Zobrazeno 1 - 10
of 1 050
pro vyhledávání: '"Pande, Vijay S."'
Autor:
Eastman, Peter, Galvelis, Raimondas, Peláez, Raúl P., Abreu, Charlles R. A., Farr, Stephen E., Gallicchio, Emilio, Gorenko, Anton, Henry, Michael M., Hu, Frank, Huang, Jing, Krämer, Andreas, Michel, Julien, Mitchell, Joshua A., Pande, Vijay S., Rodrigues, João PGLM, Rodriguez-Guerra, Jaime, Simmonett, Andrew C., Swails, Jason, Zhang, Ivy, Chodera, John D., De Fabritiis, Gianni, Markland, Thomas E.
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added
Externí odkaz:
http://arxiv.org/abs/2310.03121
Simulations of biomolecules have enormous potential to inform our understanding of biology but require extremely demanding calculations. For over twenty years, the Folding@home distributed computing project has pioneered a massively parallel approach
Externí odkaz:
http://arxiv.org/abs/2303.08993
The classical simulation of quantum systems typically requires exponential resources. Recently, the introduction of a machine learning-based wavefunction ansatz has led to the ability to solve the quantum many-body problem in regimes that had previou
Externí odkaz:
http://arxiv.org/abs/1910.10675
Autor:
Sinitskiy, Anton V., Pande, Vijay S.
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 a physical approach predicting energies and electron densities of mol
Externí odkaz:
http://arxiv.org/abs/1908.00971
Autor:
Eastman, Peter, Pande, Vijay S.
We train a neural network to predict human gene expression levels based on experimental data for rat cells. The network is trained with paired human/rat samples from the Open TG-GATES database, where paired samples were treated with the same compound
Externí odkaz:
http://arxiv.org/abs/1907.03041
The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) properties of drug candidates are estimated to account for up to 50% of all clinical trial failures. Predicting ADMET properties has therefore been of great interest to the c
Externí odkaz:
http://arxiv.org/abs/1903.11789
Autor:
Eastman, Peter, Pande, Vijay S.
We train a neural network to predict chemical toxicity based on gene expression data. The input to the network is a full expression profile collected either in vitro from cultured cells or in vivo from live animals. The output is a set of fine graine
Externí odkaz:
http://arxiv.org/abs/1902.00060
Autor:
Sinitskiy, Anton V., Pande, Vijay S.
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. The electron density plays a key role in DFT. In this work, we explore
Externí odkaz:
http://arxiv.org/abs/1809.02723
Many important analgesics relieve pain by binding to the $\mu$-Opioid Receptor ($\mu$OR), which makes the $\mu$OR among the most clinically relevant proteins of the G Protein Coupled Receptor (GPCR) family. Despite previous studies on the activation
Externí odkaz:
http://arxiv.org/abs/1804.08206
Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems. In this work, we present a data-driven approach for
Externí odkaz:
http://arxiv.org/abs/1803.08993