1. How to predict the basic chemical properties?

    The TargetNet can only be accessed by selecting 'Basic Properties Prediction' link shown as the following figure.

    Step 1: Users are required to submit a molecular structure in SMILES format or a molecular file in SDF format or to draw a structure using the JME editor.
    The first way: By Inputing SMILES
    Type in SMILES into the field or click the example button to load a example SMILES.
    The second way: By uploading molecular file
    Choose a molecular file in format of SDF. Users can also click the ‘example’ button to download a molecular file for an example.
    The third way: By drawing molecule from editor below
    Input a molecule by drawing structure in the JSME editor form Peter Ertl.
    Step 2: Set parameters
    There are two parameters need to be set. The first one: select data source can be chosen according to the step 1. The second parameter: select method can be chosen from 'Ramdom forest', 'Cubist', 'SVM', and 'Boosting' which are four algorithms applied into the models for prediction.
    Step 3: Submit and process
    Click the 'Submit' button and then start processing. The processing will last for a while and then users will be redirected to the result page.

    Step 4: Results
    The result table contains "bp", "den", "mp", "sol", "st", "vis", "vp", and "fp". "1" marks the ‘per page’ button, and users can choose the number of items shown in the table. "2" marks the "search" button, and users can conveniently type in a keyword to look for a certain item in the results. "3" marks the ‘rank’ button, and users can click the button to get an increasing or decreasing order of the values. "4" marks the "download" button, and users can download the results as a *.csv file.

  2. How to calculate the TPSA, MR and LogP ?

    Step 1: Input molecular structures
    Step 2: Set parameters
    Step 3: Processing and results
    These three steps are like the steps in "How to predict the basic chemical properties?".

Visits since Aug 30, 2016

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