A practical guide to identifying appropriate reviewers, avoiding conflicts, using AI responsibly, and predicting who is most likely to accept
Suggesting reviewers is one of the most delicate parts of a journal submission.
It can be tempting to treat it as a strategic exercise:
"Who will give us a favourable review?"
That is the wrong starting point.
The appropriate question is:
Who is sufficiently knowledgeable, sufficiently independent, and sufficiently likely to evaluate this manuscript fairly?
A good reviewer list helps the editor find qualified experts quickly. A poor list can raise concerns about conflicts of interest, reviewer manipulation, or attempts to influence the editorial process.
The ideal reviewer-suggestion process therefore combines scientific expertise, independence, diversity, availability and editorial judgment.
1. What is the purpose of suggesting reviewers?
When a journal asks authors to suggest reviewers, it is generally not asking:
"Who do you want to review your paper?"
It is asking:
"Who could competently and independently evaluate this work?"
The editor remains responsible for choosing the reviewers.
Your suggestions are therefore recommendations, not nominations.
A strong reviewer list gives the editor several useful options.
2. The first principle: choose expertise, not friendliness
The most important rule is:
Never start by asking who likes your work. Start by asking who understands your work.
Suppose your paper contains:
- comparative genomics,
- phylogenetics,
- gene-loss analysis,
- transcriptomics,
- evolutionary modelling,
- and functional interpretation.
You may need reviewers with different strengths.
For example:
| Reviewer | Primary expertise |
|---|---|
| A | Comparative genomics |
| B | Molecular evolution |
| C | Gene-loss/pseudogene evolution |
| D | Transcriptomics |
| E | Functional genomics |
You don't necessarily need five people who work on exactly the same gene.
You need a group capable of evaluating the different claims made by the paper.
3. Start by identifying the manuscript's "reviewer needs"
Before searching for names, make a list of the expertise required.
For example:
Scientific question
Who studies the biological phenomenon?
Methodology
Who understands the main analytical approach?
Data type
Who understands the underlying dataset?
Interpretation
Who works on the broader biological/evolutionary question?
Specialized methods
Who understands the unusual method or model?
This produces a reviewer expertise matrix.
For example:
| Manuscript component | Required expertise |
|---|---|
| Gene loss | Comparative genomics |
| Orthology | Genome evolution |
| Phylogeny | Molecular phylogenetics |
| Selection tests | Evolutionary modelling |
| Transcriptomics | Gene-expression analysis |
| Functional interpretation | Molecular biology |
Now search for people who collectively cover these areas.
4. Where should reviewer candidates come from?
There are many legitimate sources.
A. Recent papers
Probably the best starting point.
Search for researchers who published papers addressing the same scientific problem within approximately the last 3–5 years.
Look at:
- authors of key papers
- authors of recent methodological papers
- authors of competing interpretations
- authors of high-quality reviews
5. Follow the citation network
Start with your manuscript's most important references.
For each important paper:
- Who wrote it?
- Who cited it?
- Who subsequently challenged or extended it?
- Who publishes repeatedly in this area?
- Who developed the methods you use?
This creates a much better reviewer pool than simply searching Google for:
"gene X expert."
6. Look at recent papers rather than reputation alone
A famous scientist may be an excellent expert but a poor practical reviewer candidate.
Possible problems:
- no longer active in the field
- too senior and overloaded
- moved into a different research area
- does not review manuscripts anymore
- lacks expertise in the specific methods
A researcher publishing three relevant papers in the last two years may be a better candidate than a famous scientist who published one relevant paper ten years ago.
7. Examine the authors' recent publications
For each candidate, ask:
How recently did they publish on the topic?
How often do they publish in the area?
Are they methodologically relevant?
Do they publish independently?
Are they likely to understand the manuscript?
Are they currently active?
This is much more informative than citation count alone.
8. Build a reviewer candidate spreadsheet
A lab should ideally maintain one.
For example:
| Candidate | Institution | Expertise | Recent relevant paper | Conflict? | Likely expertise | Availability | Recommendation |
|---|---|---|---|---|---|---|---|
| A | Univ X | Gene evolution | 2025 | No | Very high | High | Yes |
| B | Univ Y | Phylogenomics | 2026 | No | High | Medium | Yes |
| C | Univ Z | Functional biology | 2024 | Recent collaborator | High | Unknown | No |
| D | Univ Q | Gene loss | 2026 | Competitor | Very high | Unknown | Maybe |
This is far better than producing five names at the last minute.
9. Conflict of interest is the critical filter
Before suggesting anyone, investigate conflicts.
Potential conflicts include:
Recent collaboration
Someone who recently co-authored a paper with you is generally inappropriate.
Same institution
A researcher at your institution is usually unsuitable.
Recent student-supervisor relationship
Avoid former supervisors, students and close academic relationships where the journal's policies consider them conflicts.
Family/personal relationships
Obviously avoid these.
Financial relationships
Avoid people with relevant financial interests.
Direct professional conflicts
If there is an ongoing dispute, competition or formal conflict, be cautious.
Manuscript overlap
A person working on an extremely similar unpublished project may present a competing-interest problem.
10. Scientific disagreement is not automatically a conflict
This distinction is important.
Suppose your manuscript challenges Professor X's published interpretation.
Should Professor X automatically be excluded?
Not necessarily.
If Professor X is an independent expert and the journal permits author-suggested reviewers, they may actually be an excellent reviewer.
But there is a difference between:
"This researcher has a different scientific interpretation."
and:
"This researcher is involved in an active personal/professional dispute with us."
The former can be scientifically valuable.
The latter may constitute a genuine conflict.
11. Competitors require careful judgment
Direct competitors are complicated.
A direct competitor may be:
- highly qualified,
- completely independent,
- scientifically ideal,
but may also have:
- unpublished competing work,
- a strong personal stake in the outcome,
- access to your ideas before publication.
Do not automatically exclude competitors.
Instead ask:
Would a reasonable editor consider this person capable of providing an independent review without a significant competing interest?
If the answer is uncertain, disclose the issue rather than attempting to manipulate the reviewer pool.
12. How many reviewers should you suggest?
Follow the journal's instructions.
If it asks for:
3 reviewers
provide three strong candidates.
If it asks for:
5–8
provide a broader but carefully screened pool.
Do not submit 20 names merely to increase the chance that your preferred person is selected.
Quality matters more than quantity.
13. Don't suggest only your friends
This is one of the most obvious warning signs.
A suspicious list might consist of:
- former collaborators,
- close colleagues,
- former supervisors,
- people from your academic network,
- researchers who cite you frequently.
Even if none technically violates a conflict rule, the pattern can look inappropriate.
14. Don't suggest only famous scientists
The opposite mistake is also common.
A list of five Nobel-level or field-leading researchers may look impressive but be practically useless.
Very senior researchers may:
- receive enormous numbers of invitations,
- have heavy administrative responsibilities,
- decline most reviews,
- delegate some reviewing,
- have limited time.
A mid-career researcher with exactly the right expertise may be much more likely to accept.
15. Which reviewers are most likely to accept?
There is no reliable way to predict an individual's decision.
However, reviewer acceptance tends to be more plausible when the candidate:
Is actively publishing
Someone currently publishing in the area is more likely to see the manuscript as relevant.
Has reviewed before
Researchers who regularly review for journals are accustomed to the process.
Is at an appropriate career stage
Mid-career researchers and established early-career investigators may have both expertise and motivation, although this varies enormously.
Has a close topical match
A manuscript directly aligned with their research is more attractive than a peripheral connection.
Has the methodological expertise
Researchers are more likely to accept when they can confidently evaluate the work.
Is not overloaded
A person with a huge administrative workload may decline even if they are the perfect scientific match.
Is not conflicted
Obvious conflicts reduce the likelihood of a legitimate invitation.
16. A useful way to think about reviewer acceptance
You can conceptualize reviewer availability as:
Expertise × relevance × independence × availability
rather than:
fame × citation count.
A candidate with moderate fame but excellent topical fit may be a much better reviewer than a superstar.
17. Early-career researchers can be excellent reviewers
Do not restrict your list to professors.
A postdoctoral researcher, assistant professor, research scientist or newly independent investigator may have:
- extremely current expertise,
- detailed methodological knowledge,
- time and motivation,
- experience with the exact analytical techniques.
However, check whether the journal permits the person and whether the invitation should instead go to their PI or supervisor.
Never assume that someone should review simply because they are a coauthor's former student.
18. Geographic diversity
If appropriate candidates exist, avoid selecting everyone from:
- one country,
- one institution,
- one academic network.
A geographically diverse reviewer pool can help reduce network bias.
However, expertise should come first.
Do not select a weaker reviewer merely to satisfy geographic diversity.
19. Gender and other diversity considerations
Where multiple equally qualified candidates exist, diversity can be a useful consideration.
But diversity should not become a substitute for expertise.
The priority remains:
- competence
- independence
- relevance
- availability
with diversity considered among otherwise appropriate candidates.
20. Search systematically
A robust search strategy might look like this:
Step 1
Identify 10–20 relevant recent papers.
Step 2
Extract authors.
Step 3
Remove:
- your authors,
- recent collaborators,
- institutional colleagues,
- obvious conflicts.
Step 4
Rank the remaining candidates by expertise.
Step 5
Check recent publication activity.
Step 6
Check methodological relevance.
Step 7
Check current affiliation.
Step 8
Check whether they are plausibly independent.
Step 9
Select the strongest candidates.
Step 10
Verify contact details from authoritative sources.
21. Use multiple discovery routes
Do not rely on one database.
Useful sources can include:
- PubMed
- Google Scholar
- Web of Science
- Scopus
- Crossref
- ORCID
- institutional websites
- journal articles
- conference programs
- field-specific databases
The objective is not to find the biggest list.
It is to verify the identity, expertise and independence of candidates.
22. AI can help—but it should not make the final decision
AI can be very useful for generating a candidate pool.
For example, give an AI system:
- title
- abstract
- keywords
- methods
- major references
- research question
and ask it:
Identify researchers who have published peer-reviewed work in the last five years on the specific scientific questions and methods represented in this manuscript.
This can produce useful candidates.
But the output should be treated as:
a discovery tool, not a reviewer recommendation.
23. A good AI reviewer-search workflow
A robust workflow is:
MANUSCRIPT ↓ AI identifies scientific themes ↓ AI generates candidate researchers ↓ Human verifies publications ↓ Human checks conflicts ↓ Human checks current affiliation ↓ Human evaluates independence ↓ Human ranks candidates ↓ Final reviewer suggestions
The human verification stage is essential.
24. What AI is particularly good at
AI can help with:
Identifying subfields
For example:
comparative genomics + molecular evolution + pseudogenization
Finding expertise gaps
AI may recognize that your manuscript actually requires:
- phylogenetics expertise,
- statistical modelling,
- functional genomics,
- genome annotation.
Expanding candidate pools
It can identify researchers you may not have encountered through your immediate academic network.
Comparing expertise
You can ask AI to summarize:
What aspects of this manuscript could researcher X evaluate particularly well?
Detecting obvious conflicts
AI can flag potential coauthorship relationships—but these must be independently verified.
25. What AI should NOT be trusted to do
Do not blindly accept AI-generated:
- researcher names,
- affiliations,
- email addresses,
- publication histories,
- conflicts,
- claims about expertise.
AI systems can hallucinate.
A nonexistent paper or incorrect affiliation can easily make its way into a reviewer list.
Therefore:
Every suggested reviewer should be verified against primary or authoritative sources.
26. Don't ask AI "Who will give me a favourable review?"
That is a problematic objective.
A better prompt is:
"Identify independent researchers who are highly qualified to evaluate the scientific claims and methodology of this manuscript."
The distinction is important.
The goal should be appropriate review, not favorable review.
27. Don't use AI to predict personal bias
Be particularly cautious with prompts such as:
"Which researchers are likely to agree with our conclusions?"
or:
"Find reviewers who will be sympathetic to our paper."
This moves the process away from identifying independent expertise and toward attempting to influence peer review.
Instead ask:
"Which researchers have published evidence supporting, challenging, or independently investigating the scientific question?"
That produces a more balanced reviewer pool.
28. Use AI to identify both sides of a scientific debate
This can actually improve reviewer selection.
For example:
Identify researchers who have independently published evidence supporting the prevailing model and researchers who have published evidence challenging it.
Then:
- verify the candidates,
- remove conflicts,
- assess expertise,
- let the editor choose.
This is particularly useful when your manuscript challenges an established interpretation.
29. AI can help build a reviewer matrix
For example:
| Candidate | Expertise | Methods | Recent work | Independence | Potential conflict | Overall |
|---|---|---|---|---|---|---|
| A | Very high | High | 2026 | High | None | Excellent |
| B | High | Very high | 2025 | High | None | Excellent |
| C | Very high | Medium | 2026 | Low | Competitor | Avoid |
| D | Medium | High | 2026 | High | None | Good |
This is a much better use of AI than asking:
"Give me five reviewers."
30. Verify every candidate manually
For every final candidate, check:
Identity
Is this actually the person you think they are?
Current affiliation
Has the person moved?
Expertise
Do their recent papers actually match your manuscript?
Recent publication
Have they published recently in the area?
Conflicts
Have they coauthored with your authors?
Institution
Are they independent?
Relationship
Do you have a close professional relationship?
Contact details
Is the email address from an authoritative source?
31. Be careful with email addresses
Do not obtain reviewer emails from questionable databases.
Prefer:
- university profile
- laboratory website
- ORCID-associated information
- journal publication
- institutional directory
And if the journal can identify the reviewer from their name and affiliation without requiring an email, do not invent one.
32. What about people who cited your work?
They can be appropriate.
But citation alone does not make someone a good reviewer.
A person who cited your paper once in passing is not necessarily an expert.
Conversely, someone who never cited you may be an excellent reviewer.
Expertise should be determined from the body of relevant work, not citation relationships.
33. What about authors of papers you cite?
They are often excellent candidates.
But there are two possibilities:
They are independent experts.
Excellent.
Your manuscript directly attacks their published conclusion.
Potentially still appropriate, but consider whether there is a genuine conflict and whether the editor should know about the relationship.
Scientific disagreement itself is not a reason to manipulate the reviewer pool.
34. What about reviewers who reviewed your previous paper?
Potentially excellent candidates if:
- they are independent,
- their expertise remains appropriate,
- there is no conflict,
- the journal allows it.
But do not attempt to reconstruct anonymous reviewers through clues.
If a reviewer was anonymous, you should not try to identify them simply because you liked or disliked their review.
35. What about suggesting the same reviewers to multiple journals?
There is nothing inherently wrong with recommending an appropriate expert to another journal if the manuscript is subsequently submitted elsewhere.
But don't maintain a rigid list of "my five favourite reviewers."
The appropriate reviewer pool should be rebuilt based on:
- the manuscript,
- the target journal,
- the current literature,
- current conflicts.
36. A particularly useful concept: reviewer complementarity
Instead of asking:
Who are the five best experts?
ask:
What five perspectives would provide the strongest independent evaluation?
For example:
Reviewer 1 → Biological question Reviewer 2 → Evolutionary theory Reviewer 3 → Computational methodology Reviewer 4 → Statistical analysis Reviewer 5 → Functional interpretation
This can be much stronger than five people who all work on exactly the same biological system.
37. The ideal reviewer profile
A particularly good candidate often has:
- recent publications in the topic,
- direct methodological expertise,
- sufficient independence,
- active research,
- experience reviewing,
- no obvious conflicts,
- an appropriate career stage,
- a professional connection to the scientific problem rather than to the authors.
That combination is much more valuable than prestige alone.
38. A practical ranking system
A lab could score candidates:
| Criterion | Score |
|---|---|
| Scientific expertise | 0–5 |
| Methodological expertise | 0–5 |
| Recent activity | 0–3 |
| Independence | 0–5 |
| Relevance to manuscript | 0–5 |
| Availability likelihood | 0–3 |
| Conflict risk | −5 to 0 |
| Overall | — |
But do not turn this into an algorithm that automatically selects reviewers.
The scoring system is a decision aid.
39. An important warning: don't optimize for acceptance probability
You might be tempted to build:
"Reviewer X = 80% chance of accepting invitation."
That is not really knowable from public information.
Instead, use qualitative categories:
Likely to be feasible
- active researcher
- strong topical fit
- regular publication activity
- not obviously overloaded
Possibly difficult
- extremely senior
- major administrative role
- exceptionally high-profile
- already serving on many editorial boards
Poor candidate
- conflict
- weak expertise
- inactive in field
- insufficient independence
40. Never contact a reviewer asking them to review your paper
Unless the journal explicitly permits pre-submission reviewer contact, do not send someone:
"We have suggested you as a reviewer. Please accept if invited."
That undermines the independence of the process.
Similarly, do not send them the manuscript privately with the expectation that they will review it.
Let the journal handle the invitation.
41. Don't ask colleagues to create fake reviewer identities
This is an obvious but important ethical boundary.
Never:
- create fake email accounts,
- provide personal addresses pretending to be institutional,
- nominate yourself under another identity,
- ask a friend to review under a different identity,
- manipulate reviewer databases.
These practices can constitute serious publication misconduct.
42. Don't suggest people who cannot reasonably review the manuscript
For example, don't recommend:
- a graduate student who has no independent reviewing role unless the journal permits it,
- a retired researcher who has completely left the field,
- someone working in an unrelated field simply because they are famous,
- a person whose expertise is only tangentially related.
43. Reviewer suggestions should help—not control—the editor
The healthiest attitude is:
"Here are several people who could competently evaluate this manuscript."
not:
"Here are the people we want you to select."
The editor may select none of them.
That is completely normal.
44. A recommended laboratory workflow
For every manuscript:
Stage 1 — Identify expertise
Create the reviewer-needs matrix.
Stage 2 — Generate candidates
Use:
- recent papers,
- citation networks,
- databases,
- editorial boards,
- conferences,
- AI-assisted discovery.
Stage 3 — Create 10–20 candidates
Do not immediately select five.
Stage 4 — Verify candidates
Check:
- publications,
- affiliation,
- conflicts,
- independence.
Stage 5 — Rank
Identify the strongest candidates.
Stage 6 — Create a balanced final list
Provide the number requested by the journal.
Stage 7 — Record the rationale
Maintain the internal reviewer-selection record.
45. Keep a reviewer-selection record
For your lab's publication archive, I would create:
Reviewer_Selection/ ├── Candidate_List.xlsx ├── Expertise_Assessment.xlsx ├── Conflict_Check.xlsx ├── AI_Assisted_Search_Record.txt └── Final_Reviewer_List.pdf
This is particularly useful if a question later arises about why a reviewer was suggested.
46. A useful AI prompt
A responsible AI-assisted workflow might use a prompt like:
"Analyze the following manuscript abstract, key claims, methods and references. Identify the scientific subfields and methodological expertise required to independently evaluate the manuscript. Then generate a candidate pool of researchers who have published recent peer-reviewed work in those areas. For each candidate, explain which aspect of the manuscript they are qualified to assess. Do not rank candidates according to whether they are likely to support the manuscript's conclusions. Do not infer personal relationships or conflicts without evidence. Clearly mark any information that requires independent verification."
Then provide:
- title
- abstract
- key claims
- methods
- major references
After AI produces the candidate list, independently verify every candidate.
47. An even better AI workflow
Instead of asking AI for names immediately:
Prompt 1
Identify the five most important scientific expertise areas needed to review this manuscript.
Prompt 2
Identify the major scientific debates relevant to the manuscript.
Prompt 3
Identify researchers who have published recent work representing the different perspectives in those debates.
Prompt 4
Compare these candidates by topical expertise and methodological relevance.
Prompt 5
Identify information that must be independently verified before any candidate is suggested to a journal.
This reduces hallucination and confirmation bias.
48. The final reviewer-selection checklist
Before submitting the names:
Expertise
- Can this person genuinely understand the manuscript?
- Have they published recently in the relevant area?
- Do they understand the key methodology?
Independence
- No recent collaboration?
- No institutional conflict?
- No close personal/professional relationship?
- No inappropriate financial relationship?
Scientific balance
- Are different relevant perspectives represented?
- Is the list dominated by one academic network?
- Have we avoided selecting people simply because they are likely to agree?
Practicality
- Are they currently active?
- Is their current affiliation correct?
- Are contact details verified?
- Is the suggested number consistent with journal instructions?
AI
- Was AI used only as a discovery/organization aid?
- Were all candidates independently verified?
- Were no AI-generated claims about conflicts accepted without verification?
- Was AI used to identify expertise rather than favorable reviewers?
49. The central principle
The best reviewer-selection strategy is not:
"Find people who will like our paper."
It is:
"Find people who would be capable of finding the weaknesses in our paper, while being sufficiently independent to evaluate those weaknesses fairly."
That mindset has an interesting advantage.
If your manuscript survives scrutiny from people who genuinely understand its weaknesses, the paper is usually stronger.
50. The final reviewer-selection philosophy
A good reviewer list should make an editor think:
These are credible, independent experts who understand the science and can evaluate this manuscript properly.
It should not make the editor think:
These authors appear to be trying to engineer a favourable review.
The strongest reviewer-selection process therefore follows a simple sequence:
Understand the manuscript → identify the expertise required → find active experts → verify their work → eliminate conflicts → ensure appropriate breadth → suggest the strongest independent candidates.
AI can make the search and organization much faster.
It should not replace the scientific and ethical judgment involved in deciding who is an appropriate reviewer.
And perhaps the most important rule of all:
Choose reviewers you would still consider appropriate if you had no idea whether they would recommend acceptance or rejection.
That is a good test of whether the reviewer-selection process is genuinely about obtaining an independent scientific assessment rather than attempting to control the outcome.
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