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| 2. |
- 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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| 3. |
- Chen, Hongming, et al.
(författare)
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In silico prediction of unbound brain-to-plasma concentration ratio using machine learning algorithms
- 2011
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Ingår i: Journal of Molecular Graphics and Modelling. - : Elsevier BV. - 1093-3263 .- 1873-4243. ; 29:8, s. 985-995
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Tidskriftsartikel (refereegranskat)abstract
- Distribution over the blood-brain barrier (BBB) is an important parameter to consider for compounds that will be synthesized in a drug discovery project. Drugs that aim at targets in the central nervous system (CNS) must pass the BBB. In contrast, drugs that act peripherally are often optimised to minimize the risk of CNS side effects by restricting their potential to reach the brain. Historically, most prediction methods have focused on the total compound distribution between the blood plasma and the brain. However, recently it has been proposed that the unbound brain-to-plasma concentration ratio (K(p,uu,brain)) is more relevant. In the current study, quantitative K(p,uu,brain) prediction models have been built on a set of 173 in-house compounds by using various machine learning algorithms. The best model was shown to be reasonably predictive for the test set of 73 compounds (R(2) = 0.58). When used for qualitative prediction the model shows an accuracy of 0.85 (Kappa = 0.68). An additional external test set containing 111 marketed CNS active drugs was also classified with the model and 89% of these drugs were correctly predicted as having high brain exposure.
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| 4. |
- Hakulinen, Jonna K, et al.
(författare)
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MraY-antibiotic complex reveals details of tunicamycin mode of action.
- 2017
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Ingår i: Nature chemical biology. - : Springer Science and Business Media LLC. - 1552-4469 .- 1552-4450. ; 13:3, s. 265-267
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Tidskriftsartikel (refereegranskat)abstract
- The rapid increase of antibiotic resistance has created an urgent need to develop novel antimicrobial agents. Here we describe the crystal structure of the promising bacterial target phospho-N-acetylmuramoyl-pentapeptide translocase (MraY) in complex with the nucleoside antibiotic tunicamycin. The structure not only reveals the mode of action of several related natural-product antibiotics but also gives an indication on the binding mode of the MraY UDP-MurNAc-pentapeptide and undecaprenyl-phosphate substrates.
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| 5. |
- Hansson, Mari, et al.
(författare)
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On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors
- 2014
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Ingår i: Journal of Biomolecular Screening. - : Elsevier BV. - 1087-0571 .- 1552-454X. ; 19:5, s. 727-737
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Tidskriftsartikel (refereegranskat)abstract
- W High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent in-house HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp(3)-hybridized carbons (Fsp3), and fraction of molecular framework (f(MF)). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened. Beta-binomial statistical models were built to model the molecular hit rate as a function of these descriptors. The advantage of the beta-binomial statistical models is that the correlation between the descriptors is taken into account. Higher degree polynomial terms of the descriptors were also added into the beta-binomial statistic model to improve the model quality. The relative influence of different molecular descriptors on molecular hit rate has been estimated, taking into account that the descriptors are correlated to each other through applying beta-binomial statistical modeling. The results show that ClogP has the largest influence on the molecular hit rate, followed by Fsp3 and HEV. f(MF) has only a minor influence besides its correlation with the other molecular descriptors.
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| 6. |
- 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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| 7. |
- Mao, Junjie, et al.
(författare)
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A Web of Things Based Device-adaptive Service Composition Framework
- 2016
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Ingår i: 2016 IEEE 13th International Conference on e-Business Engineering (ICEBE). - : IEEE. - 9781509061198 - 9781509061204 ; , s. 40-47
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Konferensbidrag (refereegranskat)abstract
- In WoT environment, smart things provide RESTful services to expose their resources and operations. There are a large number of smart things that offer the same functionalities but have different service interfaces. Because of the high coupling between device service instances and process specifications like BPEL, the cost of reusing a BPEL specification between different device environments could be extremely high. We propose a device-adaptive service composition framework for WoT environment, in order to help users to apply the business process and service composition technologies more conveniently. In the framework, we design an activity description model, which is a semantic description for business activities, to overcome the shortcoming of directly binding the process and the service. Then, a matching mechanism between the model and the WADL of device services is proposed to select candidate services for the composition. Furthermore, we represent the matching result in a logical composition model, with which the source code of a general service can be automatically generated. The general service is a unified encapsulation for device services that match the functionalities of business activity. So user can interact with the general service instead of the origin services on the device, which decouples the process specification and the actual device services. A case study is offered to illustrate how to apply our framework in an intelligent charging pile sharing platform.
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| 8. |
- 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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| 9. |
- 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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| 10. |
- Shevtsov, Oleksii, 1988, et al.
(författare)
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A de novo molecular generation method using latent vector based generative adversarial network
- 2019
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Ingår i: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946 .- 1758-2946. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: One to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases. Sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.[Figure not available: See fulltext.]
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