PONE-D-23-09446Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic spacePLOS ONE

Dear Dr. Yabuuchi,

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Additional Editor Comments:

Please, consider the minor revisions suggested by the reviewers.

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Comment for the manuscript:

“Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space”

Author have applied semantic space and word embedding algorithm to develop machine learning for classification of antimicrobial compouds . This a new type methods to develop machine learning model for classification.

In my opinion -

It is a well-written manuscript.

Methods are written explaning every step.

The results and figures are well explained.

The discussion and conclusion explain very well the outcomes of this manuscript.

Reviewer #2: The manuscript entitled ‘Virtual screening of antimicrobial plant extracts by machine-learning

classification of chemical compounds in semantic space’ described the evaluation of a new computational screening strategy for classifying bioactive compounds and plants in semantic space generated by word embedding algorithm.

The manuscript is well written. The study is relevant and important especially for researchers in countries where traditional knowledge about the medicinal uses of available plant has become limited or unavailable. In such situations applications like this can help explore the flora for drug discovery. It is also important for areas with rich traditional knowledge but where indigens are unwilling to give out information on important medicinal plants for research.

Unfortunately, the application does not address the issues of chemical and bioactive variation in plants due to factors such as geographical location of plant material, season of harvesting etc. that may affect predictions by the application.

Lines 236-238: A stronger reason HTS are uncommon is the cost involved in setting one up (acquisition, installation and maintenance). Revise.

Linea 248-249: why should plants that were previous investigated be subjects to WEBVS in its large-scale exploration? Is it necessary?

Linea 264: What else is there to consider about the medicinal potential of C. sieboldii when the authors had indicated that the plant is in short supply therefore its removal from the Japanese Pharmacopoeia? Revise.

Overall, it is a good manuscript worthy for consideration for publication after minor corrections.

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Reviewer #1: Yes: Subhash Chandra

Reviewer #2: Yes: Gustav Komlaga

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Virtual screening of antimicrobial plant extracts by machine-learning classification of chemical compounds in semantic space

PONE-D-23-09446R1

Dear Dr. Yabuuchi,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Guadalupe Virginia Nevárez-Moorillón, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

I reviewed the corrections in the final document, as described in the cover letter by the corresponding author. Thank you for the edits and the manuscript can be accepted without further changes.

Reviewers' comments: