Correction to “Two-Dimensional Metal Dichalcogenides and Oxides for Hydrogen Evolution: A Computational Screening Approach”

2015 ◽  
Vol 6 (14) ◽  
pp. 2669-2670 ◽  
Author(s):  
Mohnish Pandey ◽  
Aleksandra Vojvodic ◽  
Kristian S. Thygesen ◽  
Karsten W. Jacobsen
Keyword(s):  
Hydrogen Evolution ◽  
Two Dimensional ◽  
Computational Screening ◽  
Screening Approach ◽  
Metal Dichalcogenides
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