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Review
Peer-Review Record

“Dividing and Conquering” and “Caching” in Molecular Modeling

Int. J. Mol. Sci. 2021, 22(9), 5053; https://doi.org/10.3390/ijms22095053
by Xiaoyong Cao 1 and Pu Tian 1,2,*
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Int. J. Mol. Sci. 2021, 22(9), 5053; https://doi.org/10.3390/ijms22095053
Submission received: 3 March 2021 / Revised: 26 April 2021 / Accepted: 27 April 2021 / Published: 10 May 2021
(This article belongs to the Special Issue Advances in Molecular Simulation)

Round 1

Reviewer 1 Report

In the letter to the Editor, the authors wrote that the review is sent to RSC Advances and not to the International Journal of Molecular Sciences. This must be clarified as the article should not be sent to two different journals at the same time.

The review is interesting and discusses important challenges in molecular modeling: an accurate description of molecular interaction and efficient sampling methods. It presents different approaches to coarse-graining molecular systems. The recent progress in the machine learning approach to molecular potentials and sampling is also discussed. Finally, the local free energy landscape approach is presented as another strategy 
that facilitates molecular modeling through partially transferable in resolution caching of distributions for local clusters of molecular degrees of freedom.

The review cites almost 200 references, mostly from the last few years which is adequate.

 

Author Response

We carefully revised the whole manuscript and detailed changed are highlighted. Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

In this review, Cao and Tian focused on reviewing the main methodological developments in molecular modeling in the past years, including the most recent innovations in the adaptation of machine learning related methods to this area of research. Overall, this review gives a broad view of the several methods, without focusing too much on the methodological details, but with an introductory perspective that can easily be followed by new researchers. The approach used by the authors to move away from the detailed description of the more traditional methods, focusing more on the new trendy machine learning approaches is something not so much seen in other reviews. The review is well written with some pin-pointed grammar errors that should be corrected. I found this review very interesting and, in my opinion, it can follow on for publication after a thorough revision of the english.

Author Response

We made changes throughout the manuscript for both English issues and more accurate representation of ideas. Modifications are highlighted in the revised manuscript. Please see the attachment.

Author Response File: Author Response.pdf

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