Results of the 2017 Roadmap survey of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenge community

Results of the 2017 Roadmap survey of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenge community David L. Mobley 1 , John D. Chodera 2 and Michael K. Gilson 3 of Pharmaceutical Sciences and Chemistry, University of California, Irvine 2 Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center 3 Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego 1 Departments Abstract: The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) series of blind prediction challenges provide unbiased, prospective tests of computational methods, serving as a crowdsourcing mechanism to drive innovation. These challenges solicit submissions from the computational community to predict properties of interest, such as solvation, partition, or binding of drug-like molecules. These challenges provided substantial benefit to the community, and have led to roughly 100 publications, many of which are broadly cited (see attached bibliography). We are currently seeking funding from the NIH and surveyed the community concerning experiences with SAMPL and how our future plans for SAMPL can best align with the community’s interests and needs. This document summarizes the results of this survey and describes our findings. On the whole, the community enthusiastically supports our plans for the future of SAMPL, and provided modest suggestions to further strengthen our plans. For up-to-date info please see the SAMPL website. Survey methods and results Here, we reproduce the results of the survey in full except for anonymizing respondents. Questions 1 and 2 dealt with identifying information (name and e-mail) and are thus bypassed here. There were 44 respondents, though not all respondents answered all questions. The survey was conducted via Google Forms from April 18 to June 19. The survey was advertised via Twitter and the Mobley Lab website, and e-mailed to to past participants.