scholarly journals Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

2018 ◽  
Vol 4 (4) ◽  
pp. eaaq1566 ◽  
Author(s):  
Fang Ren ◽  
Logan Ward ◽  
Travis Williams ◽  
Kevin J. Laws ◽  
Christopher Wolverton ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Metallic Glasses ◽  
High Throughput Experiments

scholarly journals On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Aaron Gilad Kusne ◽  
Tieren Gao ◽  
Apurva Mehta ◽  
Liqin Ke ◽  
Manh Cuong Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rare Earth ◽  
High Throughput ◽  
Permanent Magnets ◽  
High Throughput Experiments

scholarly journals COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning

2020 ◽  
Author(s):  
◽  
Hagit Achdout ◽  
Anthony Aimon ◽  
Elad Bar-David ◽  
Haim Barr ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Open Science ◽  
Viral Inhibition ◽  
Enzymatic Assays ◽  
Main Protease ◽  
Covalent Inhibitors ◽  
Ic50 Values ◽  
High Throughput Experiments ◽  
High Throughput Crystallography

AbstractHerein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.

scholarly journals COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning

2020 ◽  
Author(s):  
The COVID Moonshot Consortium ◽  
John Chodera ◽  
Alpha Lee ◽  
Nir London ◽  
Frank von Delft
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Open Science ◽  
Viral Inhibition ◽  
Enzymatic Assays ◽  
Main Protease ◽  
Covalent Inhibitors ◽  
Ic50 Values ◽  
High Throughput Experiments ◽  
High Throughput Crystallography

<div><div><div><p>Herein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.<br></p></div></div></div>

scholarly journals Machine learning analysis identifies Drosophila Grunge/Atrophin as an important learning and memory gene required for memory retention and social learning

2017 ◽  
Author(s):  
Balint Z Kacsoh ◽  
Casey S. Greene ◽  
Giovanni Bosco
Keyword(s):  
Machine Learning ◽  
Learning And Memory ◽  
High Throughput ◽  
Mushroom Body ◽  
Neuronal Development ◽  
Active Role ◽  
Long Term Memory ◽  
Memory Retention ◽  
Genome Wide Data ◽  
High Throughput Experiments

ABSTRACTHigh throughput experiments are becoming increasingly common, and scientists must balance hypothesis driven experiments with genome wide data acquisition. We sought to predict novel genes involved in Drosophila learning and long-term memory from existing public high-throughput data. We performed an analysis using PILGRM, which analyzes public gene expression compendia using machine learning. We evaluated the top prediction alongside genes involved in learning and memory in IMP, an interface for functional relationship networks. We identified Grunge/Atrophin (Gug/Atro), a transcriptional repressor, histone deacetylase, as our top candidate. We find, through multiple, distinct assays, that Gug has an active role as a modulator of memory retention in the fly and its function is required in the adult mushroom body. Depletion of Gug specifically in neurons of the adult mushroom body, after cell division and neuronal development is complete, suggests that Gug function is important for memory retention through regulation of neuronal activity, and not by altering neurodevelopment. Our study provides a previously uncharacterized role for Gug as a possible regulator of neuronal plasticity at the interface of memory retention and memory extinction.

scholarly journals Phase prediction of Ni-base superalloys via high-throughput experiments and machine learning

2020 ◽  
Vol 9 (1) ◽  
pp. 32-40
Author(s):  
Zijun Qin ◽  
Zi Wang ◽  
Yunqiang Wang ◽  
Lina Zhang ◽  
Weifu Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Phase Prediction ◽  
High Throughput Experiments

scholarly journals Discovering exceptionally hard and wear-resistant metallic glasses by combining machine-learning with high throughput experimentation

2022 ◽  
Vol 9 (1) ◽  
pp. 011403
Author(s):  
Suchismita Sarker ◽  
Robert Tang-Kong ◽  
Rachel Schoeppner ◽  
Logan Ward ◽  
Naila Al Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Metallic Glasses ◽  
Wear Resistant ◽  
High Throughput Experimentation

scholarly journals A titanium alloys design method based on high-throughput experiments and machine learning

2021 ◽  
Vol 11 ◽  
pp. 2336-2353
Author(s):  
Chengpeng Zhu ◽  
Chao Li ◽  
Di Wu ◽  
Wan Ye ◽  
Shuangxi Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Titanium Alloys ◽  
High Throughput ◽  
Design Method ◽  
High Throughput Experiments

scholarly journals COVID Moonshot: Open Science Discovery of SARS-CoV-2 Main Protease Inhibitors by Combining Crowdsourcing, High-Throughput Experiments, Computational Simulations, and Machine Learning

2020 ◽  
Author(s):  
The COVID Moonshot Consortium ◽  
John Chodera ◽  
Alpha Lee ◽  
Nir London ◽  
Frank von Delft
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Open Science ◽  
Viral Inhibition ◽  
Enzymatic Assays ◽  
Main Protease ◽  
Covalent Inhibitors ◽  
Ic50 Values ◽  
High Throughput Experiments ◽  
High Throughput Crystallography

<div><div><div><p>Herein we provide a living summary of the data generated during the COVID Moonshot project focused on the development of SARS-CoV-2 main protease (Mpro) inhibitors. Our approach uniquely combines crowdsourced medicinal chemistry insights with high throughput crystallography, exascale computational chemistry infrastructure for simulations, and machine learning in triaging designs and predicting synthetic routes. This manuscript describes our methodologies leading to both covalent and non-covalent inhibitors displaying protease IC50 values under 150 nM and viral inhibition under 5 uM in multiple different viral replication assays. Furthermore, we provide over 200 crystal structures of fragment-like and lead-like molecules in complex with the main protease. Over 1000 synthesized and ordered compounds are also reported with the corresponding activity in Mpro enzymatic assays using two different experimental setups. The data referenced in this document will be continually updated to reflect the current experimental progress of the COVID Moonshot project, and serves as a citable reference for ensuing publications. All of the generated data is open to other researchers who may find it of use.<br></p></div></div></div>

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