Article
Chemistry, Physical
Lim Heo, Collin F. Arbour, Giacomo Janson, Michael Feig
Summary: Protein structures can be determined experimentally or computationally. Computational methods utilize databases for structure prediction, but predicting structures distant from known ones may result in lower accuracy. Physics-based refinement methods can improve model accuracy.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2021)
Article
Biochemistry & Molecular Biology
Christopher Faulkner, Nora H. de Leeuw
Summary: Fentanyl, a potent opioid analgesic, is used in combination with propofol in general anesthesia. Molecular dynamics simulations revealed that fentanyl acts as a stabilizer, helping propofol to remain in binding sites. This study provides insights into the interactions between different drugs in the anesthesia process.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2022)
Article
Chemistry, Multidisciplinary
Yu-Peng Huang, Yijie Xia, Lijiang Yang, Jiachen Wei, Yi Isaac Yang, Yi Qin Gao
Summary: SPONGE is a software package for molecular dynamics simulation that utilizes various potential energy functions and the latest CUDA-enabled GPUs. The research group focuses on developing methods and theories to understand molecular mechanisms, combining enhanced sampling methods with machine learning techniques. The researcher got into theoretical chemistry as a PhD student and emphasizes curiosity, passion, and persistence as important qualities for scientific research.
CHINESE JOURNAL OF CHEMISTRY
(2022)
Article
Chemistry, Physical
David Montes de Oca Zapiain, Mitchell A. Wood, Nicholas Lubbers, Carlos Z. Pereyra, Aidan P. Thompson, Danny Perez
Summary: Advances in machine learning have made it possible to develop interatomic potentials with both the accuracy of first principles methods and the efficiency of empirical potentials. However, achieving transferability remains a challenge for machine learning-based potentials. This study focuses on developing systematic and scalable approaches to generate diverse training sets, and evaluates the performance of different potentials trained on these datasets.
NPJ COMPUTATIONAL MATERIALS
(2022)
Review
Chemistry, Physical
Wilfred F. van Gunsteren, Xavier Daura, Patrick F. J. Fuchs, Niels Hansen, Bruno A. C. Horta, Philippe H. Huenenberger, Alan E. Mark, Maria Pechlaner, Sereina Riniker, Chris Oostenbrink
Summary: Computer simulations of molecular systems play a crucial role in chemistry, biology, and physics, but it is important to consider uncertainty and errors affecting the calculated properties; advantages and shortcomings of commonly used assumptions and approximations in simulating bio-molecular systems should be taken into account; developers can improve simulation quality by discussing ways to facilitate and expand research involving bio-molecular simulations.
Article
Chemistry, Physical
Francesco Benfenati, Guglielmo Mazzola, Chiara Capecci, Panagiotis Kl Barkoutsos, Pauline J. Ollitrault, Ivano Tavernelli, Leonardo Guidoni
Summary: nu-VQE is a modified quantum computing algorithm for electronic structure optimization, using a nonunitary operator to simplify the wave function ansatz and achieve better results on noisy quantum computers. The method shows a significant improvement in accuracy compared to traditional VQE methods, with an order of magnitude reduction in absolute energy error.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2021)
Article
Chemistry, Medicinal
Rajendra P. Joshi, Katherine J. Schultz, Jesse William Wilson, Agustin Kruel, Rohith Anand Varikoti, Chathuri J. Kombala, Daniel W. Kneller, Stephanie Galanie, Gwyndalyn Phillips, Qiu Zhang, Leighton Coates, Jyothi Parvathareddy, Surekha Surendranathan, Ying Kong, Austin Clyde, Arvind Ramanathan, Colleen B. Jonsson, Kristoffer R. Brandvold, Mowei Zhou, Martha S. Head, Andrey Kovalevsky, Neeraj Kumar
Summary: Researchers have developed a closed-loop artificial intelligence pipeline to design covalent candidates, and identified several potential inhibitors through experimental validation. The utility and effectiveness of the developed method have been demonstrated.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Physics, Fluids & Plasmas
Andrey Pereverzev
Summary: This paper derives expressions for classical isothermal and adiabatic elastic constants with explicit boundary contributions and compares the results obtained using two different forms of the Born term. The study shows that the new form of the Born term involving only first derivatives of atomic-group or total potential energies converges to the same value as the original Born term but at a slower rate.
Article
Chemistry, Physical
Giulio Imbalzano, Yongbin Zhuang, Venkat Kapil, Kevin Rossi, Edgar A. Engel, Federico Grasselli, Michele Ceriotti
Summary: Machine-learning models are an effective strategy to bypass time-consuming electronic-structure calculations and enable accurate simulations of larger scale and complexity. Uncertainty quantification plays a crucial role in improving the accuracy and resilience of simulations, and can be applied to different types of structural and thermodynamic properties across diverse systems.
JOURNAL OF CHEMICAL PHYSICS
(2021)
Article
Computer Science, Interdisciplinary Applications
V. R. Coluci, S. O. Dantas, V. K. Tewary
Summary: A Green's function formalism is applied to solve equations of motion in classical molecular dynamics simulations, and a method is proposed to accelerate CGFMD simulations, providing a considerable gain in computational cost while maintaining accuracy and energy conservation. The method is tested on one-dimensional lattice of oscillators and shows rapid convergence with increasing problem size, with computational time scaling linearly with atom number. An OpenMP parallel version is implemented, exhibiting a speedup of 14x for N = 50000. CGFMD can be an alternative integration technique for molecular dynamics simulations.
COMPUTER PHYSICS COMMUNICATIONS
(2022)
Article
Physics, Multidisciplinary
Shichen Cao, Jingjing Li, Kenric P. Nelson, Mark A. Kon
Summary: This paper presents a coupled variational autoencoder method to improve the accuracy and robustness of handwritten numeral image models. By enhancing the likelihood of reconstructed images and reducing divergence between the posterior and prior latent distribution, the method successfully weights outlier samples with a higher penalty. Evaluation on the MNIST dataset and its corrupted modification C-MNIST demonstrates that the coupled VAE algorithm significantly enhances reconstruction, especially when seeded with corrupted images. Moreover, the algorithm reduces the divergence between the posterior and prior distribution of the latent variables.
Article
Multidisciplinary Sciences
Alex Albaugh, Geyao Gu, Todd R. Gingrich
Summary: Simulations can unravel the complex relationship between molecular structure and function. In this study, we demonstrate how slight changes in a molecular motor's structure can reverse its typical dynamic behavior using molecular simulations. These findings highlight the potential of molecular simulation in guiding the development of artificial molecular motors.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2023)
Article
Chemistry, Analytical
Erik Molino-Minero-Re, Antonio A. Aguileta, Ramon F. Brena, Enrique Garcia-Ceja
Summary: This research focuses on multi-sensor fusion methods to enhance the reliability of decision-making processes. The proposed approach for predicting the best fusion architecture has shown promising results across different domains.
Article
Automation & Control Systems
Yazdan Batmani, Shahabeddin Najafi
Summary: In this article, an event-triggered technique is proposed for continuous-time linear systems, which decomposes the closed-loop dynamic into fast and slow subsystems and designs individualized event-triggered mechanisms to achieve smoother closed-loop responses and reduce the number of transmitted packages from the system to the controller.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Rakesh Kumar Mishra, Lakshmi Maganti
Summary: The stability of the DNA double helix is significantly increased in the presence of covalently bonded drugs, which can potentially help in designing pharmaceutical drugs to target cancer cells. The study shows that disruption of hydrogen bonds and variation of stacking overlap parameters provide evidence of symmetry during rupture and asymmetry in the unzip event, indicating a promising pathway to open the double helix at a specific position.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2022)