SAMPL6 | Challenge inputs , details , and results for the SAMPL6 series | GPU library

SAMPL6 is a Python library typically used in Hardware, GPU, Deep Learning, Pytorch applications. SAMPL6 has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However SAMPL6 build file is not available. You can download it from GitHub.

(This is reproduced from the SAMPL6 Website). SAMPL6 includes challenges based on aqueous host-guest binding data (binding free energies and, optionally, binding enthalpies) for three different host molecules; and on physical properties (pKas, partition coefficients), for a set of fragment-like molecules. The host-guest systems are useful to test simulation methods, force fields, and solvent models, in the context of binding, without posing the setup issues and computational burden of protein simulations. The physical properties offer efficient tests of force field accuracy when detailed simulations are used, and can also test pKa prediction methods, continuum solvation models, and knowledge-based prediction methods. SAMPL6 will also introduce a new challenge component, the “SAMPLing challenge”, in which computational methods will be evaluated on how efficiently their calculations approach well-converged reference results generated by the organizers. Participants will be provided with machine readable setup files for the molecular systems, including force field setups, along with recommended cutoffs and treatments of long-ranged interactions. The SAMPLing challenge is expected to include one or more cases from each challenge component (host-guest binding on each system; log D calculation). Further information on both the host-guest and physical property components of SAMPL6 follow. Thanks to Drs. Bruce Gibb (Tulane U.) and Lyle Isaacs (U. Maryland) for providing the host-guest data, Andrea Rizzi for SAMPLing challenge data, and Dr. John Chodera, Mehtap Isik and collaborators from Merck (Dorothy Levorse, Timothy Rhodes, Ikenna Ndukwe) for the physicochemical property data sets (pKa and logP).