36 citations · 75 across the 6 of their papers we have counts for
6 papers
Physical machine learning outperforms "human learning" in Quantum Chemistry
Anton V. Sinitskiy, Vijay S. Pande
Two types of approaches to modeling molecular systems have demonstrated high practical efficiency. Density functional theory (DFT), the most widely used quantum chemical method, is…
Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)
Anton V. Sinitskiy, Vijay S. Pande
Density functional theory (DFT) is one of the main methods in Quantum Chemistry that offers an attractive trade off between the cost and accuracy of quantum chemical computations.…
Quantum Mechanics / Coarse-Grained Molecular Mechanics (QM/CG-MM)
Anton V. Sinitskiy, Gregory A. Voth
Numerous molecular systems, including solutions, proteins, and composite materials, can be modeled using mixed-resolution representations, of which the quantum mechanics/molecular…
Theoretical restrictions on longest implicit timescales in Markov state models of biomolecular dynamics
Anton V. Sinitskiy, Vijay S. Pande
Markov state models (MSMs) have been widely used to analyze computer simulations of various biomolecular systems. They can capture conformational transitions much slower than an av…
Computationally Discovered Potentiating Role of Glycans on NMDA Receptors
Anton V. Sinitskiy, Nathaniel H. Stanley, David H. Hackos +3
N-methyl-D-aspartate receptors (NMDARs) are glycoproteins in the brain central to learning and memory. The effects of glycosylation on the structure and dynamics of NMDARs are larg…
Modeling molecular crystals formed by spin-active metal complexes by atom-atom potentials
Anton V. Sinitskiy, Andrei L. Tchougréeff, Andrei M. Tokmachev +1
We apply the atom-atom potentials to molecular crystals of iron (II) complexes with bulky organic ligands. The crystals under study are formed by low-spin or high-spin molecules of…