SAMPL (Statistical Assessment of the Modeling of Proteins and Ligands) is a set of community-wide blind challenges aimed to advance computational techniques as standard predictive tools in rational drug design. A broad range of biologically relevant systems with different sizes and levels of complexities including proteins, host-guest complexes, and drug-like small molecules have been selected to test the latest modeling methods and force fields in SAMPL. New experimental data, such as binding affinity and hydration free energy, are withheld from participants until the prediction submission deadline, so that the true predictive power of methods can be revealed. The most recent SAMPL5 challenge contains two prediction categories: the binding affinity of host-guest systems, and the distribution coefficients of drug-like molecules between water and cyclohexane. Since 2008, the SAMPL challenge series has attracted widespread interest from scientists engaged in the field of computer-aided drug design (CADD) around the world, and has resulted in around 100 publications with many of them highly cited. The current SAMPL organizers include Prof. John Chodera at Memorial Sloan Kettering Cancer Center, Prof. Michael K. Gilson at University of California, San Diego, Prof. David Mobley at University of California, Irvine, and Prof. Michael Shirts, at University of Colorado, Boulder.
The SAMPL challenge seeks to accelerate progress in developing quantitative, accurate drug discovery tools by providing prospective validation and rigorous comparisons for computational methodologies and force fields. Computer-aided drug design methods have been considerably improved over time, along with the rapid growth of high-performance computing capabilities. However, their applicability in the pharmaceutical industry are still highly limited, due to the insufficient accuracy. Lacking large-scale prospective validations, methods tend to suffer from over-fitting the pre-existing experimental data. To overcome this, SAMPL challenges have been organized as blind tests: each time new datasets are carefully designed and collected from academic or industrial research laboratories, and measurements are released shortly after the deadline of prediction submission. Researchers then can compare those high-quality, prospective experimental data with the submitted estimates.
SAMPL has historically focused on the properties of host-guest systems and drug-like small molecules. These simply model systems require considerably less computational resources to simulate, compared to the protein systems, and thus enable much faster convergence. Meanwhile, through careful design, these model systems can be used to focus on one particular or a subset of simulation challenges. The past several SAMPL host-guest, hydration free energy and log D challenges revealed the limitations in generalized force fields, facilitated the development of solvent models, and highlighted the importance of properly handling protonation states and salt effects.
Registration and participation is free for SAMPL challenges. The most recent SAMPL challenge, SAMPL6, required online registration on the Drug Design Data Resource (D3R) website. Instructions, input files and results were then provided through the same website. Participants were allowed to submit multiple predictions through the D3R website, either anonymously or with research affiliation. Since the SAMPL2 challenge, all participants have been invited to attend the SAMPL workshops and submit manuscripts to describe their results. After a peer-review process, the resulting papers, along with the overview papers which summarize all submitting data, were published in the special issues of the Journal of Computer-Aided Molecular Design.
While SAMPL serves a clear community need, its future is uncertain in that it remains an unfunded initiative. Currently, funding is being sought from the NIH to allow the design of future SAMPL challenges to drive advances in the areas they are most needed for modeling efforts. If grant funding is not forthcoming, perhaps it will become possible for an industrial partnership to help sustain and extend SAMPL. While it may be able to continue in its current form slightly longer, it has become increasingly clear that some resources are needed in order to ensure its continued existence and success.
Earlier SAMPL challenges
The first SAMPL exercise, SAMPL0 (2008) focused on the predictions of solvation free energies of 17 small molecules. A research group at Stanford University and scientists at OpenEye Scientific Software carried out the calculations. Despite the informal format, SAMPL0 laid the groundwork for the following SAMPL challenges.
SAMPL1 (2009) and SAMPL2 challenges (2010) were organized by OpenEye and continued to focus on predicting solvation free energies of drug-like small molecules. Attempts were also made to predict binding affinities, binding poses and tautomer ratios. Both challenges attracted significant participations from computational scientists and researchers in academia and industry.
SAMPL3 and SAMPL4
The blinded data sets for host-guest binding affinities were introduced for the first time in SAMPL3 (2011-2012), along with solvation free energies for small molecules and the binding affinity data for 500 fragment-like tyrosine inhibitors. Three host molecules were all from the cucurbituril family. The SAMPL3 challenge received 103 submissions from 23 research groups worldwide.
Different from the prior three SAMPL events, the SAMPL4 exercise (2013-2014) was coordinated by academic researchers, with logistical support from OpenEye. Datasets in SAMPL4 consisted of binding affinities for host-guest systems and HIV integrase inhibitors, as well as hydration free energies of small molecules. Host molecules included cucurbituril (CB7) and octa-acid. The SAMPL4 hydration challenge involved 49 submissions from 19 groups. The participation of the host–guest challenge also grew significantly compared to SAMPL3. The workshop was held at Stanford University in September, 2013.
The protein-ligand challenges were separated from SAMPL in SAMPL5 (2015-2016) and were distributed as the new Grand Challenges of the Drug Design Data Resource (D3R). SAMPL5 allowed participants to make predictions of the binding affinities of three sets of host-guest systems: an acyclic CB7 derivative and two host from the octa-acid family. Participants were also encouraged to submit predictions for binding enthalpies. A wide array of computational methods were tested, including density functional theory (DFT), molecular dynamics, docking, and metadynamics. The distribution coefficient predictions were introduced for the first time, receiving total of 76 submissions from 18 researcher groups or scientists for a set of 53 small molecules. The workshop was held in March, 2016 at University of California, San Diego as part of the D3R workshop. The top-performing methods in the host-guest challenge yielded encouraging yet imperfect correlations with experimental data, accompanied by large, systematic shifts relative to experiment.
The SAMPL6 testing systems include cucurbituril, octa-acid, tetra-endo-methyl octa-acid, and a series of fragment-like small molecules. The host-guest, conformational sampling and pKa prediction challenges of SAMPL6 are now closed. The SAMPL6 workshop was jointly run with the D3R workshop on Feb. 22 and 23, 2018, in Scripps Institution of Oceanography, La Jolla, CA (https://drugdesigndata.org//about/d3r-2018-workshop).
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