Dear Dr. Cagan,

Thank you very much for submitting your manuscript "Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: the Multi-Targeting Drug DREAM Challenge" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Edwin Wang

Benchmarking Editor

PLOS Computational Biology

Edwin Wang

Benchmarking Editor

PLOS Computational Biology

***********************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: In this manuscript, the authors reported the main results of the Multi-Targeting Drug DREAM Challenge, in developing computational methods for prediction of multi-target kinase inhibitors to treat RET-based cancer models and TAU-based neurodegenerative disease model in Drosophila. This Challenge was not organized in a very intelligent manner, and the results might not be so striking. I think the methods developed in this Challenge will be useful, however, only considering static features of proteins or small molecules might not ensure to reach a promising accuracy in reality. Since this Challenge has been finished, and indeed all participants have tried their best on method development, I think this paper can be accepted, after a minor but essential revision. I hope the authors can organize their future Challenge in a more sophisticated manner, and developed methods should touch the real fundamental problems. My major concerns are below:

1. The basic rationales of the Challenge were established on hypotheses. In Problem 1, the aim was prediction of small molecules that efficiently bound RET, BRAF, SRC and S6K, and didn’t bind MKNK1, TTK, ERK8, PDK1, and PAK3. In Problem 2, the aim was prediction of small molecules that efficiently bound AURKA, PAK1, FGFR1, and STK11, and didn’t bind PAK3, MAP3K7, and PIK3CA. 1) I do not think the concepts of pro-targets and anti-targets have been widely adopted by academic community, and targeting pro-targets but not anti-targets might not be the central dogma in drug design. 2) The basic hypothesis behind the Challenge is that if a small molecule can efficiently bind pro-targets but not anti-targets, corresponding diseases will be efficiently cured. I do not think this hypothesis is right. The identified small molecules might have additional side effects, and might be toxic in humans. 3) The second hypothesis behind the Challenge is that experimental in Drosophila can be used in humans. I also do not agree with this point. Even experiments in mice might make nonsense in humans. Human diseases are very complicated, and I do not think fly model can be really useful. Since the Challenge has been finalized, I hope the authors can be more cautious on organizing such competition in the future. A good Challenge cannot be founded on sand piles.

2. In the biological aspect, drugs that can efficiently treat the human diseases but not one or multiple proteins will be more preferred. If a drug can indeed block some proteins but cannot efficiently treat the disease, I do not think such drugs can be useful. Thus, an additional assay should be adopted to validate whether selected drugs can efficiently inhibit RET- and TAU-based models. If the authors cannot do such experiments, they must demonstrated why selected drugs targeting some proteins but not others can be really useful in disease treatment.

3. Prediction of multi-protein targeting inhibitors is an interesting topic. However, I do not think using static features derived from proteins or small molecules is enough to reach a promising accuracy. Image-base screening of small molecules using an appropriate readout might also be efficient. Thus, combination of multi-modal data types might be more useful.

4. Method 1 and 2 should be described with more details. Features derived from proteins or small molecules should be listed and interpreted in an Excel table. Used parameters in neural networks should be present to enable a full reproducibility. Please give a formal names of Zhaoping Xiong’s method.

Taken together, I think the paper can be accepted, although the Challenge did not touch a very important problem. In the past years, a number of such Challenges have been organized. None of them really resolved any important problems.

Reviewer #2: The authors described the development of the Multi-Targeting Drug DREAM Challenge for the identification of multi-target kinase inhibitors. This development is necessary because multi-targeting therapeutics have key advantages over single-targeting therapeutics, but it is difficult to evaluate which molecules have desirable target profiles. The authors designed two problems and around ten teams submitted their results to this Challenge. The authors combined computational approaches and experiment validation to evaluate the submitted drugs. The models of two teams with the top scores were shared with the public through Github. This manuscript is well-structured. I recommend it for publication.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: No: Features used in descrbed methods are not available and should be provided in the revision.

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear Dr. Cagan,

We are pleased to inform you that your manuscript 'Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: the Multi-Targeting Drug DREAM Challenge' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Edwin Wang

Benchmarking Editor

PLOS Computational Biology

Edwin Wang

Benchmarking Editor

PLOS Computational Biology

***********************************************************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors carefully revised the manuscript, and addressed all my concerns. The current form is ready for publication.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Yu Xue