A volunteer computing approach is presented for the purpose of screening a large number of molecular structures with respect to their suitability as new battery electrolyte solvents. Collective properties like melting, boiling and flash points are evaluated using COSMOtherm and quantitative structure-property relationship (QSPR) based methods, while electronic structure theory methods are used for the computation of electrochemical stability window estimators. Two application examples are presented: first, the results of a previous large-scale screening test (PCCP, 2014, 16, 7919) are re-evaluated with respect to the mentioned collective properties. As a second application example, all reasonable nitrile solvents up to 12 heavy atoms are generated and used to illustrate a suitable filter protocol for picking Pareto-optimal candidates.
Correctly ranking protein-ligand interactions with respect to overall free energy of binding is a grand challenge for virtual drug design. Here we compare the performance of various quantum chemical approaches for tackling this so-called "scoring" problem. Relying on systematically generated benchmark sets of large protein/ligand model complexes based on the PDBbind database, we show that the performance depends first of all on the general level of theory. Comparing classical molecular mechanics (MM), semiempirical quantum mechanical (SQM), and density functional theory (DFT) based methods, we find that enhanced SQM approaches perform very similar to DFT methods and substantially different from MM potentials.
Recent successes and failures of the application of ‘enhanced’ semiempirical QM (SQM) methods are reviewed in the light of the benefits and backdraws of adding dispersion (D) and hydrogen-bond (H) correction terms. We find that the accuracy of SQM-DH methods for non-covalent interactions is very often reported to be comparable to dispersion-corrected density functional theory (DFT-D), while computation times are about three orders of magnitude lower. SQM-DH methods thus open up a possibility to simulate realistically large model systems for problems both in life and materials science with comparably high accuracy.
We review the first successes and failures of a “new wave” of quantum chemistry-based approaches to the treatment of protein/ligand interactions. These approaches share the use of “enhanced”, dispersion (D), and/or hydrogen-bond (H) corrected density functional theory (DFT) or semi-empirical quantum mechanical (SQM) methods, in combination with ensemble weighting techniques of some form to capture entropic effects. Benchmark and model system calculations in comparison to high-level theoretical as well as experimental references have shown that both DFT-D (dispersion-corrected density functional theory) and SQM-DH (dispersion and hydrogen bond-corrected semi-empirical quantum mechanical) perform much more accurately than older DFT and SQM approaches and also standard docking methods. In addition, DFT-D might soon become and SQM-DH already is fast enough to compute a large number of binding modes of comparably large protein/ligand complexes, thus allowing for a more accurate assessment of entropic effects.
The accurate prediction of the strength of protein-ligand interactions is a very di±cult problem despite impressive advances in the¯eld of biomolecular modeling. There are good reasons to believe that quantum mechanical methods can help with this task, but the application of such methods in the context of scoring is still in its infancy. Here we benchmark several wave function theory (WFT), density functional theory (DFT) and semiempirical quantum mechanical (SQM) approaches against high-level theoretical references for realistic test cases. Based on our ndings for systematically generated model systems of real protein/ligand complexes from the PDB-bind database, we can recommend SCS-MP2 and B2-PLYP-D3 as reference methods, TPSS-D3+D abc /def-TZVPP as the best DFT approach and PM6-DH+ as a fast and accurate alternative to full ab initio treatments.
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