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https://www.readbyqxmd.com/read/27805045/dpdr-cpi-a-server-that-predicts-drug-positioning-and-drug-repositioning-via-chemical-protein-interactome
#1
Heng Luo, Ping Zhang, Xi Hang Cao, Dizheng Du, Hao Ye, Hui Huang, Can Li, Shengying Qin, Chunling Wan, Leming Shi, Lin He, Lun Yang
The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational "drug candidate positioning" or "drug positioning", to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value...
November 2, 2016: Scientific Reports
https://www.readbyqxmd.com/read/25580936/interfacial-strain-induced-structural-and-polarization-evolutions-in-epitaxial-multiferroic-bifeo3-001-thin-films
#2
Haizhong Guo, Ruiqiang Zhao, Kui-Juan Jin, Lin Gu, Dongdong Xiao, Zhenzhong Yang, Xiaolong Li, Le Wang, Xu He, Junxing Gu, Qian Wan, Can Wang, Huibin Lu, Chen Ge, Meng He, Guozhen Yang
Varying the film thickness is a precise route to tune the interfacial strain to manipulate the properties of the multiferroic materials. Here, to explore the effects of the interfacial strain on the properties of the multiferroic BiFeO3 films, we investigated thickness-dependent structural and polarization evolutions of the BiFeO3 films. The epitaxial growth with an atomic stacking sequence of BiO/TiO2 at the interface was confirmed by scanning transmission electron microscopy. Combining X-ray diffraction experiments and first-principles calculations, a thickness-dependent structural evolution was observed from a fully strained tetragonality to a partially relaxed one without any structural phase transition or rotated twins...
February 4, 2015: ACS Applied Materials & Interfaces
https://www.readbyqxmd.com/read/24875476/ddi-cpi-a-server-that-predicts-drug-drug-interactions-through-implementing-the-chemical-protein-interactome
#3
Heng Luo, Ping Zhang, Hui Huang, Jialiang Huang, Emily Kao, Leming Shi, Lin He, Lun Yang
Drug-drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug-human protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model...
July 2014: Nucleic Acids Research
https://www.readbyqxmd.com/read/21558322/drar-cpi-a-server-for-identifying-drug-repositioning-potential-and-adverse-drug-reactions-via-the-chemical-protein-interactome
#4
Heng Luo, Jian Chen, Leming Shi, Mike Mikailov, Huang Zhu, Kejian Wang, Lin He, Lun Yang
Identifying new indications for existing drugs (drug repositioning) is an efficient way of maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death among hospitalized patients. As both new indications and ADRs are caused by unexpected chemical-protein interactions on off-targets, it is reasonable to predict these interactions by mining the chemical-protein interactome (CPI). Making such predictions has recently been facilitated by a web server named DRAR-CPI. This server has a representative collection of drug molecules and targetable human proteins built up from our work in drug repositioning and ADR...
July 2011: Nucleic Acids Research
https://www.readbyqxmd.com/read/19417066/sepresa-a-server-for-the-prediction-of-populations-susceptible-to-serious-adverse-drug-reactions-implementing-the-methodology-of-a-chemical-protein-interactome
#5
Lun Yang, Heng Luo, Jian Chen, Qinghe Xing, Lin He
Serious adverse drug reactions (SADRs) are caused by unexpected drug-human protein interactions, and some polymorphisms within binding pockets make the population carrying these polymorphisms susceptible to SADR. Predicting which populations are likely to be susceptible to SADR will not only strengthen drug safety, but will also assist enterprises to adjust R&D and marketing strategies. Making such predictions has recently been facilitated by the introduction of a web server named SePreSA. The server has a comprehensive collection of the structural models of nearly all the well known SADR targets...
July 2009: Nucleic Acids Research
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