Abstract
The recent worldwide spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1, has been endangering the life of human beings all around the world. To provide useful clues for developing antiviral drugs, information of anatomical therapeutic chemicals is vitally important. In view of this, a CNN based predictor called “iATC_Deep-mISF” has been developed. The predictor is particularly useful in dealing with the multi-label systems in which some chemicals may occur in two or more different classes. To maximize the convenience for most experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iATC_Deep-mISF/, which will become a very powerful tool for developing effective drugs to fight pandemic coronavirus and save the mankind of this planet.
Highlights
In 2017, a powerful predictor called “iATC-mISF”, was developed, which is overwhelmingly superior to its counterparts
The new predictor developed via the above procedures is called “iATC_Deep-mISF”, where “iATC_Deep” stands for “predict anatomical therapeutic chemicals”, and “mISF” for “multi-label classes”
To make them more intuitive and easier to understand for most experimental scientists, here we use the following intuitive Chou’s five metrics [7] or the “global metrics” that have recently been widely used for studying various multi-label systems
Summary
In 2017, a powerful predictor called “iATC-mISF”, was developed, which is overwhelmingly superior to its counterparts. According to the 5-step guidelines [3] and demonstrated in a series of recent publications (see, e.g., [4] [5]), to develop a statistical predictor that can be used by experimental scientists and can stimulate theoretical scientists to develop more relevant ones, we should make the following five steps crystal clear: 1) benchmark dataset, 2) sample formulation, 3) operation algorithm, 4) anticipated accuracy, and 5) web-server. We are to elaborate how to deal with these procedures one-by-one
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