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In Silico Prediction of Oral Bioavailability
2007Preclinical predictive models of human oral bioavailability would be of considerable benefit to pharmaceutical product development. Identification of potentially orally bioavailable compounds may assist in candidate selection, or compounds with poor projected bioavailability may be identified early, saving potential late failure and, thus ...
Turner, Joseph +1 more
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In silico Prediction of Chemical Ames Mutagenicity
Journal of Chemical Information and Modeling, 2012Mutagenicity is one of the most important end points of toxicity. Due to high cost and laboriousness in experimental tests, it is necessary to develop robust in silico methods to predict chemical mutagenicity. In this paper, a comprehensive database containing 7617 diverse compounds, including 4252 mutagens and 3365 nonmutagens, was constructed. On the
Congying Xu +7 more
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In Silico Prediction for ncRNAs in Prokaryotes
2021The identification and characterization of non-coding RNAs (ncRNAs) in prokaryotes is an important step in the study of the interaction of these molecules with mRNAs-or target proteins, in the post-transcriptional regulation process. Here, we describe one of the main in silico prediction methods in prokaryotes, using the TargetRNA2 tool to predict ...
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In Silico Prediction of Drug Properties
Current Medicinal Chemistry, 2009Drug design has become inconceivable without the assistance of computer-aided methods. In this context in silico was chosen as designation to emphasize the relationship to in vitro and in vivo testing. Nowadays, virtual screening covers much more than estimation of solubility and oral bioavailability of compounds.
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In Silico Prediction of Binding Sites on Proteins
Current Medicinal Chemistry, 2010The majority of biological processes involve the association of proteins or binding of other ligands to proteins. The accurate prediction of putative binding sites on the protein surface can be very helpful for rational drug design on target proteins of medical relevance, for predicting the geometry of protein-protein as well as protein-ligand ...
Simon, Leis +2 more
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In silico prediction of acyl glucuronide reactivity
Journal of Computer-Aided Molecular Design, 2011Drugs and drug candidates containing a carboxylic acid moiety, including many widely used non-steroidal anti-inflammatory drugs (NSAIDs) are often metabolized to form acyl glucuronides (AGs). NSAIDs such as Ibuprofen are amongst the most widely used drugs on the market, whereas similar carboxylic acid drugs such as Suprofen have been withdrawn due to ...
Tim Potter +8 more
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In Silico Prediction of RNA Secondary Structure
2017The secondary structure of an RNA molecule represents the base-pairing interactions within the molecule and fundamentally determines its overall structure. In this chapter, we overview the main approaches and existing tools for predicting RNA secondary structures, as well as methods for identifying noncoding RNAs from genomic sequences or RNA ...
Tahi, Fariza +2 more
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In silico prediction of drug toxicity
Journal of Computer-Aided Molecular Design, 2003It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative ...
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In Silico Prediction of Tumor Neoantigens with TIminer
2020Tumor neoantigens are at the core of immunological tumor control and response to immunotherapy. In silico prediction of tumor neoantigens from next-generation sequencing (NGS) data is possible but requires the assembly of complex, multistep computational pipelines and extensive data preprocessing.
Alexander, Kirchmair +1 more
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Survey of In-silico Prediction of Anticancer Peptides
Current Topics in Medicinal Chemistry, 2021Cancer is one of the major causes of death in human beings. While traditional cancer treatments kill cancerous cells, they negatively affect normal cells. In addition, the side effects and high medical costs of treatment prevent effective management of cancer.
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