For past SAMPL special issues, please refer to our history.
SAMPL7 Physical-Properties Challenge
In coordination with Terry Stouch, we are planning a special issue of J. Comp. Aided Mol. Design (JCAMD) focused on the SAMPL7 physical properties challenge. The submission deadline for this is March 31, 2021, and the issue is already open for submission; standard analysis results are available on the SAMPL7 GitHub repo. As usual, papers are expected to appear online in advance of their publication in the special issue as soon as they are ready unless otherwise requested. Articles will typically appear in the final issue in order of submission, and the first few articles get priority for selection for cover artwork.
General plans for future special issues
We plan to continue running SAMPL special issues and special sections in partnership with JCAMD. We expect that, with each individual challenge, we will announce a submission deadline. When multiple challenges/challenge components occur around the same time, these will likely be coupled into a single special issue, whereas if challenges are quite disparate in timing they will be split across separate issues or special sections in a regular issue.
We expect to typically have two special issues or special sections per year in JCAMD except if/when all challenges in a given year end up occurring around the same time, in which case a single issue may be sufficient.
The distinction between special issues and special sections is planned to be driven by participation/submission rate; when enough papers are submitted, we plan on a special issue, but will fall back to a special section of the journal when this is not the case.
In general, submission deadlines are firm; papers submitted late are not guaranteed to be included in the same special issue or special section, though in some cases they may be if they make it through the review process rapidly enough. Most late papers will be published in the next regular issue(s) or in a future SAMPL special issue or section if it is soon, at the discretion of the editor and guest editor in consultation with the author.
Review criteria for special issues
In general, review criteria for SAMPL special issues are modestly different than for typical journal publications. Specifically, since these are blind prediction challenges, the community feeling has been that participants should be entitled to report what they did even if the reviewers might feel that it was ill advised, not particularly novel/exciting, or had modest had technical problems. However, papers must still:
- fairly report their results, without overselling or giving an unwarranted sales pitch
- clearly identify and discuss any technical flaws reviews or others might have highlighted
- provide adequate details (and supporting materials) so that others can reproduce the work
and otherwise ensure that they meet the standards of the journal.
Additionally, as a particular focus of SAMPL is on lessons learned, authors are urged to pay devote careful attention to highlighting what was learned from participation and how it might be of benefit to the field or to others employing similar methodologies.
Submitters/participants often review papers
Normally, we expect challenge participants to be willing to review one another’s papers with the criteria above in mind. We do not see this as a conflict of interest (or rather, the editor will serve to try and defray/moderate any potential conflicts) since these are blind challenges, and subject to the review criteria above. In other words, participants are typically entitled to report what they did, even if the reviewer(s) are not completely enthusiastic about the approach. Additionally, we see participants as uniquely qualified to review one another’s papers, as they are familiar with the specific dataset(s) and may be aware of nuances and complexities that other participants should be made aware of.
Given this, we highly encourage participants to accept requests to review papers by other participants, to the extent reasonably possible.
Acknowledging and citing SAMPL
If you’ve benefitted from our work on the SAMPL series of challenges, please be sure to acknowledge our SAMPL NIH grant in any publications/presentations. This funded host-guest experiments, as well as our work organizing and administrating these challenges. You may acknowledge SAMPL by saying something like, “We appreciate the National Institutes of Health for its support of the SAMPL project via R01GM124270 to David L. Mobley (UC Irvine).”
We also ask you to cite the SAMPL dataset(s) you used. These are versioned on Zenodo, and a link will be available from the SAMPL repository for the challenge in which you participated.
Of course, we also appreciate it if you cite any overview/experimental papers relevant to the particular SAMPL challenge you participated in.