PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations.

The presented "PubChemQC B3LYP/6-31G*//PM6" data set is composed of the electronic properties of 85,938,443 molecules, encompassing a broad spectrum of molecules from essential compounds to biomolecules with a molecular weight up to 1000. These molecules account for 94.0% of the original PubChem Compound catalog as of August 29, 2016. The electronic properties, including orbitals, orbital energies, total energies, dipole moments, and other pertinent properties, were computed by using the B3LYP/6-31G* and PM6 methods. The data set, available in three formats, namely, GAMESS quantum chemistry program files, selected JSON output files, and a PostgreSQL database, provides researchers with the ability to query molecular properties. It is further subdivided into five subdata sets for more specific data. The first two subsets encompass molecules with carbon, hydrogen, oxygen, and nitrogen with molecular weights under 300 and 500, respectively. The third and fourth subsets incorporate molecules with carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur, fluorine, and chlorine, with molecular weights under 300 and 500, respectively. The fifth subset comprises molecules with carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur, fluorine, chlorine, sodium, potassium, magnesium, and calcium, with a molecular weight of under 500. The coefficients of determination for the highest occupied molecular orbital-lowest unoccupied molecular orbital energy gap range from 0.892 (for CHON500) to 0.803 (for the whole data set). These comprehensive results pave the way for applications in drug discovery and materials science, among others. The data sets can be accessed under the Creative Commons Attribution 4.0 International license at the following web address: https://nakatamaho.riken.jp/pubchemqc.riken.jp/b3lyp_pm6_datasets.html.

PubChemQC PM6: Data Sets of 221 Million Molecules with Optimized Molecular Geometries and Electronic Properties.

We report on optimized molecular geometries and electronic properties calculated by the PM6 method for 94.0% of the 91.6 million molecules cataloged in PubChem Compounds retrieved on August 29, 2016. In addition to neutral states, we also calculated those for cationic, anionic, and spin flipped electronic states of 56.2%, 49.7%, and 41.3% of the molecules, respectively. Thus, the grand total of the PM6 calculations amounted to 221 million. We compared the resulting molecular geometries with B3LYP/6-31G* optimized geometries for 2.6 million molecules. The root-mean-square deviations in bond length and bond angle were approximately 0.016 Å and 1.7°, respectively. Then, using linear regression to examine the HOMO energy levels E(HOMO) in the B3LYP and PM6 calculations, we found that EB3LYP(HOMO) = 0.876EPM6(HOMO) + 1.975 (eV) and calculated the coefficient of determination to be 0.803. Likewise, we examined the LUMO energy levels and found EB3LYP(LUMO) = 1.069EPM6(LUMO) - 0.420 (eV); the coefficient of determination was 0.842. We also generated four subdata sets, each of which was composed of molecules with molecular weights less than 500. Subdata set i contained C, H, O and N, ii contained C, H, N, O, P, and S, iii contained C, H, N, O, P, S, F, and Cl, and iv contained C, H, N, O, P, S, F, Cl, Na, K, Mg, and Ca. The data sets are available at http://pubchemqc.riken.jp/pm6_datasets.html under a Creative Commons Attribution 4.0 International license.

4',6-Dimethoxyisoflavone-7-O-ß-D-glucopyranoside (wistin) is a peroxisome proliferator-activated receptor α (PPARα) agonist in mouse hepatocytes.

Peroxisome proliferator-activated receptors (PPARs) are ligand-dependent transcription factors that regulate lipid and glucose metabolism. PPARα mainly affects fatty acid metabolism, and its activation lowers lipid levels. PPARγ is involved in the regulation of adipogenesis, insulin sensitivity, energy balance, and lipid biosynthesis. We have previously reported that 4',6-dimethoxyisoflavone-7-O-ß-D-glucopyranoside (wistin) can activate PPARγ. The purpose of the present study is to investigate the PPARα agonist activity of wistin. Using a luciferase reporter assay system of PPARα in monkey COS7 kidney cells, we showed that wistin could activate PPARα (P < 0.01 at 10 µg/mL) in a dose-dependent manner. Moreover, the addition of wistin upregulated the expression of PPARα (P < 0.01 at 10 µg/mL) and PPARα target genes including carnitine palmitoyltransferase 1a (P < 0.05 at 10 µg/mL), acyl-CoA oxidase (P < 0.01 at 10 µg/mL), acyl-CoA synthase (P < 0.05 at 10 µg/mL), PPARγ coactivator 1α (P < 0.05 at 10 µg/mL), uncoupling protein 2 (P < 0.05 at 1 µg/mL), and uncoupling protein 3 (P < 0.05 at 10 µg/mL), which are genes involved in lipid efflux and energy expenditure, in mouse primary hepatocytes. Furthermore, wistin inhibited cellular triglyceride accumulation in hepatocytes (P < 0.05 at 10 µg/mL) in a dose-dependent manner. These results indicate that wistin could suppress lipid accumulation through PPARα activation. The action of wistin on PPARα could be of interest for the amelioration of lipid metabolic disorders. To the best of our knowledge, wistin is the first reported isoflavonoid O-glycoside with PPARα agonist activity.

PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry.

Large-scale molecular databases play an essential role in the investigation of various subjects such as the development of organic materials, in silico drug design, and data-driven studies with machine learning. We have developed a large-scale quantum chemistry database based on first-principles methods. Our database currently contains the ground-state electronic structures of 3 million molecules based on density functional theory (DFT) at the B3LYP/6-31G* level, and we successively calculated 10 low-lying excited states of over 2 million molecules via time-dependent DFT with the B3LYP functional and the 6-31+G* basis set. To select the molecules calculated in our project, we referred to the PubChem Project, which was used as the source of the molecular structures in short strings using the InChI and SMILES representations. Accordingly, we have named our quantum chemistry database project "PubChemQC" ( http://pubchemqc.riken.jp/ ) and placed it in the public domain. In this paper, we show the fundamental features of the PubChemQC database and discuss the techniques used to construct the data set for large-scale quantum chemistry calculations. We also present a machine learning approach to predict the electronic structure of molecules as an example to demonstrate the suitability of the large-scale quantum chemistry database.

Relativistic quantum Monte Carlo method using zeroth-order regular approximation Hamiltonian.

We propose a new relativistic treatment in the quantum Monte Carlo (QMC) technique using the zeroth-order regular approximation (ZORA) Hamiltonian. The novel ZORA local energy is derived, and its availability is examined with some variational Monte Carlo calculations. We optimize the wave functions variationally and evaluate the relativistic and correlation effects simultaneously. It is shown that our ZORA-QMC method with Jastrow-Slater wave functions can recover not only relativistic effects but also almost the same amount of electron correlations as the nonrelativistic QMC method can by evaluating the ionization potentials of the first row atoms, Li-Ne.

Variational calculation of second-order reduced density matrices by strong N-representability conditions and an accurate semidefinite programming solver.

The reduced density matrix (RDM) method, which is a variational calculation based on the second-order reduced density matrix, is applied to the ground state energies and the dipole moments for 57 different states of atoms, molecules, and to the ground state energies and the elements of 2-RDM for the Hubbard model. We explore the well-known N-representability conditions (P, Q, and G) together with the more recent and much stronger T1 and T2(') conditions. T2(') condition was recently rederived and it implies T2 condition. Using these N-representability conditions, we can usually calculate correlation energies in percentage ranging from 100% to 101%, whose accuracy is similar to CCSD(T) and even better for high spin states or anion systems where CCSD(T) fails. Highly accurate calculations are carried out by handling equality constraints and/or developing multiple precision arithmetic in the semidefinite programming (SDP) solver. Results show that handling equality constraints correctly improves the accuracy from 0.1 to 0.6 mhartree. Additionally, improvements by replacing T2 condition with T2(') condition are typically of 0.1-0.5 mhartree. The newly developed multiple precision arithmetic version of SDP solver calculates extraordinary accurate energies for the one dimensional Hubbard model and Be atom. It gives at least 16 significant digits for energies, where double precision calculations gives only two to eight digits. It also provides physically meaningful results for the Hubbard model in the high correlation limit.

Simple Hamiltonians which exhibit drastic failures by variational determination of the two-particle reduced density matrix with some well known N-representability conditions.

Calculations on small molecular systems indicate that the variational approach employing the two-particle reduced density matrix (2-RDM) as the basic unknown and applying the P, Q, G, T1, and T2 representability conditions provides an accuracy that is competitive with the best standard ab initio methods of quantum chemistry. However, in this paper we consider a simple class of Hamiltonians for which an exact ground state wave function can be written as a single Slater determinant and yet the same 2-RDM approach gives a drastically nonrepresentable result. This shows the need for stronger representability conditions than the mentioned ones.