By Barry A Bunin; et al
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Extra resources for Chemoinformatics : theory, practice, & products
16. Schematic architecture of a three layer feed-forward network Supervised learning using multi-layered NNs usually involves so called backpropagation of data in order to train the intermediate or hidden node layers for a given classification problem. Training is continued until a sufficiently accurate solution is obtained for the training set data, and the so derived node settings and connection weights are then used to classify new molecules. Typical tasks for supervised NN learning in chemoinformatics include, for example, distinguishing active from inactive compounds or drug-like molecules from non-drugs.
Com). Communication between different elements of the database is generally facilitated by using the Scientific Query Language (SQL). Typically, the user communicates with the database using “hidden” SQL through an interactive graphical interface to retrieve relational database information. An important feature of relational database design is that different types of data structures and software tools can be easily combined and transformed into coherent database architecture. This makes it possible to customize local databases for specific applications.
It is often crucial to apply different techniques in parallel and pay particular attention to the reproduction of available experimental results and the formulation of experimentally testable hypotheses. For a practitioner in the chemoinformatics area, it is clearly very important to compare different computational tools and their functionalities and gain experience by studying test cases. However, being aware of potential caveats and shortcomings, practical chemoinformatics can not only make a significant impact on chemistry programs, but can also be “great fun”.