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Dear xxxxx,
You will be pleased to know that your paper entitled ¡°xxxxxxxxxxxxxxxxxxxxx¡± is reviewed and will be re-evaluated for publication in xxxxxxxxxxxxxx only after incorporation of the following Major changes suggested by the referee(s) .Please find below the comments and send us the REVISED paper within a week(by May 2, 2009).
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Referee 1 Comments:
This is an interesting manuscript and allows the reader to see the original data used for xxxx. An important aspoect is that the influence of xxxxxxxxxhas been incorporated xxxxxxxxxxxxx.
Although the manuscript is set out logically, it could do with a grammar check from a native English speaker.
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Referee 2 Comments:  This paper proposes prediction of xxxxxxxxxxxx by a model to analysis previously reported compounds. This is an important topic; however, 1. The manuscript did not give where the model Eq. (1) come from, or how is the Eq. (1) developed? 2. The model is not a new one. The prediction of xxxxxxxxxxxxxx by considering the role of xxxxxxxxxxxxxxxxxxxx has been reported in literature (xxxxxxxxxxxxxxxxxxxxxxx).
ÎÒ²»¶®This is an important topicÊÇʲôÑùµÄÒ»¸öÆÀ¼Û°¡£¬ºóÃæÌáÁË2¸öÎÊÌ⣬µÚÒ»¸öÎÒÖªµÀ¸ÃÈçºÎ»Ø´ð¡£µÚ2¸öÎÊÌâÊÇÎÒÓõ½µÄÒ»¸öÒòËØÇ°ÈËÒѾ­¿¼Âǵ½ÁË£¬ÎÒÒ²ÓÃÁË£¬ËùÒÔ²»ÊÇnew one£¬ÄÇÓ¦¸Ã×ö³öÈçºÎµÄ¼û½â£¿Ìá³ö×Ô¼ºµÄÐÄÒâÂð£¿

Referee 3 Comments: I recommend that this manuscript should be accepted for publication after revision according to the following comment: 1) Page 3, line 7: Concerning xxxxxx: Are there any structural features in xxxxxx? If so, describe it briefly.
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Referee 4 Comments:
The paper can not be judged due to experimental, molecular and meritorical problems. The structure of paper and procedures are not clearly described and seen not appropriate. Reviewer believes that this paper is irrelevant as typical medical paper about xxxxxxxxxxxxxxx. Data in paper mostly are not new, because Authors addressed fully or partially old data (see references 1, 2, 6, 12, 22, 23, 24). In generally Authors repeated data from other scientists. They are not presenting typical experimental data. In this situation it is difficult to discus xxxxxxxxxxxxxxx and especially xxxxxxxxxxxx. About above problems they can only speculate. It is difficult to accept study, which are summarized on page 7 line 4 in next ways ¡°xxxxxxxxxxxxx¡±. No scientific comments from neuroscience reviewer side may be being able to comment this computer specialist. Additionally in paper are grammar mistakes. It is typical paramedical paper. It can be consider in chemical, physical or mathematical journals for publication.
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Referee 5 Comments:
There are numerous spelling and grammatical errors throughout the manuscript that need to be corrected.
The second paragraph in the introduction does not make any sense. The two sentences seem to be two separate ideas that are unconnected.
Second page, end of first paragraph: The ¡®biological factors¡¯ are not discussed.
Methods section, Descriptor Collection: There is no discussion about xxxxxxxxxxxx in the following section. A brief introduction to the methods xxxxxxxxxxxxx should be given.
Methods section, Data Collection: There is no discussion as to how the compounds were partitioned into the training and test sets. Furthermore, it would appear from figure 1 that the chemical space represented by the compounds in the test set is not adequately covered by the compounds in the training set. (see, for example, compounds 98, 99, and 108). In addition, there are quite a few small organic compounds represented in the training set with no comparable examples in the test set. A quantitative measure of the chemical similarity, for example, may be informative. Taken together, these factors could bias the model towards the compounds in the training set.
Results & Discussion ¨C Development of Predictive Model: There is no discussion devoted to xxxxxxxxxxxxxxxx. This is curious, as Table 1 indicates that the values of the descriptors calculated for these five compounds are (with on exception) within the dynamic range of the dataset used to train the model.
Proper controls were not carried out with respect to model validation in both the training and test sets. The responses  should be randomized (or scrambled) and the model building / validation carried out to ensure that the experimental approach cannot be attributed to modeling noise.
Although the author¡¯s efforts to include xxxxxxxxxxxx in the model are admirable, they have not actually shown that incorporation of these descriptors have resulted in a better model. The authors should use the same procedures to generate models without xxxxxxxxxxxxx, and compare the results to their existing model. If the model presented in the manuscript is shown to be significantly improved over these three models, the authors may make the claim that incorporation of xxxxxxxxxxx descriptors result in a model that is a better predictor of xxxxxxxxxxxxxxxx.
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