Solution Phase DNA-Compatible Pictet-Spengler Reaction Aided by Machine Learning Building Block Filtering

2020 ◽  
Author(s):  
Ke Li ◽  
Xiaohong Liu ◽  
Sixiu Liu ◽  
Yulong An ◽  
Yanfang Shen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Building Block ◽  
Solution Phase

scholarly journals Solution-Phase DNA-Compatible Pictet-Spengler Reaction Aided by Machine Learning Building Block Filtering

iScience ◽  
2020 ◽  
Vol 23 (6) ◽  
pp. 101142 ◽  
Author(s):  
Ke Li ◽  
Xiaohong Liu ◽  
Sixiu Liu ◽  
Yulong An ◽  
Yanfang Shen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Building Block ◽  
Solution Phase

scholarly journals Total synthesis of TMG-chitotriomycin based on an automated electrochemical assembly of a disaccharide building block

2017 ◽  
Vol 13 ◽  
pp. 919-924 ◽  
Author(s):  
Yuta Isoda ◽  
Norihiko Sasaki ◽  
Kei Kitamura ◽  
Shuji Takahashi ◽  
Sujit Manmode ◽  
...  
Keyword(s):  
Total Synthesis ◽  
Building Block ◽  
Building Blocks ◽  
Solution Phase ◽  
Protecting Groups ◽  
Phase Synthesis ◽  
Solution Phase Synthesis

The total synthesis of TMG-chitotriomycin using an automated electrochemical synthesizer for the assembly of carbohydrate building blocks is demonstrated. We have successfully prepared a precursor of TMG-chitotriomycin, which is a structurally-pure tetrasaccharide with typical protecting groups, through the methodology of automated electrochemical solution-phase synthesis developed by us. The synthesis of structurally well-defined TMG-chitotriomycin has been accomplished in 10-steps from a disaccharide building block.

scholarly journals Combinatorial Optimization and Reasoning with Graph Neural Networks

2021 ◽  
Author(s):  
Quentin Cappart ◽  
Didier Chételat ◽  
Elias B. Khalil ◽  
Andrea Lodi ◽  
Christopher Morris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Combinatorial Optimization ◽  
Computer Science ◽  
Operations Research ◽  
Building Block ◽  
Related Data ◽  
Problem Instances ◽  
Graph Neural Networks

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have mostly focused on solving problem instances in isolation, ignoring the fact that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning, especially graph neural networks, as a key building block for combinatorial tasks, either directly as solvers or by enhancing the former. This paper presents a conceptual review of recent key advancements in this emerging field, aiming at researchers in both optimization and machine learning.

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions

2021 ◽  
Vol 17 (9) ◽  
pp. 5745-5758 ◽  
Author(s):  
Xiaoliang Pan ◽  
Junjie Yang ◽  
Richard Van ◽  
Evgeny Epifanovsky ◽  
Junming Ho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Energy Simulation ◽  
Solution Phase ◽  
Enzyme Reactions

scholarly journals Solution phase synthesis of short oligoribonucleotides on a precipitative tetrapodal support

2014 ◽  
Vol 10 ◽  
pp. 2279-2285 ◽  
Author(s):  
Alejandro Gimenez Molina ◽  
Amit M Jabgunde ◽  
Pasi Virta ◽  
Harri Lönnberg
Keyword(s):  
Building Block ◽  
Rna Synthesis ◽  
Building Blocks ◽  
Solution Phase ◽  
Dipolar Cycloaddition ◽  
Proof Of Concept ◽  
Phase Synthesis ◽  
Solution Phase Synthesis ◽  
High Yields

An effective method for the synthesis of short oligoribonucleotides in solution has been elaborated. Novel 2'-O-(2-cyanoethyl)-5'-O-(1-methoxy-1-methylethyl) protected ribonucleoside 3'-phosphoramidites have been prepared and their usefulness as building blocks in RNA synthesis on a soluble support has been demonstrated. As a proof of concept, a pentameric oligoribonucleotide, 3'-UUGCA-5', has been prepared on a precipitative tetrapodal tetrakis(4-azidomethylphenyl)pentaerythritol support. The 3'-terminal nucleoside was coupled to the support as a 3'-O-(4-pentynoyl) derivative by Cu(I) promoted 1,3-dipolar cycloaddition. Couplings were carried out with 1.5 equiv of the building block. In each coupling cycle, the small molecular reagents and byproducts were removed by two quantitative precipitations from MeOH, one after oxidation and the second after the 5'-deprotection. After completion of the chain assembly, treatment with triethylamine, ammonia and TBAF released the pentamer in high yields.

Solid-state and solution phase reactivity of 10-hydroxy-10,9-boroxophenanthrene: a model building block for self-assembly processes

2002 ◽  
Vol 26 (6) ◽  
pp. 701-710 ◽  
Author(s):  
Lyndsey M. Greig ◽  
Benson M. Kariuki ◽  
Scott Habershon ◽  
Neil Spencer ◽  
Roy L. Johnston ◽  
...  
Keyword(s):  
Solid State ◽  
Self Assembly ◽  
Building Block ◽  
Model Building ◽  
Solution Phase

scholarly journals Machine Learning Assisted Free Energy Simulation of Solution–Phase and Enzyme Reactions

2021 ◽  
Author(s):  
Xiaoliang Pan ◽  
junjie yang ◽  
Richard Van ◽  
Evgeny Epifanovsky ◽  
Junming Ho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Reaction Pathway ◽  
Molecular System ◽  
Computational Cost ◽  
Energy Simulation ◽  
Solution Phase ◽  
Chorismate Mutase ◽  
Enzymatic Reactions ◽  
Enzyme Reactions

Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort in developing stable and accurate MLPs for enzymatic reactions. Here, we report a protocol for performing machine learning assisted free energy simulation of solution-phase and enzyme reactions at an ab initio quantum mechanical and molecular mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy as well as forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (DMLP) is trained to reproduce the differences between ai-QM/MM and semiempirical (se) QM/MM energy and forces. To account for the effect of the condensed–phase environment in both MLP and DMLP, the DeePMD representation of a molecular system is extended to incorporate external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and DMLP reproduce the ai-QM/MM energy and forces with an error on average less than 1.0 kcal/mol and 1.0 kcal/mol/Å for representative configurations along the reaction pathway. For both reactions, MLP/DMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results, but only at a fractional computational cost.<br>

scholarly journals Machine Learning Assisted Free Energy Simulation of Solution–Phase and Enzyme Reactions

2021 ◽  
Author(s):  
Xiaoliang Pan ◽  
junjie yang ◽  
Richard Van ◽  
Evgeny Epifanovsky ◽  
Junming Ho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Reaction Pathway ◽  
Molecular System ◽  
Computational Cost ◽  
Energy Simulation ◽  
Solution Phase ◽  
Chorismate Mutase ◽  
Enzymatic Reactions ◽  
Enzyme Reactions

Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort in developing stable and accurate MLPs for enzymatic reactions. Here, we report a protocol for performing machine learning assisted free energy simulation of solution-phase and enzyme reactions at an ab initio quantum mechanical and molecular mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy as well as forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (DMLP) is trained to reproduce the differences between ai-QM/MM and semiempirical (se) QM/MM energy and forces. To account for the effect of the condensed–phase environment in both MLP and DMLP, the DeePMD representation of a molecular system is extended to incorporate external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and DMLP reproduce the ai-QM/MM energy and forces with an error on average less than 1.0 kcal/mol and 1.0 kcal/mol/Å for representative configurations along the reaction pathway. For both reactions, MLP/DMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results, but only at a fractional computational cost.<br>

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Transition alpha structure in beta-isomorphous Nb-Hf alloys

1987 ◽  
Vol 45 ◽  
pp. 232-233
Author(s):  
R.W. Carpenter ◽  
Changhai Li ◽  
David J. Smith
Keyword(s):  
Solid Solution ◽  
Solution Phase ◽  
Miscibility Gap ◽  
Large Strains ◽  
Solid Solution Phase ◽  
Two Phase ◽  
Diffuse Intensity ◽  
Metastable Form ◽  
The Matrix ◽  
Equilibrium Morphology

Binary Nb-Hf alloys exhibit a wide bcc solid solution phase field at temperatures above the Hfα→ß transition (2023K) and a two phase bcc+hcp field at lower temperatures. The β solvus exhibits a small slope above about 1500K, suggesting the possible existence of a miscibility gap. An earlier investigation showed that two morphological forms of precipitate occur during the bcc→hcp transformation. The equilibrium morphology is rod-type with axes along <113> bcc. The crystallographic habit of the rod precipitate follows the Burgers relations: {110}||{0001}, <112> || <1010>. The earlier metastable form, transition α, occurs as thin discs with {100} habit. The {100} discs induce large strains in the matrix. Selected area diffraction examination of regions ∼2 microns in diameter containing many disc precipitates showed that, a diffuse intensity distribution whose symmetry resembled the distribution of equilibrium α Bragg spots was associated with the disc precipitate.

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