| 1. |
- Chen, Jianhua, et al.
(författare)
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Highly stretchable organic electrochemical transistors with strain-resistant performance
- 2022
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Ingår i: Nature Materials. - : NATURE PORTFOLIO. - 1476-1122 .- 1476-4660. ; 21, s. 564-571
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Tidskriftsartikel (refereegranskat)abstract
- Realizing fully stretchable electronic materials is central to advancing new types of mechanically agile and skin-integrable optoelectronic device technologies. Here we demonstrate a materials design concept combining an organic semiconductor film with a honeycomb porous structure with biaxially prestretched platform that enables high-performance organic electrochemical transistors with a charge transport stability over 30-140% tensional strain, limited only by metal contact fatigue. The prestretched honeycomb semiconductor channel of donor-acceptor polymer poly(2,5-bis(2-octyldodecyl)-3,6-di(thiophen-2-yl)-2,5-diketo-pyrrolopyrrole-alt-2,5-bis(3-triethyleneglycoloxy-thiophen-2-yl) exhibits high ion uptake and completely stable electrochemical and mechanical properties over 1,500 redox cycles with 10(4) stretching cycles under 30% strain. Invariant electrocardiogram recording cycles and synapse responses under varying strains, along with mechanical finite element analysis, underscore that the present stretchable organic electrochemical transistor design strategy is suitable for diverse applications requiring stable signal output under deformation with low power dissipation and mechanical robustness. Highly stretchable organic electrochemical transistors with stable charge transport under severe tensional strains are demonstrated using a honeycomb semiconducting polymer morphology, thereby enabling controllable signal output for diverse stretchable bioelectronic applications.
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| 2. |
- Hering, Jenny, et al.
(författare)
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Exploring the Active Site of the Antibacterial Target MraY by Modified Tunicamycins.
- 2020
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Ingår i: ACS chemical biology. - : American Chemical Society (ACS). - 1554-8937 .- 1554-8929. ; 15:11, s. 2885-2895
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Tidskriftsartikel (refereegranskat)abstract
- The alarming growth of antibiotic resistance that is currently ongoing is a serious threat to human health. One of the most promising novel antibiotic targets is MraY (phospho-MurNAc-pentapeptide-transferase), an essential enzyme in bacterial cell wall synthesis. Through recent advances in biochemical research, there is now structural information available for MraY, and for its human homologue GPT (GlcNAc-1-P-transferase), that opens up exciting possibilities for structure-based drug design. The antibiotic compound tunicamycin is a natural product inhibitor of MraY that is also toxic to eukaryotes through its binding to GPT. In this work, we have used tunicamycin and modified versions of tunicamycin as tool compounds to explore the active site of MraY and to gain further insight into what determines inhibitor potency. We have investigated tunicamycin variants where the following motifs have been modified: the length and branching of the tunicamycin fatty acyl chain, the saturation of the fatty acyl chain, the 6″-hydroxyl group of the GlcNAc ring, and the ring structure of the uracil motif. The compounds are analyzed in terms of how potently they bind to MraY, inhibit the activity of the enzyme, and affect the protein thermal stability. Finally, we rationalize these results in the context of the protein structures of MraY and GPT.
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| 3. |
- Mercado, Rocio, 1992, et al.
(författare)
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Graph networks for molecular design
- 2021
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Ingår i: Machine Learning: Science and Technology. - : IOP Publishing. - 2632-2153. ; 2:2
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning methods applied to chemistry can be used to accelerate the discovery of new molecules. This work introduces GraphINVENT, a platform developed for graph-based molecular design using graph neural networks (GNNs). GraphINVENT uses a tiered deep neural network architecture to probabilistically generate new molecules a single bond at a time. All models implemented in GraphINVENT can quickly learn to build molecules resembling the training set molecules without any explicit programming of chemical rules. The models have been benchmarked using the MOSES distribution-based metrics, showing how GraphINVENT models compare well with state-of-the-art generative models. This work compares six different GNN-based generative models in GraphINVENT, and shows that ultimately the gated-graph neural network performs best against the metrics considered here.
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| 4. |
- Mercado, Rocio, 1992, et al.
(författare)
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Practical notes on building molecular graph generative models
- 2020
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Ingår i: Applied AI Letters. - : Wiley. - 2689-5595. ; 1:2
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Here are presented technical notes and tips on developing graph generative models for molecular design. Although this work stems from the development of GraphINVENT, a Python platform for iterative molecular generation using graph neural networks, this work is relevant to researchers studying other architectures for graph-based molecular design. In this work, technical details that could be of interest to researchers developing their own molecular generative models are discussed, including an overview of previous work in graph-based molecular design and strategies for designing new models. Advice on development and debugging tools which are helpful during code development is also provided. Finally, methods that were tested but which ultimately did not lead to promising results in the development of GraphINVENT are described here in the hope that this will help other researchers avoid pitfalls in development and instead focus their efforts on more promising strategies for graph-based molecular generation.
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