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The introduction of machine-learning (ML) algorithms to quantum mechanics enables rapid evaluation of otherwise intractable expressions at the cost of prior training on appropriate benchmarks. Many computational bottlenecks in the evaluation of accurate electronic structure theory could potentially benefit from the application of such models, from reducing the complexity of the underlying wave function parameter space to circumventing the complications of solving the electronic Schrödinger equation entirely. Applications of ML to electronic structure have thus far been focused on learning molecular properties (mainly the energy) from geometric representations. While this line of study has been quite successful, highly accurate models typically require a “big data” approach with thousands of train- ing data points. Herein, we propose a general, systematically improvable scheme for wave function-based ML of arbitrary molecular properties, inspired by the underlying equations that govern the canonical approach to computing the properties. To this end, we combine the established ML machinery of the t-amplitude tensor representation with a new reduced density matrix representation. The resulting model provides quantitative accuracy in both the electronic energy and dipoles of small molecules using only a few dozen training points per system.

The challenge of assigning the absolute stereochemical configuration to a chiral compound can be overcome via accurate ab initio predictions of optical rotation, a sensitive molecular property that is further complicated by solvent effects. The solvent's "chiral imprint"-the transfer of the chirality from the solute to the surrounding achiral solvent-is explored here using conformational averaging and time-dependent density-functional theory. These complex solvent effects are taken into account via simple averaging over a molecular dynamics trajectory together with the explicit quantum mechanical consideration of the solvent molecules within the solute's cybotactic region and implicit modeling of the bulk solvent. We consider several axes along which the system's optical rotation varies, including the sampling of the dynamical trajectory, the quality of the one-electron basis set, and the use of continuum solvent models to account for bulk effects.

A convenient route was envisaged toward the synthesis of dihydrochelerythrine (DHCHL), 4 by intramolecular Suzuki coupling of 2-bromo-N-(2-bromobenzyl)-naphthalen-1-amine derivative 5 via in situ generated arylborane. This compound was converted to (+/-)-6-acetonyldihydrochelerythrine (ADC), 3 which was then resolved by chiral prep-HPLC. Efficiency of DHCHL for the stabilization of promoter quadruplex DNA structures and a comparison study with the parent natural alkaloid chelerythrine (CHL), 1 was performed. A thorough investigation was carried out to assess the quadruplex binding affinity by using various biophysical and biochemical studies and the binding mode was explained by using molecular modeling and dynamics studies. Results clearly indicate that DHCHL is a strong G-quadruplex stabilizer with affinity similar to that of the parent alkaloid CHL. Compounds ADC and DHCHL were also screened against different human cancer cell lines. Among the cancer cells, (+/-)-ADC and its enantiomers showed varied (15-48%) inhibition against human colorectal cell line HCT116 and breast cancer cell line MDA-MB-231 albeit low enantio-specificity in the inhibitory effect; whereas DHCHL showed 30% inhibition against A431 cell line only, suggesting the compounds are indeed cancer tissue specific. (C) 2016 Elsevier Ltd. All rights reserved.