RESUMO
The primary focus of GAMESS over the last 5 years has been the development of new high-performance codes that are able to take effective and efficient advantage of the most advanced computer architectures, both CPU and accelerators. These efforts include employing density fitting and fragmentation methods to reduce the high scaling of well-correlated (e.g., coupled-cluster) methods as well as developing novel codes that can take optimal advantage of graphical processing units and other modern accelerators. Because accurate wave functions can be very complex, an important new functionality in GAMESS is the quasi-atomic orbital analysis, an unbiased approach to the understanding of covalent bonds embedded in the wave function. Best practices for the maintenance and distribution of GAMESS are also discussed.
RESUMO
Bioisosteres are molecules that differ in substituents but still have very similar shapes. Bioisosteric replacements are ubiquitous in modern drug design, where they are used to alter metabolism, change bioavailability, or modify activity of the lead compound. Prediction of relative affinities of bioisosteres with computational methods is a long-standing task; however, the very shape closeness makes bioisosteric substitutions almost intractable for computational methods, which use standard force fields. Here, we design a quantum mechanical (QM)-cluster approach based on the GFN2-xTB semi-empirical quantum-chemical method and apply it to a set of H â F bioisosteric replacements. The proposed methodology enables advanced prediction of biological activity change upon bioisosteric substitution of -H with -F, with the standard deviation of 0.60 kcal/mol, surpassing the ChemPLP scoring function (0.83 kcal/mol), and making QM-based ΔΔG estimation comparable to â¼0.42 kcal/mol standard deviation of in vitro experiment. The speed of the method and lack of tunable parameters makes it affordable in current drug research.
Assuntos
Desenho de Fármacos , Teoria QuânticaRESUMO
Kirkpatrick et al. (Reports, 9 December 2021, p. 1385) trained a neural network-based DFT functional, DM21, on fractional-charge (FC) and fractional-spin (FS) systems, and they claim that it has outstanding accuracy for chemical systems exhibiting strong correlation. Here, we show that the ability of DM21 to generalize the behavior of such systems does not follow from the published results and requires revisiting.
RESUMO
The acid-base neutralization reaction of commercially available disodium 2,6-naphthalenedisulfonate (NDS, 2 equivalents) and the tetrahydrochloride salt of tetrakis(4-aminophenyl)methane (TAPM, 1 equivalent) in water gave a novel three-dimensional charge-assisted hydrogen-bonded framework (CAHOF, F-1). The framework F-1 was characterized by X-ray diffraction, TGA, elemental analysis, and 1H NMR spectroscopy. The framework was supported by hydrogen bonds between the sulfonate anions and the ammonium cations of NDS and protonated TAPM moieties, respectively. The CAHOF material functioned as a new type of catalytically active Brønsted acid in a series of reactions, including the ring opening of epoxides by water and alcohols. A Diels-Alder reaction between cyclopentadiene and methyl vinyl ketone was also catalyzed by F-1 in heptane. Depending on the polarity of the solvent mixture, the CAHOF F-1 could function as a purely heterogeneous catalyst or partly dissociate, providing some dissolved F-1 as the real catalyst. In all cases, the catalyst could easily be recovered and recycled.