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The chemical space exploration is an in-silico lead discovery process which is not restricted by the existing compound libraries. On the other hand, the vastness of the chemical space can pose a limit on its application. That is also the case of a recently introduced molecular morphing-based method called Molpher which is focused on exploring the space between a pair of molecules by finding a connecting...
The EU-funded eNanoMapper project proposes a computational infrastructure for toxicological data management of engineered nanomaterials (ENMs) based on open standards, ontologies and an interoperable design to enable a more effective, integrated approach to European research in nanotechnology. eNanoMapper's goal is to support the collaborative safety assessment for ENMs by creating a modular, extensible...
Nanomaterials are of interest to virtually every scientific discipline and a wide diversity of user communities. To date, however, there is no agreed upon system for describing nanomaterials, especially across disciplines and user communities. A CODATA-VAMAS Joint Working Group has defined a Uniform Description System (UDS) for nanomaterials that takes into account a wide variety of features useful...
Fang-Feng-Tang is a Chinese herbal formula which is designed to treat syndrome Feng-Han-Shi-Bi for rheumatoid arthritis (RA). Although its therapeutic effects are widely accepted, its mechanism is still obscure. In this study, by integrating leading knowledge databases of associated different domains, we explored Fang-Feng-Tang's potential therapeutic agents for RA through targeted pathway hierarchies...
Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there...
Prediction the interactions between proteins (targets) and small molecules (ligands) is a critical task for the drug discovery in silico. In this work, we consider the target binding site instead of the whole target and propose a pairwise input neural network (PINN) for constructing the site-ligand interaction prediction model. Different with the ordinary artificial neural network (ANN) with one vector...
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