Deep Neural Networks (DNNs) have been in the spotlight for last couple of years. This technique has been applied with great success to traditional Machine Learning problems such as Image and Speech recognition. Moreover, it has been applied to other learning problems such as mastering the game of Go being able to beat a human professional of this game for the first time in history.A DNN is a system made of biologically-inspired neurons that are organized in layers with increasing levels of abstraction. Take for instance a DNN trained to classify paintings by their author. In such system the first layers will be in charge of detecting colors and edges. Subsequent layers will use that information to assess more complex patterns such as shapes, and recurring motives. Finally, the last layers will take all that information into consideration in order to predict the artist that produced a given painting.DNNs' ability to extract information at different levels of abstraction can also be used to perform creative tasks. For instance, work has been done to use DNNs for speech synthesis, the design of new encryption algorithms, and to apply a given pictorical style to an image.
20/02/2017 | Tags:
In the past decades, the ‘one gene, one target, one drug’ paradigm has dominated drug discovery, inspiring the development of potent and exquisitely selective ligands that can guard against unwanted side effects.
18/03/2013 | Tags:
The majority of drugs in the market regulate the function of a protein through binding to its active site. On the other hand, allosteric sites are hardly targeted by small molecules even though they exhibit many advantages as drug targets. One reason may be the difficulty in detecting allosteric sites. For example, a couple hundred allosteric sites have been detected on crystallized proteins while thousands of active sites are present. Consider there exist as many allosteric sites as active sites, imagine how many possibilities for drug discovery open.Read More
18/02/2013 | Tags:
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