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33
SwissTargetPrediction: a web server for target prediction of bioactive small molecules
- Nucleic Acids Res
, 2014
"... Bioactive small molecules, such as drugs or metabo-lites, bind to proteins or other macro-molecular tar-gets to modulate their activity, which in turn results in the observed phenotypic effects. For this reason, mapping the targets of bioactive small molecules is a key step toward unraveling the mol ..."
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Bioactive small molecules, such as drugs or metabo-lites, bind to proteins or other macro-molecular tar-gets to modulate their activity, which in turn results in the observed phenotypic effects. For this reason, mapping the targets of bioactive small molecules is a key step toward unraveling the molecular mecha-nisms underlying their bioactivity and predicting po-tential side effects or cross-reactivity. Recently, large datasets of protein–small molecule interactions have become available, providing a unique source of in-formation for the development of knowledge-based approaches to computationally identify new targets for uncharacterized molecules or secondary targets for known molecules. Here, we introduce SwissTar-getPrediction, a web server to accurately predict the targets of bioactive molecules based on a combina-tion of 2D and 3D similarity measures with known ligands. Predictions can be carried out in five differ-ent organisms, and mapping predictions by homol-ogy within and between different species is enabled for close paralogs and orthologs. SwissTargetPre-diction is accessible free of charge and without login requirement at
Communication MyChEMBL: A Virtual Platform for Distributing Cheminformatics Tools and Open Data
, 2014
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Helminth.net: expansions to Nematode.net and an introduction to Trematode.net. Nucleic Acids Res 43: D698–D706
, 2015
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WITHDRAWN––a resource for withdrawn and discontinued drugs
, 2015
"... Post-marketing drug withdrawals can be associated with various events, ranging from safety issues such as reported deaths or severe side-effects, to a mul-titude of non-safety problems including lack of effi-cacy, manufacturing, regulatory or business issues. During the last century, the majority of ..."
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Post-marketing drug withdrawals can be associated with various events, ranging from safety issues such as reported deaths or severe side-effects, to a mul-titude of non-safety problems including lack of effi-cacy, manufacturing, regulatory or business issues. During the last century, the majority of drugs vol-untarily withdrawn from the market or prohibited by regulatory agencies was reported to be related to adverse drug reactions. Understanding the under-lying mechanisms of toxicity is of utmost impor-tance for current and future drug discovery. Here, we present WITHDRAWN, a resource for withdrawn and discontinued drugs publicly accessible at
SureChEMBL: a large-scale, chemically annotated patent document database
, 2015
"... SureChEMBL is a publicly available large-scale re-source containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides ac- ..."
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SureChEMBL is a publicly available large-scale re-source containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides ac-cess to a previously unavailable, open and timely set of annotated compound-patent associations, com-plemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent doc-uments, analysis of SureChEMBL data has immedi-ate applications in drug discovery, medicinal chem-istry and other commercial areas of chemical sci-ence. Currently, the database contains 17 million compounds extracted from 14 million patent doc-uments. Access is available through a dedicated web-based interface and data downloads at:
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
"... Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best pre-dictive performance in areas such as speech and image recogniti ..."
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Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best pre-dictive performance in areas such as speech and image recognition by hierarchi-cally composing simple local features into complex models. Although DNNs have been used in drug discovery for QSAR and ligand-based bioactivity predictions, none of these models have benefited from this powerful convolutional architec-ture. This paper introduces AtomNet, the first structure-based, deep convolutional neural network designed to predict the bioactivity of small molecules for drug dis-covery applications. We demonstrate how to apply the convolutional concepts of feature locality and hierarchical composition to the modeling of bioactivity and chemical interactions. In further contrast to existing DNN techniques, we show that AtomNet’s application of local convolutional filters to structural target infor-mation successfully predicts new active molecules for targets with no previously known modulators. Finally, we show that AtomNet outperforms previous docking approaches on a diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9 on 57.8 % of the targets in the DUDE benchmark. 1
Developing a Molecular Roadmap of Drug- Food Interactions
, 2016
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
RESEARCH ARTICLE Open Access
"... Unusual conservation among genes encoding small secreted salivary gland proteins from a gall midge ..."
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Unusual conservation among genes encoding small secreted salivary gland proteins from a gall midge
