From the journal Digital Discovery Peer review history

Dear Dr Reymond:

Manuscript ID: DD-ART-03-2023-000039
TITLE: Alchemical Analysis of FDA Approved Drugs

Thank you for your submission to Digital Discovery, published by the Royal Society of Chemistry. I sent your manuscript to reviewers and I have now received their reports which are copied below.

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César de la Fuente, Ph.D.
Associate Editor, Digital Discovery

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

Reviewer 1

The authors propose a new type of similarity measure based on reaction fingerprints and RXNmapper.
The paper is interesting for the technically minded reader and well written.

I think it would be interesting to discuss explicit if a low AMCD would correspond to transformations that a chemist would find intuitive.

Minor comment
Figure 3d, the delta value of 5.26 can hardly be meaningful.

Reviewer 2

The authors propose the combination of compound pair differences as calculated by differential reaction pair fingerprints (DRPF), RXNMapper, and TMAPs to map pairs of compounds ino a 2D space. The authors demonstrate that pairs of compounds that differ by similar transformations get mapped close in space.

The manuscript is clearly written and code is available.

To improve the value of the manuscript, I ask the authors to provide some more specific ideas of what this kind of visualization can be used for. In the conclusion section, the authors write " These tools should generally be applicable to analyze drug sets from multiple angles in the context of drug discovery. " This should be expanded upon.

Reviewer 3

The present manuscript describes the application of two similarity measures DRFP (differential reaction fingerprint) and RXNMapper, ordinarily used to encode chemical reactions, to map the structural similarity across drug pairs from three datasets – 1,213 FDA-approved drugs, 1,500 EGFR inhibitors, and 274 polymyxin B analogues.

The authors measured 8 different fingerprints [3 molecular fingerprints: extended connectivity fingerprints ECFP4 and ECFP6, the MinHashed Fingerprint MHFP6; 3 pharmacophore fingerprints: the RDKit Atom-Pair Fingerprint (AP), the Macromolecule Extended Fingerprint (MXFP), the MinHashed Atom-Pair fingerprint MAP4; and 2 composition fingerprints: the Molecular ACCess System keys (MACCS) and Molecular Quantum Numbers (MQNs)]. They calculated the corresponding pairwise distances (Jaccard or Taxicab) based on each fingerprint for every molecular pair and the ranking of molecular pairs in all three datasets. The distances were displayed using violin plots. These similarity measures were conducted across three datasets – the highly structurally diverse FDA-approved drugs, and two focused datasets EGFR inhibitors and polymyxin B (PMB) analogues. To avoid time-consuming experiments and overwhelming numbers of molecular pairs, the authors selected the top-20 nearest neighbours and similarity measures below a certain Jaccard threshold.

Overall, the manuscript is well-written. The merits of these two similarity measures are clearly explained. DRFP combined with tree-map (TMAP) visualization provides a global bidimensional similarity representation across drug pairs. RXNMapper allows to distinguish drug pairs from different chemical processes with (forward or backward) atom-mapping confidence scores.

The authors address the comments below;
(1) Typo?
RXNMapper or rxnmapper (page 6)

(2) “Distances were correlated between ECFP4, ECFP6, MHFP6, MAP4, which all encode circular substructures around atoms (r2~ 0.8, Figure 2b/3b/4b).” (page 7)
Looking at the figures 2b/3b/4b, correlation coefficient r2 >= 0.8. In Figure 4b, r2 ~0.65 for MAP4.

Reviewer 4

Exploring and understanding SAR among a set of cpds based on chemical similarity is essential to drug design and to extract medicinal design knowledge from very large datasets. The authors present a novel way to map multiple chemical similarity relationships simultaneously to help identify interesting pairs for SAR insights. The paper is well written and the method described should be widely applicable to drug discovery in the real world.

We would like to thank the editor and the four reviewers for analyzing our manuscript and providing constructive comments. We have modified the manuscript to address all questions asked as detailed below, all changes made are highlighted in yellow. With these changes, we hope that our manuscript will now be suitable for publication in RSC Digital Discovery.


Referee: 1

Comments to the Author
The authors propose a new type of similarity measure based on reaction fingerprints and RXNmapper.
The paper is interesting for the technically minded reader and well written.

I think it would be interesting to discuss explicit if a low AMCD would correspond to transformations that a chemist would find intuitive.

Our answer: We thank the reviewer for the helpful suggestion, we extended the discussion of amcd values on page 9 as follows: “This observation suggests that the amcd metric effectively captures chemically intuitive transformations, aligning well with the way chemists predict and perceive such changes in molecules during drug design and development.”

Minor comment
Figure 3d, the delta value of 5.26 can hardly be meaningful.

Our answer: Thanks for pointing this out. We have corrected the delta value in Figure 3d (there was a calculation error) and rounded, the corrected value is 4.3.



Referee: 2

Comments to the Author
The authors propose the combination of compound pair differences as calculated by differential reaction pair fingerprints (DRPF), RXNMapper, and TMAPs to map pairs of compounds ino a 2D space. The authors demonstrate that pairs of compounds that differ by similar transformations get mapped close in space.

The manuscript is clearly written and code is available.

To improve the value of the manuscript, I ask the authors to provide some more specific ideas of what this kind of visualization can be used for. In the conclusion section, the authors write " These tools should generally be applicable to analyze drug sets from multiple angles in the context of drug discovery. " This should be expanded upon.

Our answer: We thank the reviewer for these very positive comments. To address the comment on envisioning possible application, we have extended the conclusion by mentioning a very specific possible use case, with two added recent review articles references:

“One specific case could be the analysis of analog series obtained from generative models53,54 to help identify feasible transformations and single out scaffold hopping changes.”

Referee: 3

Comments to the Author
The present manuscript describes the application of two similarity measures DRFP (differential reaction fingerprint) and RXNMapper, ordinarily used to encode chemical reactions, to map the structural similarity across drug pairs from three datasets – 1,213 FDA-approved drugs, 1,500 EGFR inhibitors, and 274 polymyxin B analogues.

The authors measured 8 different fingerprints [3 molecular fingerprints: extended connectivity fingerprints ECFP4 and ECFP6, the MinHashed Fingerprint MHFP6; 3 pharmacophore fingerprints: the RDKit Atom-Pair Fingerprint (AP), the Macromolecule Extended Fingerprint (MXFP), the MinHashed Atom-Pair fingerprint MAP4; and 2 composition fingerprints: the Molecular ACCess System keys (MACCS) and Molecular Quantum Numbers (MQNs)]. They calculated the corresponding pairwise distances (Jaccard or Taxicab) based on each fingerprint for every molecular pair and the ranking of molecular pairs in all three datasets. The distances were displayed using violin plots. These similarity measures were conducted across three datasets – the highly structurally diverse FDA-approved drugs, and two focused datasets EGFR inhibitors and polymyxin B (PMB) analogues. To avoid time-consuming experiments and overwhelming numbers of molecular pairs, the authors selected the top-20 nearest neighbours and similarity measures below a certain Jaccard threshold.

Overall, the manuscript is well-written. The merits of these two similarity measures are clearly explained. DRFP combined with tree-map (TMAP) visualization provides a global bidimensional similarity representation across drug pairs. RXNMapper allows to distinguish drug pairs from different chemical processes with (forward or backward) atom-mapping confidence scores.

The authors address the comments below;
(1) Typo?
RXNMapper or rxnmapper (page 6)

Our answer: Thanks for pointing this out, we have corrected to uniformly use “RXNMapper”


(2) “Distances were correlated between ECFP4, ECFP6, MHFP6, MAP4, which all encode circular substructures around atoms (r2~ 0.8, Figure 2b/3b/4b).” (page 7)
Looking at the figures 2b/3b/4b, correlation coefficient r2 >= 0.8. In Figure 4b, r2 ~0.65 for MAP4.

Our answer: Thanks for pointing this out. We added a comment to explain the lower correlation of the MAP4 fingerprint with the other types of fingerprints on page 7 as follows: “However, the correlations of MAP4 with other circular substructure fingerprints, particularly in the polymyxin B2 set, were generally lower. This can be attributed to its hybrid nature, which encodes both substructures and atom-pairs. Even so, the correlation between MAP4 and circular substructure fingerprints was notably stronger than its correlation with other fingerprint types.”


Referee: 4

Comments to the Author
Exploring and understanding SAR among a set of cpds based on chemical similarity is essential to drug design and to extract medicinal design knowledge from very large datasets. The authors present a novel way to map multiple chemical similarity relationships simultaneously to help identify interesting pairs for SAR insights. The paper is well written and the method described should be widely applicable to drug discovery in the real world.

Our answer: Thanks for these very positive comments.

Dear Dr Reymond:

Manuscript ID: DD-ART-03-2023-000039.R1
TITLE: Alchemical Analysis of FDA Approved Drugs

Thank you for submitting your revised manuscript to Digital Discovery. I am pleased to accept your manuscript for publication in its current form. I have copied any final comments from the reviewer(s) below.

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César de la Fuente, Ph.D.
Associate Editor, Digital Discovery

Reviewer 1

The authors has responded to my comments